Data Mining for Financial Applications

NASA Astrophysics Data System (ADS)

Kovalerchuk, Boris; Vityaev, Evgenii

This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology.

Fusion and Gaussian mixture based classifiers for SONAR data

NASA Astrophysics Data System (ADS)

Kotari, Vikas; Chang, KC

2011-06-01

Underwater mines are inexpensive and highly effective weapons. They are difficult to detect and classify. Hence detection and classification of underwater mines is essential for the safety of naval vessels. This necessitates a formulation of highly efficient classifiers and detection techniques. Current techniques primarily focus on signals from one source. Data fusion is known to increase the accuracy of detection and classification. In this paper, we formulated a fusion-based classifier and a Gaussian mixture model (GMM) based classifier for classification of underwater mines. The emphasis has been on sound navigation and ranging (SONAR) signals due to their extensive use in current naval operations. The classifiers have been tested on real SONAR data obtained from University of California Irvine (UCI) repository. The performance of both GMM based classifier and fusion based classifier clearly demonstrate their superior classification accuracy over conventional single source cases and validate our approach.

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques.

PubMed

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M; Anticoi, Hernán Francisco; Guash, Eduard

2018-03-07

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector-either surface or underground mining-based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.

A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Sainani, Varsha

The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

Application of the Deformation Information System for automated analysis and mapping of mining terrain deformations - case study from SW Poland

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Grzempowski, Piotr; Milczarek, Wojciech; Nowacka, Anna

2015-04-01

Monitoring, mapping and modelling of mining induced terrain deformations are important tasks for quantifying and minimising threats that arise from underground extraction of useful minerals and affect surface infrastructure, human safety, the environment and security of the mining operation itself. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and expanding with the progress in geographical information technologies. These include for example: terrestrial geodetic measurements, Global Navigation Satellite Systems, remote sensing, GIS based modelling and spatial statistics, finite element method modelling, geological modelling, empirical modelling using e.g. the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The presentation shows the results of numerical modelling and mapping of mining terrain deformations for two cases of underground mining sites in SW Poland, hard coal one (abandoned) and copper ore (active) using the functionalities of the Deformation Information System (DIS) (Blachowski et al, 2014 @ http://meetingorganizer.copernicus.org/EGU2014/EGU2014-7949.pdf). The functionalities of the spatial data modelling module of DIS have been presented and its applications in modelling, mapping and visualising mining terrain deformations based on processing of measurement data (geodetic and GNSS) for these two cases have been characterised and compared. These include, self-developed and implemented in DIS, automation procedures for calculating mining terrain subsidence with different interpolation techniques, calculation of other mining deformation parameters (i.e. tilt, horizontal displacement, horizontal strain and curvature), as well as mapping mining terrain categories based on classification of the values of these parameters as used in Poland. Acknowledgments. This work has been financed from the National Science Centre Project "Development of a numerical method of mining ground deformation modelling in complex geological and mining conditions" UMO-2012/07/B/ST10/04297 executed at the Faculty of Geoengineering, Mining and Geology of the Wroclaw University of Technology (Poland).

Privacy Preserving Technique for Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform

NASA Astrophysics Data System (ADS)

Kadampur, Mohammad Ali; D. v. L. N., Somayajulu

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity.

A demonstration of ERTS-1 analog and digital techniques applied to strip mining in Maryland and West Virginia

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schubert, J.

1974-01-01

The largest contour strip mining operations in western Maryland and West Virginia are located within the Georges Creek and the Upper Potomac Basins. These two coal basins lie within the Georges Creek (Wellersburg) syncline. The disturbed strip mine areas were delineated with the surrounding geological and vegetation features using ERTS-1 data in both analog (imagery) and digital form. The two digital systems used were: (1) the ERTS-Analysis system, a point-by-point digital analysis of spectral signatures based on known spectral values, and (2) the LARS Automatic Data Processing System. The digital techniques being developed will later be incorporated into a data base for land use planning. These two systems aided in efforts to determine the extent and state of strip mining in this region. Aircraft data, ground verification information, and geological field studies also aided in the application of ERTS-1 imagery to perform an integrated analysis that assessed the adverse effects of strip mining. The results indicated that ERTS can both monitor and map the extent of strip mining to determine immediately the acreage affected and indicate where future reclamation and revegetation may be necessary.

Knowledge-Based Reinforcement Learning for Data Mining

NASA Astrophysics Data System (ADS)

Kudenko, Daniel; Grzes, Marek

Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data collection behaviour in the respective domains. In future work, we intend to demonstrate and evaluate our techniques on concrete real-world data mining applications.

Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring.

PubMed

Asner, Gregory P; Llactayo, William; Tupayachi, Raul; Luna, Ernesto Ráez

2013-11-12

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests.

Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring

PubMed Central

Asner, Gregory P.; Llactayo, William; Tupayachi, Raul; Luna, Ernesto Ráez

2013-01-01

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests. PMID:24167281

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques

PubMed Central

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M.; Anticoi, Hernán Francisco; Guash, Eduard

2018-01-01

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector—either surface or underground mining—based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents. PMID:29518921

Using text-mining techniques in electronic patient records to identify ADRs from medicine use.

PubMed

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-05-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use

PubMed Central

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-01-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. PMID:22122057

Proceedings: Fourth Workshop on Mining Scientific Datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamath, C

Commercial applications of data mining in areas such as e-commerce, market-basket analysis, text-mining, and web-mining have taken on a central focus in the JCDD community. However, there is a significant amount of innovative data mining work taking place in the context of scientific and engineering applications that is not well represented in the mainstream KDD conferences. For example, scientific data mining techniques are being developed and applied to diverse fields such as remote sensing, physics, chemistry, biology, astronomy, structural mechanics, computational fluid dynamics etc. In these areas, data mining frequently complements and enhances existing analysis methods based on statistics, exploratorymore » data analysis, and domain-specific approaches. On the surface, it may appear that data from one scientific field, say genomics, is very different from another field, such as physics. However, despite their diversity, there is much that is common across the mining of scientific and engineering data. For example, techniques used to identify objects in images are very similar, regardless of whether the images came from a remote sensing application, a physics experiment, an astronomy observation, or a medical study. Further, with data mining being applied to new types of data, such as mesh data from scientific simulations, there is the opportunity to apply and extend data mining to new scientific domains. This one-day workshop brings together data miners analyzing science data and scientists from diverse fields to share their experiences, learn how techniques developed in one field can be applied in another, and better understand some of the newer techniques being developed in the KDD community. This is the fourth workshop on the topic of Mining Scientific Data sets; for information on earlier workshops, see http://www.ahpcrc.org/conferences/. This workshop continues the tradition of addressing challenging problems in a field where the diversity of applications is matched only by the opportunities that await a practitioner.« less

Western energy related overhead monitoring project. Phase 2: Summary. [Campbell County, Wyoming and coal strip mines in Montana and New Mexico

NASA Technical Reports Server (NTRS)

Anderson, J. E. (Principal Investigator)

1979-01-01

Assistance by NASA to EPA in the establishment and maintenance of a fully operational energy-related monitoring system included: (1) regional analysis applications based on LANDSAT and auxiliary data; (2) development of techniques for using aircraft MSS data to rapidly monitor site specific surface coal mine activities; and (3) registration of aircraft MSS data to a map base. The coal strip mines used in the site specific task were in Campbell County, Wyoming; Big Horn County, Montana; and the Navajo mine in San Juan County, New Mexico. The procedures and software used to accomplish these tasks are described.

Real -time dispatching modelling for trucks with different capacities in open pit mines / Modelowanie w czasie rzeczywistym przewozów ciężarówek o różnej ładowności w kopalni odkrywkowej

NASA Astrophysics Data System (ADS)

Ahangaran, Daryoush Kaveh; Yasrebi, Amir Bijan; Wetherelt, Andy; Foster, Patrick

2012-10-01

Application of fully automated systems for truck dispatching plays a major role in decreasing the transportation costs which often represent the majority of costs spent on open pit mining. Consequently, the application of a truck dispatching system has become fundamentally important in most of the world's open pit mines. Recent experiences indicate that by decreasing a truck's travelling time and the associated waiting time of its associated shovel then due to the application of a truck dispatching system the rate of production will be considerably improved. Computer-based truck dispatching systems using algorithms, advanced and accurate software are examples of these innovations. Developing an algorithm of a computer- based program appropriated to a specific mine's conditions is considered as one of the most important activities in connection with computer-based dispatching in open pit mines. In this paper the changing trend of programming and dispatching control algorithms and automation conditions will be discussed. Furthermore, since the transportation fleet of most mines use trucks with different capacities, innovative methods, operational optimisation techniques and the best possible methods for developing the required algorithm for real-time dispatching are selected by conducting research on mathematical-based planning methods. Finally, a real-time dispatching model compatible with the requirement of trucks with different capacities is developed by using two techniques of flow networks and integer programming.

Preventing spontaneous combustion after mine closing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewicki, G.

1987-11-01

The author explains how the Northern Coal Company and a Houston-based firefighting firm developed an innovative technique to reduce the risk of spontaneous combustion after mine closing in its Rienau number2 Mine. The ''Light Water TM'' ATC series of firefighting foam concentrates were designed for extinguishing flammable liquid fires. By slightly altering the chemicals, the concentrates could be used to seal the coal ribs, floor, and roof, reducing the risk of combustion. Subsequent monitoring of the mine has identified no signs of heating.

Contribution to understanding the post-mining landscape - Application of airborn LiDAR and historical maps at the example from Silesian Upland (Poland)

NASA Astrophysics Data System (ADS)

Gawior, D.; Rutkiewicz, P.; Malik, I.; Wistuba, M.

2017-11-01

LiDAR data provide new insights into the historical development of mining industry recorded in the topography and landscape. In the study on the lead ore mining in the 13th-17th century we identified remnants of mining activity in relief that are normally obscured by dense vegetation. The industry in Tarnowice Plateau was based on exploitation of galena from the bedrock. New technologies, including DEM from airborne LiDAR provide show that present landscape and relief of post-mining area under study developed during several, subsequent phases of exploitation when different techniques of exploitation were used and probably different types of ores were exploited. Study conducted on the Tarnowice Plateau proved that combining GIS visualization techniques with historical maps, among all geological maps, is a promising approach in reconstructing development of anthropogenic relief and landscape..

Underground Mining Method Selection Using WPM and PROMETHEE

NASA Astrophysics Data System (ADS)

Balusa, Bhanu Chander; Singam, Jayanthu

2018-04-01

The aim of this paper is to represent the solution to the problem of selecting suitable underground mining method for the mining industry. It is achieved by using two multi-attribute decision making techniques. These two techniques are weighted product method (WPM) and preference ranking organization method for enrichment evaluation (PROMETHEE). In this paper, analytic hierarchy process is used for weight's calculation of the attributes (i.e. parameters which are used in this paper). Mining method selection depends on physical parameters, mechanical parameters, economical parameters and technical parameters. WPM and PROMETHEE techniques have the ability to consider the relationship between the parameters and mining methods. The proposed techniques give higher accuracy and faster computation capability when compared with other decision making techniques. The proposed techniques are presented to determine the effective mining method for bauxite mine. The results of these techniques are compared with methods used in the earlier research works. The results show, conventional cut and fill method is the most suitable mining method.

Localisation of an Unknown Number of Land Mines Using a Network of Vapour Detectors

PubMed Central

Chhadé, Hiba Haj; Abdallah, Fahed; Mougharbel, Imad; Gning, Amadou; Julier, Simon; Mihaylova, Lyudmila

2014-01-01

We consider the problem of localising an unknown number of land mines using concentration information provided by a wireless sensor network. A number of vapour sensors/detectors, deployed in the region of interest, are able to detect the concentration of the explosive vapours, emanating from buried land mines. The collected data is communicated to a fusion centre. Using a model for the transport of the explosive chemicals in the air, we determine the unknown number of sources using a Principal Component Analysis (PCA)-based technique. We also formulate the inverse problem of determining the positions and emission rates of the land mines using concentration measurements provided by the wireless sensor network. We present a solution for this problem based on a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme, and we compare it to the least squares optimisation approach. Experiments conducted on simulated data show the effectiveness of the proposed approach. PMID:25384008

Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results

ERIC Educational Resources Information Center

Merceron, Agathe; Yacef, Kalina

2004-01-01

In this article we describe the initial investigations that we have conducted on student data collected from a web-based tutoring tool. We have used some data mining techniques such as association rule and symbolic data analysis, as well as traditional SQL queries to gain further insight on the students' learning and deduce information to improve…

Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

NASA Astrophysics Data System (ADS)

Baertlein, Brian A.; Liao, Wen-Jiao

2000-08-01

An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(109) neural net weights, are processed by a network having 0(102) weights. ROC curves are presented for mine detection in real and simulated imagery.

[The method and application to construct experience recommendation platform of acupuncture ancient books based on data mining technology].

PubMed

Chen, Chuyun; Hong, Jiaming; Zhou, Weilin; Lin, Guohua; Wang, Zhengfei; Zhang, Qufei; Lu, Cuina; Lu, Lihong

2017-07-12

To construct a knowledge platform of acupuncture ancient books based on data mining technology, and to provide retrieval service for users. The Oracle 10 g database was applied and JAVA was selected as development language; based on the standard library and ancient books database established by manual entry, a variety of data mining technologies, including word segmentation, speech tagging, dependency analysis, rule extraction, similarity calculation, ambiguity analysis, supervised classification technology were applied to achieve text automatic extraction of ancient books; in the last, through association mining and decision analysis, the comprehensive and intelligent analysis of disease and symptom, meridians, acupoints, rules of acupuncture and moxibustion in acupuncture ancient books were realized, and retrieval service was provided for users through structure of browser/server (B/S). The platform realized full-text retrieval, word frequency analysis and association analysis; when diseases or acupoints were searched, the frequencies of meridian, acupoints (diseases) and techniques were presented from high to low, meanwhile the support degree and confidence coefficient between disease and acupoints (special acupoint), acupoints and acupoints in prescription, disease or acupoints and technique were presented. The experience platform of acupuncture ancient books based on data mining technology could be used as a reference for selection of disease, meridian and acupoint in clinical treatment and education of acupuncture and moxibustion.

DrugQuest - a text mining workflow for drug association discovery.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

Change detection from remotely sensed images: From pixel-based to object-based approaches

NASA Astrophysics Data System (ADS)

Hussain, Masroor; Chen, Dongmei; Cheng, Angela; Wei, Hui; Stanley, David

2013-06-01

The appetite for up-to-date information about earth's surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

[Hygienic and ergonomic analysis of the technology for sinking main and subsidiary mine shafts].

PubMed

Meniaĭlo, N I; Tyshlek, E G; Gritsenko, V S; Shemiakin, G M

1989-01-01

The labour conditions in mine shafts do not correspond to the existing ergonomic and hygienic norms. Drilling and blasting techniques are most hazardous as to the gravity and duration of the factors involved. Working conditions normalization should be based on the elaboration of specifically innovative technologies which should envisage the workers' periodic staying in the mine shaft area during the work shift.

Untangling Topic Threads in Chat-Based Communication: A Case Study

DTIC Science & Technology

2011-08-01

learning techniques such as clustering are very popular for analyzing text for topic identification (Anjewierden,, Kollöffel and Hulshof 2007; Adams...Anjewierden, A., Kollöffel, B., and Hulshof , C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to

A coal mine multi-point fiber ethylene gas concentration sensor

NASA Astrophysics Data System (ADS)

Wei, Yubin; Chang, Jun; Lian, Jie; Liu, Tongyu

2015-03-01

Spontaneous combustion of the coal mine goaf is one of the main disasters in the coal mine. The detection technology based on symbolic gas is the main means to realize the spontaneous combustion prediction of the coal mine goaf, and ethylene gas is an important symbol gas of spontaneous combustion in the coal accelerated oxidation stage. In order to overcome the problem of current coal ethylene detection, the paper presents a mine optical fiber multi-point ethylene concentration sensor based on the tunable diode laser absorption spectroscopy. Based on the experiments and analysis of the near-infrared spectrum of ethylene, the system employed the 1.62 μm (DFB) wavelength fiber coupled distributed feedback laser as the light source. By using the wavelength scanning technique and developing a stable fiber coupled Herriot type long path gas absorption cell, a ppm-level high sensitivity detecting system for the concentration of ethylene gas was realized, which could meet the needs of coal mine fire prevention goaf prediction.

Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining

PubMed Central

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level. PMID:25830807

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

PubMed

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

Speciation and distribution of copper in a mining soil using multiple synchrotron-based bulk and microscopic techniques.

PubMed

Yang, Jianjun; Liu, Jin; Dynes, James J; Peak, Derek; Regier, Tom; Wang, Jian; Zhu, Shenhai; Shi, Jiyan; Tse, John S

2014-02-01

Molecular-level understanding of soil Cu speciation and distribution assists in management of Cu contamination in mining sites. In this study, one soil sample, collected from a mining site contaminated since 1950s, was characterized complementarily by multiple synchrotron-based bulk and spatially resolved techniques for the speciation and distribution of Cu as well as other related elements (Fe, Ca, Mn, K, Al, and Si). Bulk X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopy revealed that soil Cu was predominantly associated with Fe oxides instead of soil organic matter. This agreed with the closest association of Cu to Fe by microscopic X-ray fluorescence (U-XRF) and scanning transmission X-ray microscopy (STXM) nanoanalysis, along with the non-occurrence of photoreduction of soil Cu(II) by quick Cu L3,2-edge XANES spectroscopy (Q-XANES) which often occurs when Cu organic complexes are present. Furthermore, bulk-EXAFS and STXM-coupled Fe L3,2-edge nano-XANES analysis revealed soil Cu adsorbed primarily to Fe(III) oxides by inner-sphere complexation. Additionally, Cu K-edge μ-XANES, L3,2-edge bulk-XANES, and successive Q-XANES results identified the presence of Cu2S rather than radiation-damage artifacts dominant in certain microsites of the mining soil. This study demonstrates the great benefits in use of multiple combined synchrotron-based techniques for comprehensive understanding of Cu speciation in heterogeneous soil matrix, which facilitates our prediction of Cu reactivity and environmental fate in the mining site.

Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.

PubMed

Wang, Weiqi; Wang, Yanbo Justin; Bañares-Alcántara, René; Coenen, Frans; Cui, Zhanfeng

2009-12-01

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

Arguments for the need of mining education continuity and development in Romania

NASA Astrophysics Data System (ADS)

Bud, I.; Duma, S.; Pasca, I.; Gusat, D.

2018-01-01

Mining is considered the oldest conscious man activity. In the beginning, man searched for hard rocks in the outskirts area and used it to make weapons and ornaments. Subsequently, civilizations evolved through the development of infrastructure, buildings and monuments and finally, weapons. For all these it was necessary to have mineral raw materials obtained under increasingly difficult conditions, through increasingly evolved techniques. In this way, the art of mining and metallurgy was born, which led to the formation of scientific bases. The mining activity was equally art and science. The art and the science have been learned and taught in schools since ancient times and continue today in large universities with mining engineering, metallurgy, mining topography, mining environmental protection, and geology. Lately, in Romania, the mining high school has reached a deadlock and the middle and professional school has collapsed. The development of infrastructure, construction, etc. requires the exploitation and valorisation of mineral resources based on specialists. The paper warns against the danger of losing tradition and skills in mining engineers formation and militate for the re-establishment of professional and technical schools.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Role of Knowledge Management and Analytical CRM in Business: Data Mining Based Framework

ERIC Educational Resources Information Center

Ranjan, Jayanthi; Bhatnagar, Vishal

2011-01-01

Purpose: The purpose of the paper is to provide a thorough analysis of the concepts of business intelligence (BI), knowledge management (KM) and analytical CRM (aCRM) and to establish a framework for integrating all the three to each other. The paper also seeks to establish a KM and aCRM based framework using data mining (DM) techniques, which…

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems.

PubMed

Fallah, Mina; Niakan Kalhori, Sharareh R

2017-10-01

Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients' needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients' self-management. Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2011 CFR

2011-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2013 CFR

2013-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2014 CFR

2014-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2012 CFR

2012-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Miyahara, Koji; Peker, Kadir A.; Radhakrishnan, Regunathan; Xiong, Ziyou

2003-12-01

We discuss the meaning and significance of the video mining problem, and present our work on some aspects of video mining. A simple definition of video mining is unsupervised discovery of patterns in audio-visual content. Such purely unsupervised discovery is readily applicable to video surveillance as well as to consumer video browsing applications. We interpret video mining as content-adaptive or "blind" content processing, in which the first stage is content characterization and the second stage is event discovery based on the characterization obtained in stage 1. We discuss the target applications and find that using a purely unsupervised approach are too computationally complex to be implemented on our product platform. We then describe various combinations of unsupervised and supervised learning techniques that help discover patterns that are useful to the end-user of the application. We target consumer video browsing applications such as commercial message detection, sports highlights extraction etc. We employ both audio and video features. We find that supervised audio classification combined with unsupervised unusual event discovery enables accurate supervised detection of desired events. Our techniques are computationally simple and robust to common variations in production styles etc.

Monitoring of the mercury mining site Almadén implementing remote sensing technologies.

PubMed

Schmid, Thomas; Rico, Celia; Rodríguez-Rastrero, Manuel; José Sierra, María; Javier Díaz-Puente, Fco; Pelayo, Marta; Millán, Rocio

2013-08-01

The Almadén area in Spain has a long history of mercury mining with prolonged human-induced activities that are related to mineral extraction and metallurgical processes before the closure of the mines and a more recent post period dominated by projects that reclaim the mine dumps and tailings and recuperating the entire mining area. Furthermore, socio-economic alternatives such as crop cultivation, livestock breeding and tourism are increasing in the area. Up till now, only scattered information on these activities is available from specific studies. However, improved acquisition systems using satellite borne data in the last decades opens up new possibilities to periodically study an area of interest. Therefore, comparing the influence of these activities on the environment and monitoring their impact on the ecosystem vastly improves decision making for the public policy makers to implement appropriate land management measures and control environmental degradation. The objective of this work is to monitor environmental changes affected by human-induced activities within the Almadén area occurring before, during and after the mine closure over a period of nearly three decades. To achieve this, data from numerous sources at different spatial scales and time periods are implemented into a methodology based on advanced remote sensing techniques. This includes field spectroradiometry measurements, laboratory analyses and satellite borne data of different surface covers to detect land cover and use changes throughout the mining area. Finally, monitoring results show that the distribution of areas affected by mercury mining is rapidly diminishing since activities ceased and that rehabilitated mining areas form a new landscape. This refers to mine tailings that have been sealed and revegetated as well as an open pit mine that has been converted to an "artificial" lake surface. Implementing a methodology based on remote sensing techniques that integrate data from several sources at different scales greatly improves the regional characterization and monitoring of an area dominated by mercury mining activities. Copyright © 2013 Elsevier Inc. All rights reserved.

Mining Land Subsidence Monitoring Using SENTINEL-1 SAR Data

NASA Astrophysics Data System (ADS)

Yuan, W.; Wang, Q.; Fan, J.; Li, H.

2017-09-01

In this paper, DInSAR technique was used to monitor land subsidence in mining area. The study area was selected in the coal mine area located in Yuanbaoshan District, Chifeng City, and Sentinel-1 data were used to carry out DInSAR techniqu. We analyzed the interferometric results by Sentinel-1 data from December 2015 to May 2016. Through the comparison of the results of DInSAR technique and the location of the mine on the optical images, it is shown that DInSAR technique can be used to effectively monitor the land subsidence caused by underground mining, and it is an effective tool for law enforcement of over-mining.

Biogeochemical behaviour and bioremediation of uranium in waters of abandoned mines.

PubMed

Mkandawire, Martin

2013-11-01

The discharges of uranium and associated radionuclides as well as heavy metals and metalloids from waste and tailing dumps in abandoned uranium mining and processing sites pose contamination risks to surface and groundwater. Although many more are being planned for nuclear energy purposes, most of the abandoned uranium mines are a legacy of uranium production that fuelled arms race during the cold war of the last century. Since the end of cold war, there have been efforts to rehabilitate the mining sites, initially, using classical remediation techniques based on high chemical and civil engineering. Recently, bioremediation technology has been sought as alternatives to the classical approach due to reasons, which include: (a) high demand of sites requiring remediation; (b) the economic implication of running and maintaining the facilities due to high energy and work force demand; and (c) the pattern and characteristics of contaminant discharges in most of the former uranium mining and processing sites prevents the use of classical methods. This review discusses risks of uranium contamination from abandoned uranium mines from the biogeochemical point of view and the potential and limitation of uranium bioremediation technique as alternative to classical approach in abandoned uranium mining and processing sites.

Preliminary studies of the CHIM electrogeochemical method at the Kokomo Mine, Russell Gulch, Colorado

USGS Publications Warehouse

Smith, D.B.; Hoover, D.B.; Sanzolone, R.F.

1993-01-01

The CHIM electrogeochemical exploration technique was developed in the former Soviet Union about 20 years ago and is claimed to be effective in exploration for concealed mineral deposits that are not detectable by other geochemical or geophysical techniques. The method involves providing a high-voltage direct current to an anode and an array of special collector cathodes. Cations mobile in the electric field are collected at the cathodes and their concentrations determined. The U.S. Geological Survey started a study of the CHIM method by conducting tests over a precious- and base-metal-bearing quartz vein covered with 3 m of colluvial soil and weathered bedrock near the Kokomo Mine, Colorado. The tests show that the CHIM method gives better definition of the vein than conventional soil geochemistry based on a total-dissolution technique. The CHIM technique gives reproducible geochemical anomaly patterns, but the absolute concentrations depend on local site variability as well as temporal variations. Weak partial dissolutions of soils at the Kokomo Mine by an enzyme leach, a dilute acetic acid leach, and a dilute hydrochloric acid leach show results comparable to those from the CHIM method. This supports the idea that the CHIM technique is essentially a weak in-situ partial extraction involving only ions able to move in a weak electric field. ?? 1993.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shumway, R.H.; McQuarrie, A.D.

Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less

Using data mining techniques to characterize participation in observational studies.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care-based medical home pilot programme and compare the performance of commonly used classification approaches - logistic regression, support vector machines, random forests and classification tree analysis (CTA) - in correctly classifying participants and non-participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention. © 2016 John Wiley & Sons, Ltd.

Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital.

PubMed

Yoo, Sooyoung; Cho, Minsu; Kim, Eunhye; Kim, Seok; Sim, Yerim; Yoo, Donghyun; Hwang, Hee; Song, Minseok

2016-04-01

Many hospitals are increasing their efforts to improve processes because processes play an important role in enhancing work efficiency and reducing costs. However, to date, a quantitative tool has not been available to examine the before and after effects of processes and environmental changes, other than the use of indirect indicators, such as mortality rate and readmission rate. This study used process mining technology to analyze process changes based on changes in the hospital environment, such as the construction of a new building, and to measure the effects of environmental changes in terms of consultation wait time, time spent per task, and outpatient care processes. Using process mining technology, electronic health record (EHR) log data of outpatient care before and after constructing a new building were analyzed, and the effectiveness of the technology in terms of the process was evaluated. Using the process mining technique, we found that the total time spent in outpatient care did not increase significantly compared to that before the construction of a new building, considering that the number of outpatients increased, and the consultation wait time decreased. These results suggest that the operation of the outpatient clinic was effective after changes were implemented in the hospital environment. We further identified improvements in processes using the process mining technique, thereby demonstrating the usefulness of this technique for analyzing complex hospital processes at a low cost. This study confirmed the effectiveness of process mining technology at an actual hospital site. In future studies, the use of process mining technology will be expanded by applying this approach to a larger variety of process change situations. Copyright © 2016. Published by Elsevier Ireland Ltd.

Constraint-based Data Mining

NASA Astrophysics Data System (ADS)

Boulicaut, Jean-Francois; Jeudy, Baptiste

Knowledge Discovery in Databases (KDD) is a complex interactive process. The promising theoretical framework of inductive databases considers this is essentially a querying process. It is enabled by a query language which can deal either with raw data or patterns which hold in the data. Mining patterns turns to be the so-called inductive query evaluation process for which constraint-based Data Mining techniques have to be designed. An inductive query specifies declaratively the desired constraints and algorithms are used to compute the patterns satisfying the constraints in the data. We survey important results of this active research domain. This chapter emphasizes a real breakthrough for hard problems concerning local pattern mining under various constraints and it points out the current directions of research as well.

Development of Multiscale Biological Image Data Analysis: Review of 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics, Santa Barbara, USA (BII06)

PubMed Central

Auer, Manfred; Peng, Hanchuan; Singh, Ambuj

2007-01-01

The 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics was held at Santa Barbara, on Sept 7–8, 2006. Based on the presentations at the workshop, we selected and compiled this collection of research articles related to novel algorithms and enabling techniques for bio- and biomedical image analysis, mining, visualization, and biology applications. PMID:17634090

The design and implementation of web mining in web sites security

NASA Astrophysics Data System (ADS)

Li, Jian; Zhang, Guo-Yin; Gu, Guo-Chang; Li, Jian-Li

2003-06-01

The backdoor or information leak of Web servers can be detected by using Web Mining techniques on some abnormal Web log and Web application log data. The security of Web servers can be enhanced and the damage of illegal access can be avoided. Firstly, the system for discovering the patterns of information leakages in CGI scripts from Web log data was proposed. Secondly, those patterns for system administrators to modify their codes and enhance their Web site security were provided. The following aspects were described: one is to combine web application log with web log to extract more information, so web data mining could be used to mine web log for discovering the information that firewall and Information Detection System cannot find. Another approach is to propose an operation module of web site to enhance Web site security. In cluster server session, Density-Based Clustering technique is used to reduce resource cost and obtain better efficiency.

Roadway backfill method to prevent geohazards induced by room and pillar mining: a case study in Changxing coal mine, China

NASA Astrophysics Data System (ADS)

Zhou, Nan; Li, Meng; Zhang, Jixiong; Gao, Rui

2016-11-01

Coal mines in the western areas of China experience low mining rates and induce many geohazards when using the room and pillar mining method. In this research, we proposed a roadway backfill method during longwall mining to target these problems. We tested the mechanical properties of the backfill materials to determine a reasonable ratio of backfill materials for the driving roadway during longwall mining. We also introduced the roadway layout and the backfill mining technique required for this method. Based on the effects of the abutment stress from a single roadway driving task, we designed the distance between roadways and a driving and filling sequence for multiple-roadway driving. By doing so, we found the movement characteristics of the strata with quadratic stabilization for backfill mining during roadway driving. Based on this research, the driving and filling sequence of the 3101 working face in Changxing coal mine was optimized to avoid the superimposed influence of mining-induced stress. According to the analysis of the surface monitoring data, the accumulated maximum subsidence is 15 mm and the maximum horizontal deformation is 0.8 mm m-1, which indicated that the ground basically had no obvious deformation after the implementation of the roadway backfill method at 3101 working face.

Lunar surface mining for automated acquisition of helium-3: Methods, processes, and equipment

NASA Technical Reports Server (NTRS)

Li, Y. T.; Wittenberg, L. J.

1992-01-01

In this paper, several techniques considered for mining and processing the regolith on the lunar surface are presented. These techniques have been proposed and evaluated based primarily on the following criteria: (1) mining operations should be relatively simple; (2) procedures of mineral processing should be few and relatively easy; (3) transferring tonnages of regolith on the Moon should be minimized; (4) operations outside the lunar base should be readily automated; (5) all equipment should be maintainable; and (6) economic benefit should be sufficient for commercial exploitation. The economic benefits are not addressed in this paper; however, the energy benefits have been estimated to be between 250 and 350 times the mining energy. A mobile mining scheme is proposed that meets most of the mining objectives. This concept uses a bucket-wheel excavator for excavating the regolith, several mechanical electrostatic separators for beneficiation of the regolith, a fast-moving fluidized bed reactor to heat the particles, and a palladium diffuser to separate H2 from the other solar wind gases. At the final stage of the miner, the regolith 'tailings' are deposited directly into the ditch behind the miner and cylinders of the valuable solar wind gases are transported to a central gas processing facility. During the production of He-3, large quantities of valuable H2, H2O, CO, CO2, and N2 are produced for utilization at the lunar base. For larger production of He-3 the utilization of multiple-miners is recommended rather than increasing their size. Multiple miners permit operations at more sites and provide redundancy in case of equipment failure.

Lunar surface mining for automated acquisition of helium-3: Methods, processes, and equipment

NASA Astrophysics Data System (ADS)

Li, Y. T.; Wittenberg, L. J.

1992-09-01

In this paper, several techniques considered for mining and processing the regolith on the lunar surface are presented. These techniques have been proposed and evaluated based primarily on the following criteria: (1) mining operations should be relatively simple; (2) procedures of mineral processing should be few and relatively easy; (3) transferring tonnages of regolith on the Moon should be minimized; (4) operations outside the lunar base should be readily automated; (5) all equipment should be maintainable; and (6) economic benefit should be sufficient for commercial exploitation. The economic benefits are not addressed in this paper; however, the energy benefits have been estimated to be between 250 and 350 times the mining energy. A mobile mining scheme is proposed that meets most of the mining objectives. This concept uses a bucket-wheel excavator for excavating the regolith, several mechanical electrostatic separators for beneficiation of the regolith, a fast-moving fluidized bed reactor to heat the particles, and a palladium diffuser to separate H2 from the other solar wind gases. At the final stage of the miner, the regolith 'tailings' are deposited directly into the ditch behind the miner and cylinders of the valuable solar wind gases are transported to a central gas processing facility. During the production of He-3, large quantities of valuable H2, H2O, CO, CO2, and N2 are produced for utilization at the lunar base. For larger production of He-3 the utilization of multiple-miners is recommended rather than increasing their size. Multiple miners permit operations at more sites and provide redundancy in case of equipment failure.

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali; Martin, Dawn (Elliott); Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Improve Data Mining and Knowledge Discovery through the use of MatLab

NASA Technical Reports Server (NTRS)

Shaykahian, Gholan Ali; Martin, Dawn Elliott; Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Mining Interactions in Immersive Learning Environments for Real-Time Student Feedback

ERIC Educational Resources Information Center

Kennedy, Gregor; Ioannou, Ioanna; Zhou, Yun; Bailey, James; O'Leary, Stephen

2013-01-01

The analysis and use of data generated by students' interactions with learning systems or programs--learning analytics--has recently gained widespread attention in the educational technology community. Part of the reason for this interest is based on the potential of learning analytic techniques such as data mining to find hidden patterns in…

The Effectiveness of Web-Based Learning Environment: A Case Study of Public Universities in Kenya

ERIC Educational Resources Information Center

Kirui, Paul A.; Mutai, Sheila J.

2010-01-01

Web mining is emerging in many aspects of e-learning, aiming at improving online learning and teaching processes and making them more transparent and effective. Researchers using Web mining tools and techniques are challenged to learn more about the online students' reshaping online courses and educational websites, and create tools for…

Identifying Engineering Students' English Sentence Reading Comprehension Errors: Applying a Data Mining Technique

ERIC Educational Resources Information Center

Tsai, Yea-Ru; Ouyang, Chen-Sen; Chang, Yukon

2016-01-01

The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique…

Mining and beneficiation: A review of possible lunar applications

NASA Technical Reports Server (NTRS)

Chamberlain, Peter G.

1991-01-01

Successful exploration of Mars and outer space may require base stations strategically located on the Moon. Such bases must develop a certain self-sufficiency, particularly in the critical life support materials, fuel components, and construction materials. Technology is reviewed for the first steps in lunar resource recovery-mining and beneficiation. The topic is covered in three main categories: site selection; mining; and beneficiation. It will also include (in less detail) in-situ processes. The text described mining technology ranging from simple diggings and hauling vehicles (the strawman) to more specialized technology including underground excavation methods. The section of beneficiation emphasizes dry separation techniques and methods of sorting the ore by particle size. In-situ processes, chemical and thermal, are identified to stimulate further thinking by future researchers.

Application and Evaluation of ALOS PALSAR Data for Monitoring of Mining Induced Surface Deformations Using Interferometric Techniques

NASA Astrophysics Data System (ADS)

Walter, Diana; Wegmuller, Urs; Spreckels, Volker; Busch, Wolfgang

2008-11-01

The main objective of the projects "Determination of ground motions in mining areas by interferometric analyses of ALOS data" (ALOS ADEN 3576, ESA) and "Monitoring of mining induced surface deformation" (ALOS-RA-094, JAXA) is to evaluate PALSAR data for surface deformation monitoring, using interferometric techniques. We present monitoring results of surface movements for an active hard coal colliery of the German hard coal mining company RAG Deutsche Steinkohle (RAG). Underground mining activities lead to ground movements at the surface with maximum subsidence rates of about 10cm per month for the test site. In these projects the L-band sensor clearly demonstrates the good potential for deformation monitoring in active mining areas, especially in rural areas. In comparison to C-band sensors we clearly observe advantages in resolving the high deformation gradients that are present in this area and we achieve a more complete spatial coverage than with C-band. Extensive validation data based on levelling data and GPS measurements are available within RAǴs GIS based database "GeoMon" and thus enable an adequate analysis of the quality of the interferometric results. Previous analyses confirm the good accuracy of PALSAR data for deformation monitoring in mining areas. Furthermore, we present results of special investigations like precision geocoding of PALSAR data and corner reflector analysis. At present only DInSAR results are obtained due to the currently available number of PALSAR scenes. For the future we plan to also apply Persistent Scatterer Interferometry (PSI) using longer series of PALSAR data.

PubMedMiner: Mining and Visualizing MeSH-based Associations in PubMed.

PubMed

Zhang, Yucan; Sarkar, Indra Neil; Chen, Elizabeth S

2014-01-01

The exponential growth of biomedical literature provides the opportunity to develop approaches for facilitating the identification of possible relationships between biomedical concepts. Indexing by Medical Subject Headings (MeSH) represent high-quality summaries of much of this literature that can be used to support hypothesis generation and knowledge discovery tasks using techniques such as association rule mining. Based on a survey of literature mining tools, a tool implemented using Ruby and R - PubMedMiner - was developed in this study for mining and visualizing MeSH-based associations for a set of MEDLINE articles. To demonstrate PubMedMiner's functionality, a case study was conducted that focused on identifying and comparing comorbidities for asthma in children and adults. Relative to the tools surveyed, the initial results suggest that PubMedMiner provides complementary functionality for summarizing and comparing topics as well as identifying potentially new knowledge.

Challenges in recovering resources from acid mine drainage

USGS Publications Warehouse

Nordstrom, D. Kirk; Bowell, Robert J.; Campbell, Kate M.; Alpers, Charles N.

2017-01-01

Metal recovery from mine waters and effluents is not a new approach but one that has occurred largely opportunistically over the last four millennia. Due to the need for low-cost resources and increasingly stringent environmental conditions, mine waters are being considered in a fresh light with a designed, deliberate approach to resource recovery often as part of a larger water treatment evaluation. Mine water chemistry is highly dependent on many factors including geology, ore deposit composition and mineralogy, mining methods, climate, site hydrology, and others. Mine waters are typically Ca-Mg-SO4±Al±Fe with a broad range in pH and metal content. The main issue in recovering components of these waters having potential economic value, such as base metals or rare earth elements, is the separation of these from more reactive metals such as Fe and Al. Broad categories of methods for separating and extracting substances from acidic mine drainage are chemical and biological. Chemical methods include solution, physicochemical, and electrochemical technologies. Advances in membrane techniques such as reverse osmosis have been substantial and the technique is both physical and chemical. Biological methods may be further divided into microbiological and macrobiological, but only the former is considered here as a recovery method, as the latter is typically used as a passive form of water treatment.

Characterization of As-polluted soils by laboratory X-ray-based techniques coupled with sequential extractions and electron microscopy: the case of Crocette gold mine in the Monte Rosa mining district (Italy).

PubMed

Allegretta, Ignazio; Porfido, Carlo; Martin, Maria; Barberis, Elisabetta; Terzano, Roberto; Spagnuolo, Matteo

2018-06-24

Arsenic concentration and distribution were studied by combining laboratory X-ray-based techniques (wavelength dispersive X-ray fluorescence (WDXRF), micro X-ray fluorescence (μXRF), and X-ray powder diffraction (XRPD)), field emission scanning electron microscopy equipped with microanalysis (FE-SEM-EDX), and sequential extraction procedure (SEP) coupled to total reflection X-ray fluorescence (TXRF) analysis. This approach was applied to three contaminated soils and one mine tailing collected near the gold extraction plant at the Crocette gold mine (Macugnaga, VB) in the Monte Rosa mining district (Piedmont, Italy). Arsenic (As) concentration, measured with WDXRF, ranged from 145 to 40,200 mg/kg. XRPD analysis evidenced the presence of jarosite and the absence of any As-bearing mineral, suggesting a high weathering grade and strong oxidative conditions. However, small domains of Fe arsenate were identified by combining μXRF with FE-SEM-EDX. SEP results revealed that As was mainly associated to amorphous Fe oxides/hydroxides or hydroxysulfates (50-80%) and the combination of XRPD and FE-SEM-EDX suggested that this phase could be attributed to schwertmannite. On the basis of the reported results, As is scarcely mobile, even if a consistent As fraction (1-3 g As/kg of soil) is still potentially mobilizable. In general, the proposed combination of laboratory X-ray techniques could be successfully employed to unravel environmental issues related to metal(loid) pollution in soil and sediments.

Minehunting sonar system research and development

NASA Astrophysics Data System (ADS)

Ferguson, Brian

2002-05-01

Sea mines have the potential to threaten the freedom of the seas by disrupting maritime trade and restricting the freedom of maneuver of navies. The acoustic detection, localization, and classification of sea mines involves a sequence of operations starting with the transmission of a sonar pulse and ending with an operator interpreting the information on a sonar display. A recent improvement to the process stems from the application of neural networks to the computed aided detection of sea mines. The advent of ultrawideband sonar transducers together with pulse compression techniques offers a thousandfold increase in the bandwidth-time product of conventional minehunting sonar transmissions enabling stealth mines to be detected at longer ranges. These wideband signals also enable mines to be imaged at safe standoff distances by applying tomographic image reconstruction techniques. The coupling of wideband transducer technology with synthetic aperture processing enhances the resolution of side scan sonars in both the cross-track and along-track directions. The principles on which conventional and advanced minehunting sonars are based are reviewed and the results of applying novel sonar signal processing algorithms to high-frequency sonar data collected in Australian waters are presented.

Cooperative organic mine avoidance path planning

NASA Astrophysics Data System (ADS)

McCubbin, Christopher B.; Piatko, Christine D.; Peterson, Adam V.; Donnald, Creighton R.; Cohen, David

2005-06-01

The JHU/APL Path Planning team has developed path planning techniques to look for paths that balance the utility and risk associated with different routes through a minefield. Extending on previous years' efforts, we investigated real-world Naval mine avoidance requirements and developed a tactical decision aid (TDA) that satisfies those requirements. APL has developed new mine path planning techniques using graph based and genetic algorithms which quickly produce near-minimum risk paths for complicated fitness functions incorporating risk, path length, ship kinematics, and naval doctrine. The TDA user interface, a Java Swing application that obtains data via Corba interfaces to path planning databases, allows the operator to explore a fusion of historic and in situ mine field data, control the path planner, and display the planning results. To provide a context for the minefield data, the user interface also renders data from the Digital Nautical Chart database, a database created by the National Geospatial-Intelligence Agency containing charts of the world's ports and coastal regions. This TDA has been developed in conjunction with the COMID (Cooperative Organic Mine Defense) system. This paper presents a description of the algorithms, architecture, and application produced.

Text and Structural Data Mining of Influenza Mentions in Web and Social Media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corley, Courtney D.; Cook, Diane; Mikler, Armin R.

Text and structural data mining of Web and social media (WSM) provides a novel disease surveillance resource and can identify online communities for targeted public health communications (PHC) to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5-October-2008 to 21-March-2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like-illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags.

Coal mine burns drainage gas to generate power for profit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholes, W.A.

A recently commissioned gas turbine power plant that uses methane gas recovered from a coal mine is described. The power plant uses the ASEA Stal GT35B series gas turbines with a base load rating on gas of 12.9 MW at 29.3% efficiency. The plant was installed at a cost of $4 million, as part of an extensive system for removing the methane from the coal mine, enabling higher ratio of coal production to be achieved in safety with modern longwall mining techniques. The plant will save the mine up to $250,000 per month on its electricity bill plus generate profitmore » from the sale of surplus power to the local activity.« less

Using a Data Mining Approach to Develop a Student Engagement-Based Institutional Typology. IR Applications, Volume 18, February 8, 2009

ERIC Educational Resources Information Center

Luan, Jing; Zhao, Chun-Mei; Hayek, John C.

2009-01-01

Data mining provides both systematic and systemic ways to detect patterns of student engagement among students at hundreds of institutions. Using traditional statistical techniques alone, the task would be significantly difficult--if not impossible--considering the size and complexity in both data and analytical approaches necessary for this…

Use of Data Mining to Reveal Body Mass Index (BMI): Patterns among Pennsylvania Schoolchildren, Pre-K to Grade 12

ERIC Educational Resources Information Center

YoussefAgha, Ahmed H.; Lohrmann, David K.; Jayawardene, Wasantha P.

2013-01-01

Background: Health eTools for Schools was developed to assist school nurses with routine entries, including height and weight, on student health records, thus providing a readily accessible data base. Data-mining techniques were applied to this database to determine if clinically signi?cant results could be generated. Methods: Body mass index…

Space-Based Counterforce in the Second Nuclear Age

DTIC Science & Technology

2015-04-01

nuclear counterforce missiles and anti-ICBM missiles and mines ], equipped with the complete spectrum of sensing equipment including infrared...yield, fallout minimized nuclear weapons. Each ship would also have a robust loadout of space mines and anti-ICBM missiles or directed energy...29 incoming asteroids or comets which could impact Earth carrying whatever deflection technique deemed appropriate to the threat, potentially

Predicting Effective Course Conduction Strategy Using Datamining Techniques

ERIC Educational Resources Information Center

Parkavi, A.; Lakshmi, K.; Srinivasa, K. G.

2017-01-01

Data analysis techniques can be used to analyze the pattern of data in different fields. Based on the analysis' results, it is recommended that suggestions be provided to decision making authorities. The data mining techniques can be used in educational domain to improve the outcome of the educational sectors. The authors carried out this research…

A primer to frequent itemset mining for bioinformatics

PubMed Central

Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung Nghia; Vanden Berghe, Wim; Goethals, Bart

2015-01-01

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences. PMID:24162173

RANWAR: rank-based weighted association rule mining from gene expression and methylation data.

PubMed

Mallik, Saurav; Mukhopadhyay, Anirban; Maulik, Ujjwal

2015-01-01

Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported.

PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction.

PubMed

Krallinger, Martin; Rodriguez-Penagos, Carlos; Tendulkar, Ashish; Valencia, Alfonso

2009-07-01

There is an increasing interest in using literature mining techniques to complement information extracted from annotation databases or generated by bioinformatics applications. Here we present PLAN2L, a web-based online search system that integrates text mining and information extraction techniques to access systematically information useful for analyzing genetic, cellular and molecular aspects of the plant model organism Arabidopsis thaliana. Our system facilitates a more efficient retrieval of information relevant to heterogeneous biological topics, from implications in biological relationships at the level of protein interactions and gene regulation, to sub-cellular locations of gene products and associations to cellular and developmental processes, i.e. cell cycle, flowering, root, leaf and seed development. Beyond single entities, also predefined pairs of entities can be provided as queries for which literature-derived relations together with textual evidences are returned. PLAN2L does not require registration and is freely accessible at http://zope.bioinfo.cnio.es/plan2l.

APPLYING DATA MINING APPROACHES TO FURTHER ...

EPA Pesticide Factsheets

This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space. This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

Emerging technology becomes an opportunity for EOS

NASA Astrophysics Data System (ADS)

Fargion, Giulietta S.; Harberts, Robert; Masek, Jeffrey G.

1996-11-01

During the last decade, we have seen an explosive growth in our ability to collect and generate data. When implemented, NASA's Earth observing system data information system (EOSDIS) will receive about 50 gigabytes of remotely sensed image data per hour. This will generate an urgent need for new techniques and tools that can automatically and intelligently assist in transforming this abundance of data into useful knowledge. Some emerging technologies that address these challenges include data mining and knowledge discovery in databases (KDD). The most basic data mining application is a content-based search (examples include finding images of particular meteorological phenomena or identifying data that have been previously mined or interpreted). In order that these technologies be effectively exploited for EOSDIS development, a better understanding of data mining and the requirements for using this technology is necessary. The authors are currently undertaking a project exploring the requirements and options of content-based search and data mining for use on EOSDIS. The scope of the project is to develop a prototype with which to investigate user interface concepts, requirements, and designs relevant for EOSDIS core system (ECS) subsystem utilizing these techniques. The goal is to identify a generic handling of these functions. This prototype will help identify opportunities which the earth science community and EOSDIS can use to meet the challenges of collecting, searching, retrieving, and interacting with abundant data resources in highly productive ways.

Gaining Insights on Nasopharyngeal Carcinoma Treatment Outcome Using Clinical Data Mining Techniques.

PubMed

Ghaibeh, A Ammar; Kasem, Asem; Ng, Xun Jin; Nair, Hema Latha Krishna; Hirose, Jun; Thiruchelvam, Vinesh

2018-01-01

The analysis of Electronic Health Records (EHRs) is attracting a lot of research attention in the medical informatics domain. Hospitals and medical institutes started to use data mining techniques to gain new insights from the massive amounts of data that can be made available through EHRs. Researchers in the medical field have often used descriptive statistics and classical statistical methods to prove assumed medical hypotheses. However, discovering new insights from large amounts of data solely based on experts' observations is difficult. Using data mining techniques and visualizations, practitioners can find hidden knowledge, identify interesting patterns, or formulate new hypotheses to be further investigated. This paper describes a work in progress on using data mining methods to analyze clinical data of Nasopharyngeal Carcinoma (NPC) cancer patients. NPC is the fifth most common cancer among Malaysians, and the data analyzed in this study was collected from three states in Malaysia (Kuala Lumpur, Sabah and Sarawak), and is considered to be the largest up-to-date dataset of its kind. This research is addressing the issue of cancer recurrence after the completion of radiotherapy and chemotherapy treatment. We describe the procedure, problems, and insights gained during the process.

Privacy Preserving Sequential Pattern Mining in Data Stream

NASA Astrophysics Data System (ADS)

Huang, Qin-Hua

The privacy preserving data mining technique researches have gained much attention in recent years. For data stream systems, wireless networks and mobile devices, the related stream data mining techniques research is still in its' early stage. In this paper, an data mining algorithm dealing with privacy preserving problem in data stream is presented.

Discovery of Information Diffusion Process in Social Networks

NASA Astrophysics Data System (ADS)

Kim, Kwanho; Jung, Jae-Yoon; Park, Jonghun

Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

Simplified process model discovery based on role-oriented genetic mining.

PubMed

Zhao, Weidong; Liu, Xi; Dai, Weihui

2014-01-01

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

Reliable Early Classification on Multivariate Time Series with Numerical and Categorical Attributes

DTIC Science & Technology

2015-05-22

design a procedure of feature extraction in REACT named MEG (Mining Equivalence classes with shapelet Generators) based on the concept of...Equivalence Classes Mining [12, 15]. MEG can efficiently and effectively generate the discriminative features. In addition, several strategies are proposed...technique of parallel computing [4] to propose a process of pa- rallel MEG for substantially reducing the computational overhead of discovering shapelet

Monitoring of ground movement in open pit iron mines of Carajás Province (Amazon region) based on A-DInSAR techniques using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Silva, Guilherme Gregório; Mura, José Claudio; Paradella, Waldir Renato; Gama, Fabio Furlan; Temporim, Filipe Altoé

2017-04-01

Persistent scatterer interferometry (PSI) analysis of a large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground movement measurements based on a combination of differential SAR interferometry time-series (DTS) and PSI techniques, applied on a large area of extent with open pit iron mines located in Carajás (Brazilian Amazon Region), aiming at detecting linear and nonlinear ground movement. These mines have presented a history of instability, and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground-based radar and total station (prisms). Using a priori information regarding the topographic phase error and a phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X (TSX-1) images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multilook unwrapped interferograms using an extension of SVD to obtain the least-square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferograms to perform the PSI analysis. This procedure improved the capability of the PSI analysis for detecting high rates of deformation, as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risk control.

Monitoring of surface movement in a large area of the open pit iron mines (Carajás, Brazil) based on A-DInSAR techniques using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Mura, José C.; Paradella, Waldir R.; Gama, Fabio F.; Silva, Guilherme G.

2016-10-01

PSI (Persistent Scatterer Interferometry) analysis of large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground deformation measurements based on a combination of DInSAR Time-Series (DTS) and PSI techniques, applied in a large area of open pit iron mines located in Carajás (Brazilian Amazon Region), aiming at detect high rates of linear and nonlinear ground deformation. These mines have presented a historical of instability and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground based radar and total station (prisms). By using a priori information regarding the topographic phase error and phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X-1 images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multi-look unwrapped interferogram using an extension of SVD to obtain the Least-Square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferogram to perform the PSI analysis. This procedure improved the capability of the PSI analysis to detect high rates of deformation as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risks control.

Comparative analysis of data mining techniques for business data

NASA Astrophysics Data System (ADS)

Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

2014-12-01

Data mining is the process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data contained within a database. Companies are using this tool to further understand their customers, to design targeted sales and marketing campaigns, to predict what product customers will buy and the frequency of purchase, and to spot trends in customer preferences that can lead to new product development. In this paper, we conduct a systematic approach to explore several of data mining techniques in business application. The experimental result reveals that all data mining techniques accomplish their goals perfectly, but each of the technique has its own characteristics and specification that demonstrate their accuracy, proficiency and preference.

DEVELOPMENT AND DEMONSTRATION OF A PILOT SCALE FACILITY FOR FABRICATION AND MARKETING OF LIGHTWEIGHT-COAL COMBUSTION BYPRODUCTS-BASED SUPPORTS AND MINE VENTILATION BLOCKS FOR UNDERGROUND MINES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoginder P. Chugh

2002-10-01

The overall goal of this program was to develop a pilot scale facility, and design, fabricate, and market CCBs-based lightweight blocks for mine ventilation control devices, and engineered crib elements and posts for use as artificial supports in underground mines to replace similar wooden elements. This specific project was undertaken to (1) design a pilot scale facility to develop and demonstrate commercial production techniques, and (2) provide technical and marketing support to Fly Lite, Inc to operate the pilot scale facility. Fly Lite, Inc is a joint venture company of the three industrial cooperators who were involved in research intomore » the development of CCBs-based structural materials. The Fly-Lite pilot scale facility is located in McLeansboro, Illinois. Lightweight blocks for use in ventilation stoppings in underground mines have been successfully produced and marketed by the pilot-scale facility. To date, over 16,000 lightweight blocks (30-40 pcf) have been sold to the mining industry. Additionally, a smaller width (6-inch) full-density block was developed in August-September 2002 at the request of a mining company. An application has been submitted to Mine Safety and Health Administration for the developed block approval for use in mines. Commercialization of cribs and posts has also been accomplished. Two generations of cribs have been developed and demonstrated in the field. MSHA designated them suitable for use in mines. To date, over 2,000 crib elements have been sold to mines in Illinois. Two generations of posts were also demonstrated in the field and designated as suitable for use in mines by MSHA. Negotiations are currently underway with a mine in Illinois to market about 1,000 posts per year based on a field demonstration in their mine. It is estimated that 4-5 million tons CCBs (F-fly ash or FBC fly ash) may be utilized if the developed products can be commercially implemented in U.S. coal and non-coal mines.« less

On the classification techniques in data mining for microarray data classification

NASA Astrophysics Data System (ADS)

Aydadenta, Husna; Adiwijaya

2018-03-01

Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

Localization-based super-resolution imaging meets high-content screening.

PubMed

Beghin, Anne; Kechkar, Adel; Butler, Corey; Levet, Florian; Cabillic, Marine; Rossier, Olivier; Giannone, Gregory; Galland, Rémi; Choquet, Daniel; Sibarita, Jean-Baptiste

2017-12-01

Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

Hydrology of the Ferron sandstone aquifer and effects of proposed surface-coal mining in Castle Valley, Utah

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lines, G.C.; Morrissey, D.J.

Coal in the Ferron Sandstone Member of the Mancos Shale of Cretaceous age has traditionally been mined by underground techniques in the Emery Coal Field in the southern end of Castle Valley in east-central Utah. However, approximately 99 million tons are recoverable by surface mining. Ground water in the Ferron is the sole source of supply for the town of Emery, but the aquifer is essentially untapped outside the Emery area. A three-dimensional digital-computer model was used to simulate ground-water flow in the Ferron sandstone aquifer in the Emery area. The model also was used to predict the effects ofmore » dewatering of a proposed surface mine on aquifer potentiometric surfaces and the base flow of streams. Discharge from the proposed surface mine is predicted to average about 0.3 cubic foot per second during the 15 years of mine operation. Dewatering of the mine would affect the potentiometric surface of all sections of the Ferron sanstone aquifer, but the greatest effects would be in the upper section. Modeling results indicate that, except for Christiansen Wash, the dewatering of the proposed surface mine would not affect the base flow of streams.« less

Data Mining Techniques Applied to Hydrogen Lactose Breath Test.

PubMed

Rubio-Escudero, Cristina; Valverde-Fernández, Justo; Nepomuceno-Chamorro, Isabel; Pontes-Balanza, Beatriz; Hernández-Mendoza, Yoedusvany; Rodríguez-Herrera, Alfonso

2017-01-01

Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production. Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients. Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test. Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.

A proposed framework on hybrid feature selection techniques for handling high dimensional educational data

NASA Astrophysics Data System (ADS)

Shahiri, Amirah Mohamed; Husain, Wahidah; Rashid, Nur'Aini Abd

2017-10-01

Huge amounts of data in educational datasets may cause the problem in producing quality data. Recently, data mining approach are increasingly used by educational data mining researchers for analyzing the data patterns. However, many research studies have concentrated on selecting suitable learning algorithms instead of performing feature selection process. As a result, these data has problem with computational complexity and spend longer computational time for classification. The main objective of this research is to provide an overview of feature selection techniques that have been used to analyze the most significant features. Then, this research will propose a framework to improve the quality of students' dataset. The proposed framework uses filter and wrapper based technique to support prediction process in future study.

A geographical information system-based analysis of cancer mortality and population exposure to coal mining activities in West Virginia, United States of America.

PubMed

Hendryx, Michael; Fedorko, Evan; Anesetti-Rothermel, Andrew

2010-05-01

Cancer incidence and mortality rates are high in West Virginia compared to the rest of the United States of America. Previous research has suggested that exposure to activities of the coal mining industry may contribute to elevated cancer mortality, although exposure measures have been limited. This study tests alternative specifications of exposure to mining activity to determine whether a measure based on location of mines, processing plants, coal slurry impoundments and underground slurry injection sites relative to population levels is superior to a previously-reported measure of exposure based on tons mined at the county level, in the prediction of age-adjusted cancer mortality rates. To this end, we utilize two geographical information system (GIS) techniques--exploratory spatial data analysis and inverse distance mapping--to construct new statistical analyses. Total, respiratory and "other" age-adjusted cancer mortality rates in West Virginia were found to be more highly associated with the GIS-exposure measure than the tonnage measure, before and after statistical control for smoking rates. The superior performance of the GIS measure, based on where people in the state live relative to mining activity, suggests that activities of the industry contribute to cancer mortality. Further confirmation of observed phenomena is necessary with person-level studies, but the results add to the body of evidence that coal mining poses environmental risks to population health in West Virginia.

Feasibility of applying data mining techniques for predicting technical difficulties during laparoscopic cholecystectomy based on routine patient work-up in a small community hospital.

PubMed

Stanisic, Veselin; Andjelkovic, Igor; Vlaovic, Darko; Babic, Igor; Kocev, Nikola; Nikolic, Bosko; Milicevic, Miroslav

2013-10-01

Predicting technical difficulties in laparoscopic cholecystectomy (LC) in a small regional hospital increases efficacy, cost-benefit and safety of the procedure. The aim of the study was to assess whether it is possible to accurately predict a difficult LC (DLC) in a small regional hospital based only on the routine available clinical work-up parameters (patient history, ultrasound examination and blood chemistry) and their combinations. A prospective, cohort, of 369 consecutive patients operated by the same surgeon was analyzed. Conversion rate was 10 (2.7%). DLC was registered in 55 (14.90%). Various data mining techniques were applied and assessed. Seven significant predictors of DLC were identified: i) shrunken (fibrotic) gallbladder (GB); ii) ultrasound (US) GB wall thickness >4 mm; iii) >5 attacks of pain lasting >5 hours; iv) WBC >10x109 g/L; v) pericholecystic fluid; vi) urine amylase >380 IU/L, and vii) BMI >30kg/m2. Bayesian network was selected as the best classifier with accuracy of 94.57, specificity 0.98, sensitivity 0.77, AUC 0.96 and F-measure 0.81. It is possible to predict a DLC with high accuracy using data mining techniques, based on routine preoperative clinical parameters and their combinations. Use of sophisticated diagnostic equipment is not necessary.

A structural informatics approach to mine kinase knowledge bases.

PubMed

Brooijmans, Natasja; Mobilio, Dominick; Walker, Gary; Nilakantan, Ramaswamy; Denny, Rajiah A; Feyfant, Eric; Diller, David; Bikker, Jack; Humblet, Christine

2010-03-01

In this paper, we describe a combination of structural informatics approaches developed to mine data extracted from existing structure knowledge bases (Protein Data Bank and the GVK database) with a focus on kinase ATP-binding site data. In contrast to existing systems that retrieve and analyze protein structures, our techniques are centered on a database of ligand-bound geometries in relation to residues lining the binding site and transparent access to ligand-based SAR data. We illustrate the systems in the context of the Abelson kinase and related inhibitor structures. 2009 Elsevier Ltd. All rights reserved.

Evaluating bump control techniques through convergence monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campoli, A.A.

1987-07-01

A coal mine bump is the violent failure of a pillar or pillars due to overstress. Retreat coal mining concentrates stresses on the pillars directly outby gob areas, and the situation becomes critical when mining a coalbed encased in rigid associated strata. Bump control techniques employed by the Olga Mine, McDowell County, WV, were evaluated through convergence monitoring in a Bureau of Mines study. Olga uses a novel pillar splitting mining method to extract 55-ft by 70-ft chain pillars, under 1,100 to 1,550 ft of overburden. Three rows of pillars are mined simultaneously to soften the pillar line and reducemore » strain energy storage capacity. Localized stress reduction (destressing) techniques, auger drilling and shot firing, induced approximately 0.1 in. of roof-to-floor convergence in ''high'' -stress pillars near the gob line. Auger drilling of a ''low''-stress pillar located between two barrier pillars produced no convergence effects.« less

The exploration and prevention of mine water invasion in Feicheng area based on RS

NASA Astrophysics Data System (ADS)

Zheng, Yong-Guo; Wang, Ping; Ting, He

2004-10-01

Recently, when the ninth and tenth were mined in Feiching city mining area, several mine wells occurred on water invasion. Based on systematic interpretation of TMimages in Fei Cheng mining area, authors find that there are five zones of NS trending lineaments, which nearly distribute in radial in TM images. Image processing can be divided into three types, they are spectrum enhancement, spatial filtering and data fusion, the useful methods in this area are auto-adaptive enhancement, density slicing and K-L transform. With ninth and tenth seam coals mined, three mines of east area have broken out serious accidents of water. Statistical materials and the test of water quality drawing off five limestone indicates water-yielding zone near NS, NNE, and NW trending faults, or near intersection point of its and others. In order to solve the problem, using remote sensing and other techniques, we try to find some influential factors on mine flow. Further analyses, such as, the exploration of geology on earth, and microcosmic from rock slice, the authors find that there are some reasons which lead to water invasion such as geological structure, karsts, index and so on, in which the main reason might be north-south deep fracture which is the pathway of well water's distribution, migration and recharge of mine water. There being more complicate geologic structure in the west of mine area, at last, with RS authors point out important zone of mine water invasion which the prevention-control of hazards from mine water and some measures to avoid water blast in future.

Detecting Underground Mine Voids Using Complex Geophysical Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaminski, V. F.; Harbert, W. P.; Hammack, R. W.

2006-12-01

In July 2006, the National Energy Technology Laboratory in collaboration with Department of Geology and Planetary Science, University of Pittsburgh conducted complex ground geophysical surveys of an area known to be underlain by shallow coal mines. Geophysical methods including electromagnetic induction, DC resistivity and seismic reflection were conducted. The purpose of these surveys was to: 1) verify underground mine voids based on a century-old mine map that showed subsurface mine workings georeferenced to match with present location of geophysical test-site located on the territory of Bruceton research center in Pittsburgh, PA, 2) deliniate mine workings that may be potentially filledmore » with electrically conductive water filtrate emerging from adjacent groundwater collectors and 3) establish an equipment calibration site for geophysical instruments. Data from electromagnetic and resistivity surveys were further processed and inverted using EM1DFM, EMIGMA or Earthimager 2D capablilities in order to generate conductivity/depth images. Anomaly maps were generated, that revealed the locations of potential mine openings.« less

Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

H, Vathsala; Koolagudi, Shashidhar G.

2017-10-01

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005).

Identification and elucidation of anthropogenic source contribution in PM10 pollutant: Insight gain from dispersion and receptor models.

PubMed

Roy, Debananda; Singh, Gurdeep; Yadav, Pankaj

2016-10-01

Source apportionment study of PM 10 (Particulate Matter) in a critically polluted area of Jharia coalfield, India has been carried out using Dispersion model, Principle Component Analysis (PCA) and Chemical Mass Balance (CMB) techniques. Dispersion model Atmospheric Dispersion Model (AERMOD) was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas (LPG) & Diesel Generator (DG) set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM 10 . Copyright © 2016. Published by Elsevier B.V.

A strategy for selecting data mining techniques in metabolomics.

PubMed

Banimustafa, Ahmed Hmaidan; Hardy, Nigel W

2012-01-01

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.

Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature

PubMed Central

Joudaki, Hossein; Rashidian, Arash; Minaei-Bidgoli, Behrouz; Mahmoodi, Mahmood; Geraili, Bijan; Nasiri, Mahdi; Arab, Mohammad

2015-01-01

Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to data mining of health care claims. PMID:25560347

LavaNet—Neural network development environment in a general mine planning package

NASA Astrophysics Data System (ADS)

Kapageridis, Ioannis Konstantinou; Triantafyllou, A. G.

2011-04-01

LavaNet is a series of scripts written in Perl that gives access to a neural network simulation environment inside a general mine planning package. A well known and a very popular neural network development environment, the Stuttgart Neural Network Simulator, is used as the base for the development of neural networks. LavaNet runs inside VULCAN™—a complete mine planning package with advanced database, modelling and visualisation capabilities. LavaNet is taking advantage of VULCAN's Perl based scripting environment, Lava, to bring all the benefits of neural network development and application to geologists, mining engineers and other users of the specific mine planning package. LavaNet enables easy development of neural network training data sets using information from any of the data and model structures available, such as block models and drillhole databases. Neural networks can be trained inside VULCAN™ and the results be used to generate new models that can be visualised in 3D. Direct comparison of developed neural network models with conventional and geostatistical techniques is now possible within the same mine planning software package. LavaNet supports Radial Basis Function networks, Multi-Layer Perceptrons and Self-Organised Maps.

Hydrogeochemical studies of historical mining areas in the Humboldt River basin and adjacent areas, northern Nevada

USGS Publications Warehouse

Nash, J. Thomas

2005-01-01

The study area comprises the Humboldt River Basin and adjacent areas, with emphasis on mining areas relatively close to the Humboldt River. The basin comprises about 16,840 mi2 or 10,800,000 acres. The mineral resources of the Humboldt Basin have been investigated by many scientists over the past 100 years, but only recently has our knowledge of regional geology and mine geology been applied to the understanding and evaluation of mining effects on water and environmental quality. The investigations reported here apply some of the techniques and perspectives developed in the Abandoned Mine Lands Initiative (AMLI) of the U.S. Geological Survey (USGS), a program of integrated geological-hydrological-biological-chemical studies underway in the Upper Animas River watershed in Colorado and the Boulder River watershed in, Montana. The goal of my studies of sites and districts is to determine the character of mining-related contamination that is actively or potentially a threat to water quality and to estimate the potential for natural attenuation of that contamination. These geology-based studies and recommendations differ in matters of emphasis and data collection from the biology-based assessments that are the cornerstone of environmental regulations.

Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients.

PubMed

Su, Chao-Ton; Wang, Pa-Chun; Chen, Yan-Cheng; Chen, Li-Fei

2012-08-01

Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Realising the knowledge spiral in healthcare: the role of data mining and knowledge management.

PubMed

Wickramasinghe, Nilmini; Bali, Rajeev K; Gibbons, M Chris; Schaffer, Jonathan

2008-01-01

Knowledge Management (KM) is an emerging business approach aimed at solving current problems such as competitiveness and the need to innovate which are faced by businesses today. The premise for the need for KM is based on a paradigm shift in the business environment where knowledge is central to organizational performance . Organizations trying to embrace KM have many tools, techniques and strategies at their disposal. A vital technique in KM is data mining which enables critical knowledge to be gained from the analysis of large amounts of data and information. The healthcare industry is a very information rich industry. The collecting of data and information permeate most, if not all areas of this industry; however, the healthcare industry has yet to fully embrace KM, let alone the new evolving techniques of data mining. In this paper, we demonstrate the ubiquitous benefits of data mining and KM to healthcare by highlighting their potential to enable and facilitate superior clinical practice and administrative management to ensue. Specifically, we show how data mining can realize the knowledge spiral by effecting the four key transformations identified by Nonaka of turning: (1) existing explicit knowledge to new explicit knowledge, (2) existing explicit knowledge to new tacit knowledge, (3) existing tacit knowledge to new explicit knowledge and (4) existing tacit knowledge to new tacit knowledge. This is done through the establishment of theoretical models that respectively identify the function of the knowledge spiral and the powers of data mining, both exploratory and predictive, in the knowledge discovery process. Our models are then applied to a healthcare data set to demonstrate the potential of this approach as well as the implications of such an approach to the clinical and administrative aspects of healthcare. Further, we demonstrate how these techniques can facilitate hospitals to address the six healthcare quality dimensions identified by the Committee for Quality Healthcare.

Locating and defining underground goaf caused by coal mining from space-borne SAR interferometry

NASA Astrophysics Data System (ADS)

Yang, Zefa; Li, Zhiwei; Zhu, Jianjun; Yi, Huiwei; Feng, Guangcai; Hu, Jun; Wu, Lixin; Preusse, Alex; Wang, Yunjia; Papst, Markus

2018-01-01

It is crucial to locate underground goafs (i.e., mined-out areas) resulting from coal mining and define their spatial dimensions for effectively controlling the induced damages and geohazards. Traditional geophysical techniques for locating and defining underground goafs, however, are ground-based, labour-consuming and costly. This paper presents a novel space-based method for locating and defining the underground goaf caused by coal extraction using Interferometric Synthetic Aperture Radar (InSAR) techniques. As the coal mining-induced goaf is often a cuboid-shaped void and eight critical geometric parameters (i.e., length, width, height, inclined angle, azimuth angle, mining depth, and two central geodetic coordinates) are capable of locating and defining this underground space, the proposed method reduces to determine the eight geometric parameters from InSAR observations. Therefore, it first applies the Probability Integral Method (PIM), a widely used model for mining-induced deformation prediction, to construct a functional relationship between the eight geometric parameters and the InSAR-derived surface deformation. Next, the method estimates these geometric parameters from the InSAR-derived deformation observations using a hybrid simulated annealing and genetic algorithm. Finally, the proposed method was tested with both simulated and two real data sets. The results demonstrate that the estimated geometric parameters of the goafs are accurate and compatible overall, with averaged relative errors of approximately 2.1% and 8.1% being observed for the simulated and the real data experiments, respectively. Owing to the advantages of the InSAR observations, the proposed method provides a non-contact, convenient and practical method for economically locating and defining underground goafs in a large spatial area from space.

Use of a remotely piloted aircraft system for hazard assessment in a rocky mining area (Lucca, Italy)

NASA Astrophysics Data System (ADS)

Salvini, Riccardo; Mastrorocco, Giovanni; Esposito, Giuseppe; Di Bartolo, Silvia; Coggan, John; Vanneschi, Claudio

2018-01-01

The use of remote sensing techniques is now common practice in different working environments, including engineering geology. Moreover, in recent years the development of structure from motion (SfM) methods, together with rapid technological improvement, has allowed the widespread use of cost-effective remotely piloted aircraft systems (RPAS) for acquiring detailed and accurate geometrical information even in evolving environments, such as mining contexts. Indeed, the acquisition of remotely sensed data from hazardous areas provides accurate 3-D models and high-resolution orthophotos minimizing the risk for operators. The quality and quantity of the data obtainable from RPAS surveys can then be used for inspection of mining areas, audit of mining design, rock mass characterizations, stability analysis investigations and monitoring activities. Despite the widespread use of RPAS, its potential and limitations still have to be fully understood.In this paper a case study is shown where a RPAS was used for the engineering geological investigation of a closed marble mine area in Italy; direct ground-based techniques could not be applied for safety reasons. In view of the re-activation of mining operations, high-resolution images taken from different positions and heights were acquired and processed using SfM techniques to obtain an accurate and detailed 3-D model of the area. The geometrical and radiometrical information was subsequently used for a deterministic rock mass characterization, which led to the identification of two large marble blocks that pose a potential significant hazard issue for the future workforce. A preliminary stability analysis, with a focus on investigating the contribution of potential rock bridges, was then performed in order to demonstrate the potential use of RPAS information in engineering geological contexts for geohazard identification, awareness and reduction.

Monitoring of Surface Subsidence of the Mining Area Based on Sbas

NASA Astrophysics Data System (ADS)

Zhu, Y.; Zhou, S.; Zang, D.; Lu, T.

2018-05-01

This paper has collected 7 scenes of L band PALSAR sensor radar data of a mine in FengCheng city, jiangxi province, using the Small-baseline Subset (SBAS) method to invert the surface subsidence of the mine. Baselines of interference less than 800m has been chosen to constitute short baseline differential interference atlas, using pixels whose average coherent coefficient was larger than or equal to 0.3 as like high coherent point target, using singular value decomposition (SVD) method to calculate deformation phase sequence based on these high coherent points, and the accumulation of settlements of study area of different period had been obtained, so as to reflect the ground surface settlement evolution of the settlement of the area. The results of the study has showed that: SBAS technology has overcome coherent problem of the traditionality D-InSAR technique, continuous deformation field of surface mining in time dimension of time could been obtained, characteristics of ground surface settlement of mining subsidence in different period has been displayed, so to improve the accuracy and reliability of the monitoring results.

A data mining based approach to predict spatiotemporal changes in satellite images

NASA Astrophysics Data System (ADS)

Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Ghézala, H. Ben

2011-06-01

The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

Application of Foam-gel Technique to Control CO Exposure Generated During Spontaneous Combustion of Coal in Coal Mines.

PubMed

Ren, Xing W; Wang, Feng Z; Guo, Qing; Zuo, Zhao B; Fang, Qi S

2015-01-01

In China, 47.3% of state-owned coal mines are located in coal seams that are prone to spontaneous combustion. The spontaneous combustion of coal is the main cause of the generation of a large amount of carbon monoxide, which can cause serious health issues to miners. A new technique using foam-gel formation was developed to effectively control the spontaneous combustion of coal. The gel can capture more than 90% of the water in the grout and at the same time the foam can cover dangerous areas in the goaf by stacking and cooling of foam in all directions. In this study, a mechanism of foam-gel formation was introduced and the optimal proportions of additives were defined based on experiments of different foaming properties, gelling time and water loss rate as the main index parameters. The results of a field application in a coal mine promise that this new technique would effectively prevent coal oxidation in the goaf and reduce the generation of carbon monoxide.

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for internal climate studies in data mining and knowledge discovery techniques are used. However, it is necessary to use the KDD Approach and DM techniques in the climatic studies, specific interpreter of climate modeling result.

Metrics and Science Monograph Collections at the Marston Science Library, University of Florida

ERIC Educational Resources Information Center

Leonard, Michelle F.; Haas, Stephanie C.; Kisling, Vernon N.

2010-01-01

As academic libraries are increasingly supported by a matrix of database functions, the use of data mining and visualization techniques offer significant potential for future collection development and service initiatives based on quantifiable data. While data collection techniques are still not standardized and results may be skewed because of…

Evaluation of Aster Images for Characterization and Mapping of Amethyst Mining Residues

NASA Astrophysics Data System (ADS)

Markoski, P. R.; Rolim, S. B. A.

2012-07-01

The objective of this work was to evaluate the potential of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), subsystems VNIR (Visible and Near Infrared) and SWIR (Short Wave Infrared) images, for discrimination and mapping of amethyst mining residues (basalt) in the Ametista do Sul Region, Rio Grande do Sul State, Brazil. This region provides the most part of amethyst mining of the World. The basalt is extracted during the mining process and deposited outside the mine. As a result, mounts of residues (basalt) rise up. These mounts are many times smaller than ASTER pixel size (VNIR - 15 meters and SWIR - 30 meters). Thus, the pixel composition becomes a mixing of various materials, hampering its identification and mapping. Trying to solve this problem, multispectral algorithm Maximum Likelihood (MaxVer) and the hyperspectral technique SAM (Spectral Angle Mapper) were used in this work. Images from ASTER subsystems VNIR and SWIR were used to perform the classifications. SAM technique produced better results than MaxVer algorithm. The main error found by the techniques was the mixing between "shadow" and "mining residues/basalt" classes. With the SAM technique the confusion decreased because it employed the basalt spectral curve as a reference, while the multispectral techniques employed pixels groups that could have spectral mixture with other targets. The results showed that in tropical terrains as the study area, ASTER data can be efficacious for the characterization of mining residues.

A fuzzy hill-climbing algorithm for the development of a compact associative classifier

NASA Astrophysics Data System (ADS)

Mitra, Soumyaroop; Lam, Sarah S.

2012-02-01

Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

Object-based image analysis for the assessment of mineral extraction in conflict regions - a case study in the Democratic Republic of the Congo

NASA Astrophysics Data System (ADS)

Kranz, Olaf; Schoepfer, Elisabeth; Spröhnle, Kristin; Lang, Stefan

2016-06-01

In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. Object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.

Privacy Preserving Nearest Neighbor Search

NASA Astrophysics Data System (ADS)

Shaneck, Mark; Kim, Yongdae; Kumar, Vipin

Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. In this chapter we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data. We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification. We prove the security of these algorithms under the semi-honest adversarial model, and describe methods that can be used to optimize their performance. Keywords: Privacy Preserving Data Mining, Nearest Neighbor Search, Outlier Detection, Clustering, Classification, Secure Multiparty Computation

76 FR 51274 - Supplemental Nutrition Assistance Program: Major System Failures

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

... data mining as necessary to determine if losses are occurring in the process of issuing benefits. It is... further by using data mining techniques on States' data or analyzing QC data for error patterns that may... conjunction with an additional sample of cases. Data mining techniques may be employed when QC data cannot...

Assessment of practicality of remote sensing techniques for a study of the effects of strip mining in Alabama

NASA Technical Reports Server (NTRS)

Hughes, T. H.; Dillion, A. C., III; White, J. R., Jr.; Drummond, S. E., Jr.; Hooks, W. G.

1975-01-01

Because of the volume of coal produced by strip mining, the proximity of mining operations, and the diversity of mining methods (e.g. contour stripping, area stripping, multiple seam stripping, and augering, as well as underground mining), the Warrior Coal Basin seemed best suited for initial studies on the physical impact of strip mining in Alabama. Two test sites, (Cordova and Searles) representative of the various strip mining techniques and environmental problems, were chosen for intensive studies of the correlation between remote sensing and ground truth data. Efforts were eventually concentrated in the Searles Area, since it is more accessible and offers a better opportunity for study of erosional and depositional processes than the Cordova Area.

Coal Thickness Gauging Using Elastic Waves

NASA Technical Reports Server (NTRS)

Nazarian, Soheil; Bar-Cohen, Yoseph

1999-01-01

The efforts of a mining crew can be optimized, if the thickness of the coal layers to be excavated is known before excavation. Wave propagation techniques can be used to estimate the thickness of the layer based on the contrast in the wave velocity between coal and rock beyond it. Another advantage of repeated wave measurement is that the state of the stress within the mine can be estimated. The state of the stress can be used in many safety-related decisions made during the operation of the mine. Given these two advantages, a study was carried out to determine the feasibility of the methodology. The results are presented herein.

Genome engineering for microbial natural product discovery.

PubMed

Choi, Si-Sun; Katsuyama, Yohei; Bai, Linquan; Deng, Zixin; Ohnishi, Yasuo; Kim, Eung-Soo

2018-03-03

The discovery and development of microbial natural products (MNPs) have played pivotal roles in the fields of human medicine and its related biotechnology sectors over the past several decades. The post-genomic era has witnessed the development of microbial genome mining approaches to isolate previously unsuspected MNP biosynthetic gene clusters (BGCs) hidden in the genome, followed by various BGC awakening techniques to visualize compound production. Additional microbial genome engineering techniques have allowed higher MNP production titers, which could complement a traditional culture-based MNP chasing approach. Here, we describe recent developments in the MNP research paradigm, including microbial genome mining, NP BGC activation, and NP overproducing cell factory design. Copyright © 2018 Elsevier Ltd. All rights reserved.

Research on preventive technologies for bed-separation water hazard in China coal mines

NASA Astrophysics Data System (ADS)

Gui, Herong; Tong, Shijie; Qiu, Weizhong; Lin, Manli

2018-03-01

Bed-separation water is one of the major water hazards in coal mines. Targeted researches on the preventive technologies are of paramount importance to safe mining. This article studied the restrictive effect of geological and mining factors, such as lithological properties of roof strata, coal seam inclination, water source to bed separations, roof management method, dimensions of mining working face, and mining progress, on the formation of bed-separation water hazard. The key techniques to prevent bed-separation water-related accidents include interception, diversion, destructing the buffer layer, grouting and backfilling, etc. The operation and efficiency of each technique are corroborated in field engineering cases. The results of this study will offer reference to countries with similar mining conditions in the researches on bed-separation water burst and hazard control in coal mines.

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

PubMed

Renganathan, Vinaitheerthan

2017-07-01

With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.

Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms.

PubMed

Lin, Chun-Wei; Zhang, Binbin; Yang, Kuo-Tung; Hong, Tzung-Pei

2014-01-01

Data mining is used to mine meaningful and useful information or knowledge from a very large database. Some secure or private information can be discovered by data mining techniques, thus resulting in an inherent risk of threats to privacy. Privacy-preserving data mining (PPDM) has thus arisen in recent years to sanitize the original database for hiding sensitive information, which can be concerned as an NP-hard problem in sanitization process. In this paper, a compact prelarge GA-based (cpGA2DT) algorithm to delete transactions for hiding sensitive itemsets is thus proposed. It solves the limitations of the evolutionary process by adopting both the compact GA-based (cGA) mechanism and the prelarge concept. A flexible fitness function with three adjustable weights is thus designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets with minimal side effects of hiding failure, missing cost, and artificial cost. Experiments are conducted to show the performance of the proposed cpGA2DT algorithm compared to the simple GA-based (sGA2DT) algorithm and the greedy approach in terms of execution time and three side effects.

Survey of Natural Language Processing Techniques in Bioinformatics.

PubMed

Zeng, Zhiqiang; Shi, Hua; Wu, Yun; Hong, Zhiling

2015-01-01

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

Factoring uncertainty into restoration modeling of in-situ leach uranium mines

USGS Publications Warehouse

Johnson, Raymond H.; Friedel, Michael J.

2009-01-01

Postmining restoration is one of the greatest concerns for uranium in-situ leach (ISL) mining operations. The ISL-affected aquifer needs to be returned to conditions specified in the mining permit (either premining or other specified conditions). When uranium ISL operations are completed, postmining restoration is usually achieved by injecting reducing agents into the mined zone. The objective of this process is to restore the aquifer to premining conditions by reducing the solubility of uranium and other metals in the ground water. Reactive transport modeling is a potentially useful method for simulating the effectiveness of proposed restoration techniques. While reactive transport models can be useful, they are a simplification of reality that introduces uncertainty through the model conceptualization, parameterization, and calibration processes. For this reason, quantifying the uncertainty in simulated temporal and spatial hydrogeochemistry is important for postremedial risk evaluation of metal concentrations and mobility. Quantifying the range of uncertainty in key predictions (such as uranium concentrations at a specific location) can be achieved using forward Monte Carlo or other inverse modeling techniques (trial-and-error parameter sensitivity, calibration constrained Monte Carlo). These techniques provide simulated values of metal concentrations at specified locations that can be presented as nonlinear uncertainty limits or probability density functions. Decisionmakers can use these results to better evaluate environmental risk as future metal concentrations with a limited range of possibilities, based on a scientific evaluation of uncertainty.

Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.

PubMed

Ji, Yanqing; Ying, Hao; Tran, John; Dews, Peter; Massanari, R Michael

2016-07-19

Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback. The system employed association mining techniques to build a k-profile representing a user's relevance feedback. More specifically, we developed a weighted interest measure and an association mining algorithm to find the strength of association between a query and each concept in the article(s) selected by the user as feedback. The top concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity. A prototype of BiomedSearch software was made and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial experiment results indicated that BiomedSearch increased the mean average precision (MAP) for a set of queries. With UMLS and association mining techniques, BiomedSearch can effectively utilize users' relevance feedback to improve the performance of biomedical literature search.

Modelling Subjectivity in Visual Perception of Orientation for Image Retrieval.

ERIC Educational Resources Information Center

Sanchez, D.; Chamorro-Martinez, J.; Vila, M. A.

2003-01-01

Discussion of multimedia libraries and the need for storage, indexing, and retrieval techniques focuses on the combination of computer vision and data mining techniques to model high-level concepts for image retrieval based on perceptual features of the human visual system. Uses fuzzy set theory to measure users' assessments and to capture users'…

Quantitative analysis and feature recognition in 3-D microstructural data sets

NASA Astrophysics Data System (ADS)

Lewis, A. C.; Suh, C.; Stukowski, M.; Geltmacher, A. B.; Spanos, G.; Rajan, K.

2006-12-01

A three-dimensional (3-D) reconstruction of an austenitic stainless-steel microstructure was used as input for an image-based finite-element model to simulate the anisotropic elastic mechanical response of the microstructure. The quantitative data-mining and data-warehousing techniques used to correlate regions of high stress with critical microstructural features are discussed. Initial analysis of elastic stresses near grain boundaries due to mechanical loading revealed low overall correlation with their location in the microstructure. However, the use of data-mining and feature-tracking techniques to identify high-stress outliers revealed that many of these high-stress points are generated near grain boundaries and grain edges (triple junctions). These techniques also allowed for the differentiation between high stresses due to boundary conditions of the finite volume reconstructed, and those due to 3-D microstructural features.

Rules of meridians and acupoints selection in treatment of Parkinson's disease based on data mining techniques.

PubMed

Li, Zhe; Hu, Ying-Yu; Zheng, Chun-Ye; Su, Qiao-Zhen; An, Chang; Luo, Xiao-Dong; Liu, Mao-Cai

2018-01-15

To help selecting appropriate meridians and acupoints in clinical practice and experimental study for Parkinson's disease (PD), the rules of meridians and acupoints selection of acupuncture and moxibustion were analyzed in domestic and foreign clinical treatment for PD based on data mining techniques. Literature about PD treated by acupuncture and moxibustion in China and abroad was searched and selected from China National Knowledge Infrastructure and MEDLINE. Then the data from all eligible articles were extracted to establish the database of acupuncture-moxibustion for PD. The association rules of data mining techniques were used to analyze the rules of meridians and acupoints selection. Totally, 168 eligible articles were included and 184 acupoints were applied. The total frequency of acupoints application was 1,090 times. Those acupoints were mainly distributed in head and neck and extremities. Among all, Taichong (LR 3), Baihui (DU 20), Fengchi (GB 20), Hegu (LI 4) and Chorea-tremor Controlled Zone were the top five acupoints that had been used. Superior-inferior acupoints matching was utilized the most. As to involved meridians, Du Meridian, Dan (Gallbladder) Meridian, Dachang (Large Intestine) Meridian, and Gan (Liver) Meridian were the most popular meridians. The application of meridians and acupoints for PD treatment lay emphasis on the acupoints on the head, attach importance to extinguishing Gan wind, tonifying qi and blood, and nourishing sinews, and make good use of superior-inferior acupoints matching.

Comparing digital data processing techniques for surface mine and reclamation monitoring

NASA Technical Reports Server (NTRS)

Witt, R. G.; Bly, B. G.; Campbell, W. J.; Bloemer, H. H. L.; Brumfield, J. O.

1982-01-01

The results of three techniques used for processing Landsat digital data are compared for their utility in delineating areas of surface mining and subsequent reclamation. An unsupervised clustering algorithm (ISOCLS), a maximum-likelihood classifier (CLASFY), and a hybrid approach utilizing canonical analysis (ISOCLS/KLTRANS/ISOCLS) were compared by means of a detailed accuracy assessment with aerial photography at NASA's Goddard Space Flight Center. Results show that the hybrid approach was superior to the traditional techniques in distinguishing strip mined and reclaimed areas.

Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other.

PubMed

Tahmasebian, Shahram; Ghazisaeedi, Marjan; Langarizadeh, Mostafa; Mokhtaran, Mehrshad; Mahdavi-Mazdeh, Mitra; Javadian, Parisa

2017-01-01

Introduction: Chronic kidney disease (CKD) includes a wide range of pathophysiological processes which will be observed along with abnormal function of kidneys and progressive decrease in glomerular filtration rate (GFR). According to the definition decreasing GFR must have been present for at least three months. CKD will eventually result in end-stage kidney disease. In this process different factors play role and finding the relations between effective parameters in this regard can help to prevent or slow progression of this disease. There are always a lot of data being collected from the patients' medical records. This huge array of data can be considered a valuable source for analyzing, exploring and discovering information. Objectives: Using the data mining techniques, the present study tries to specify the effective parameters and also aims to determine their relations with each other in Iranian patients with CKD. Material and Methods: The study population includes 31996 patients with CKD. First, all of the data is registered in the database. Then data mining tools were used to find the hidden rules and relationships between parameters in collected data. Results: After data cleaning based on CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and running mining algorithms on the data in the database the relationships between the effective parameters was specified. Conclusion: This study was done using the data mining method pertaining to the effective factors on patients with CKD.

Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other

PubMed Central

Tahmasebian, Shahram; Ghazisaeedi, Marjan; Langarizadeh, Mostafa; Mokhtaran, Mehrshad; Mahdavi-Mazdeh, Mitra; Javadian, Parisa

2017-01-01

Introduction: Chronic kidney disease (CKD) includes a wide range of pathophysiological processes which will be observed along with abnormal function of kidneys and progressive decrease in glomerular filtration rate (GFR). According to the definition decreasing GFR must have been present for at least three months. CKD will eventually result in end-stage kidney disease. In this process different factors play role and finding the relations between effective parameters in this regard can help to prevent or slow progression of this disease. There are always a lot of data being collected from the patients’ medical records. This huge array of data can be considered a valuable source for analyzing, exploring and discovering information. Objectives: Using the data mining techniques, the present study tries to specify the effective parameters and also aims to determine their relations with each other in Iranian patients with CKD. Material and Methods: The study population includes 31996 patients with CKD. First, all of the data is registered in the database. Then data mining tools were used to find the hidden rules and relationships between parameters in collected data. Results: After data cleaning based on CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and running mining algorithms on the data in the database the relationships between the effective parameters was specified. Conclusion: This study was done using the data mining method pertaining to the effective factors on patients with CKD. PMID:28497080

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chironis, N.P.

This book contains a wealth of valuable information carefully selected and compiled from recent issues of Coal Age magazine. Much of the source material has been gathered by Coal Age Editors during their visits to coal mines, research establishments, universities and technical symposiums. Equally important are the articles and data contributed by over 50 top experts, many of whom are well known to the mining industry. Specifically, this easy-to-use handbook is divided into eleven key areas of underground mining. Here you will find the latest information on continuous mining techniques, longwall and shortwall methods and equipment, specialized mining and boringmore » systems, continuous haulage techniques, improved roof control and ventilation methods, mine communications and instrumentation, power systems, fire control methods, and new mining regulations. There is also a section on engineering and management considerations, including the modern use of computer terminals, practical techniques for picking leaders and for encouraging more safety consciousness in employees, factors affecting absenteeism, and some highly important financial considerations. All of this valuable information has been thoroughly indexed to provide immediate access to the specific data needed by the reader.« less

Data Mining in Child Welfare.

ERIC Educational Resources Information Center

Schoech, Dick; Quinn, Andrew; Rycraft, Joan R.

2000-01-01

Examines the historical and larger context of data mining and describes data mining processes, techniques, and tools. Illustrates these using a child welfare dataset concerning the employee turnover that is mined, using logistic regression and a Bayesian neural network. Discusses the data mining process, the resulting models, their predictive…

Text Mining in Biomedical Domain with Emphasis on Document Clustering

PubMed Central

2017-01-01

Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. PMID:28875048

Synchrotron-based X-Ray Spectroscopy Studies for Redox-based Remediation of Lead, Zinc, and Cadmium in Mine Waste Materials.

PubMed

Karna, Ranju R; Hettiarachchi, Ganga M; Newville, Matthew; Sun, ChengJun; Ma, Qing

2016-11-01

Several studies have examined the effect of submergence on the mobility of metals present in mine waste materials. This study examines the effect of organic carbon (OC) and sulfur (S) additions and submergence time on redox-induced biogeochemical transformations of lead (Pb), zinc (Zn), and cadmium (Cd) present in mine waste materials collected from the Tri-State mining district located in southeastern Kansas, southwestern Missouri, and northeastern Oklahoma. A completely randomized design, with a two-way treatment structure, was used for conducting a series of column experiments. Two replicates were used for each treatment combination. Effluent samples were collected at several time points, and soil samples were collected at the end of each column experiment. Because these samples are highly heterogeneous, we used a variety of synchrotron-based techniques to identify Pb, Zn, and Cd speciation at both micro- and bulk-scale. Spectroscopic analysis results from the study revealed that the addition of OC, with and without S, promoted metal-sulfide formation, whereas metal carbonates dominated in the nonamended flooded materials and in mine waste materials only amended with S. Therefore, the synergistic effect of OC and S may be more promising for managing mine waste materials disposed of in flooded subsidence mine pits instead of individual S or OC treatments. The mechanistic understanding gained in this study is also relevant for remediation of waste materials using natural or constructed wetland systems. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Coal face measurement system for underground use

NASA Technical Reports Server (NTRS)

1981-01-01

A measurement system was developed for the Eickhoff longwall shearer to determine the contour of the coal face as it mines coal. Contour data are obtained by an indirect measurement technique based on evaluating the motion of the shearer during mining. Starting from a known location, points along the coal face are established through a knowledge of the machines' positions and yaw movements as it moves past the coal face. The hardware and system operation procedures are described. The tests of system performance and their results are reported.

Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval

ERIC Educational Resources Information Center

Khribi, Mohamed Koutheair; Jemni, Mohamed; Nasraoui, Olfa

2009-01-01

In this paper, we describe an automatic personalization approach aiming to provide online automatic recommendations for active learners without requiring their explicit feedback. Recommended learning resources are computed based on the current learner's recent navigation history, as well as exploiting similarities and dissimilarities among…

The Mine Locomotive Wireless Network Strategy Based on Successive Interference Cancellation

PubMed Central

Wu, Liaoyuan; Han, Jianghong; Wei, Xing; Shi, Lei; Ding, Xu

2015-01-01

We consider a wireless network strategy based on successive interference cancellation (SIC) for mine locomotives. We firstly build the original mathematical model for the strategy which is a non-convex model. Then, we examine this model intensively, and figure out that there are certain regulations embedded in it. Based on these findings, we are able to reformulate the model into a new form and design a simple algorithm which can assign each locomotive with a proper transmitting scheme during the whole schedule procedure. Simulation results show that the outcomes obtained through this algorithm are improved by around 50% compared with those that do not apply the SIC technique. PMID:26569240

Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology.

PubMed

Oztekin, Asil; Delen, Dursun; Kong, Zhenyu James

2009-12-01

Predicting the survival of heart-lung transplant patients has the potential to play a critical role in understanding and improving the matching procedure between the recipient and graft. Although voluminous data related to the transplantation procedures is being collected and stored, only a small subset of the predictive factors has been used in modeling heart-lung transplantation outcomes. The previous studies have mainly focused on applying statistical techniques to a small set of factors selected by the domain-experts in order to reveal the simple linear relationships between the factors and survival. The collection of methods known as 'data mining' offers significant advantages over conventional statistical techniques in dealing with the latter's limitations such as normality assumption of observations, independence of observations from each other, and linearity of the relationship between the observations and the output measure(s). There are statistical methods that overcome these limitations. Yet, they are computationally more expensive and do not provide fast and flexible solutions as do data mining techniques in large datasets. The main objective of this study is to improve the prediction of outcomes following combined heart-lung transplantation by proposing an integrated data-mining methodology. A large and feature-rich dataset (16,604 cases with 283 variables) is used to (1) develop machine learning based predictive models and (2) extract the most important predictive factors. Then, using three different variable selection methods, namely, (i) machine learning methods driven variables-using decision trees, neural networks, logistic regression, (ii) the literature review-based expert-defined variables, and (iii) common sense-based interaction variables, a consolidated set of factors is generated and used to develop Cox regression models for heart-lung graft survival. The predictive models' performance in terms of 10-fold cross-validation accuracy rates for two multi-imputed datasets ranged from 79% to 86% for neural networks, from 78% to 86% for logistic regression, and from 71% to 79% for decision trees. The results indicate that the proposed integrated data mining methodology using Cox hazard models better predicted the graft survival with different variables than the conventional approaches commonly used in the literature. This result is validated by the comparison of the corresponding Gains charts for our proposed methodology and the literature review based Cox results, and by the comparison of Akaike information criteria (AIC) values received from each. Data mining-based methodology proposed in this study reveals that there are undiscovered relationships (i.e. interactions of the existing variables) among the survival-related variables, which helps better predict the survival of the heart-lung transplants. It also brings a different set of variables into the scene to be evaluated by the domain-experts and be considered prior to the organ transplantation.

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies

PubMed Central

Xia, Jingbo; Zhang, Xing; Yuan, Daojun; Chen, Lingling; Webster, Jonathan; Fang, Alex Chengyu

2013-01-01

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization. PMID:24371834

Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies.

PubMed

Xia, Jingbo; Zhang, Xing; Yuan, Daojun; Chen, Lingling; Webster, Jonathan; Fang, Alex Chengyu

2013-01-01

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.

The Hazards of Data Mining in Healthcare.

PubMed

Househ, Mowafa; Aldosari, Bakheet

2017-01-01

From the mid-1990s, data mining methods have been used to explore and find patterns and relationships in healthcare data. During the 1990s and early 2000's, data mining was a topic of great interest to healthcare researchers, as data mining showed some promise in the use of its predictive techniques to help model the healthcare system and improve the delivery of healthcare services. However, it was soon discovered that mining healthcare data had many challenges relating to the veracity of healthcare data and limitations around predictive modelling leading to failures of data mining projects. As the Big Data movement has gained momentum over the past few years, there has been a reemergence of interest in the use of data mining techniques and methods to analyze healthcare generated Big Data. Much has been written on the positive impacts of data mining on healthcare practice relating to issues of best practice, fraud detection, chronic disease management, and general healthcare decision making. Little has been written about the limitations and challenges of data mining use in healthcare. In this review paper, we explore some of the limitations and challenges in the use of data mining techniques in healthcare. Our results show that the limitations of data mining in healthcare include reliability of medical data, data sharing between healthcare organizations, inappropriate modelling leading to inaccurate predictions. We conclude that there are many pitfalls in the use of data mining in healthcare and more work is needed to show evidence of its utility in facilitating healthcare decision-making for healthcare providers, managers, and policy makers and more evidence is needed on data mining's overall impact on healthcare services and patient care.

CARIBIAM: constrained Association Rules using Interactive Biological IncrementAl Mining.

PubMed

Rahal, Imad; Rahhal, Riad; Wang, Baoying; Perrizo, William

2008-01-01

This paper analyses annotated genome data by applying a very central data-mining technique known as Association Rule Mining (ARM) with the aim of discovering rules and hypotheses capable of yielding deeper insights into this type of data. In the literature, ARM has been noted for producing an overwhelming number of rules. This work proposes a new technique capable of using domain knowledge in the form of queries in order to efficiently mine only the subset of the associations that are of interest to investigators in an incremental and interactive manner.

Technique for predicting ground-water discharge to surface coal mines and resulting changes in head

USGS Publications Warehouse

Weiss, L.S.; Galloway, D.L.; Ishii, Audrey L.

1986-01-01

Changes in seepage flux and head (groundwater level) from groundwater drainage into a surface coal mine can be predicted by a technique that considers drainage from the unsaturated zone. The user applies site-specific data to precalculated head and seepage-flux profiles. Groundwater flow through hypothetical aquifer cross sections was simulated using the U.S. Geological Survey finite-difference model, VS2D, which considers variably saturated two-dimensional flow. Conceptual models considered were (1) drainage to a first cut, and (2) drainage to multiple cuts, which includes drainage effects of an area surface mine. Dimensionless head and seepage flux profiles from 246 simulations are presented. Step-by-step instructions and examples are presented. Users are required to know aquifer characteristics and to estimate size and timing of the mine operation at a proposed site. Calculated groundwater drainage to the mine is from one excavated face only. First cut considers confined and unconfined aquifers of a wide range of permeabilities; multiple cuts considers unconfined aquifers of higher permeabilities only. The technique, developed for Illinois coal-mining regions that use area surface mining and evaluated with an actual field example, will be useful in assessing potential hydrologic impacts of mining. Application is limited to hydrogeologic settings and mine operations similar to those considered. Fracture flow, recharge, and leakage are nor considered. (USGS)

Broadband geophysical time series data from a stressed environment

NASA Astrophysics Data System (ADS)

Pun, W.; Saleh, R.; Zwaan, D.; Milkereit, B.; Valley, B.; Pilz, M.; Milkereit, C.; Milkereit, R.

2011-12-01

As classical exploration geophysical tools and techniques find new application in time lapse and monitoring studies, a fresh look at the performance and repeatability of various geophysical techniques is worth to take a closer look. We used an active, deep mine site close to Sudbury (Canada) for 3D deployment of broadband geophysical sensors for passive monitoring and detecting anomalous regions in the earth based on physical rock properties. In addition, we conducted controlled source experiments to evaluate repeatability of geophysical sources. To extend from detection to monitoring, continuous repeated measurements are necessary over a long period of time. If a controlled source is stable, the convolution problem is simplified such that any variation in the geophysical data is an effect of the earth's response. Repeated measurements are important for in-mine use to provide a better insight of stress and strain changes due to natural events and mining processes. The development, build-up and redistribution of stress lead to rock failures that can have disastrous consequences if they occur in an uncontrolled manner. In this project, different continuous and repeated in-situ geophysical measurements from a deep underground mine were analyzed to validate the feasibility of in-mine monitoring. Data acquisition tests covered both active and passive methods: gravity meter, fibre optic strain meters, fixed and portable three-component seismic arrays, EM induction coils and borehole based DC/IP resistivity sensors. The newly acquired data cover a wide range of frequencies which allow the study of short- and long-period events, ranging from 10-5 Hz to 10 kHz. Earth tides, global seismic events, tremors, acoustic emissions (microseismic events) and blasts were recorded within a 3D volume.

Soil Quality of Bauxite Mining Areas

NASA Astrophysics Data System (ADS)

Terezinha Gonçalves Bizuti, Denise; Dinarowski, Marcela; Casagrande, José Carlos; Silva, Luiz Gabriel; Soares, Marcio Roberto; Henrique Santin Brancalion, Pedro

2015-04-01

The study on soil quality index (SQI) aims to assess the current state of the soil after use and estimating its recovery through sustainable management practices This type of study is being used in this work in order to check the efficiency of forest recovery techniques in areas that have been deeply degraded by bauxite mining process, and compare them with the area of native forest, through the determination of SQI. Treatments were newly mined areas, areas undergoing restoration (topsoil use with planting of native forest species), areas in rehabilitation (employment of the green carpet with topsoil and planting of native forest species) and areas of native forests, with six repetitions, in areas of ALCOA, in the municipality of Poços de Caldas/MG. To this end, we used the additive pondered model, establishing three functions: Fertility, water movement and root development, based on chemical parameters (organic matter, base saturation, aluminum saturation and calcium content); physical (macroporosity, soil density and clay content); and microbiological testing (basal respiration by the emission of CO2 ). The SQIs obtained for each treatment was 41%, 56%, 63% and 71% for newly mined areas, native forest, areas in restoration and rehabilitation, respectively. The recovering technique that most approximates the degraded soil to the soil of reference is the restoration, where there was no statistically significant difference of areas restored with native forest. It was found that for the comparison of the studied areas must take into account the nutrient cycling, that disappear with plant removal in mining areas, once the soil of native forest features low fertility and high saturation by aluminum, also taking in account recovering time.

Analysis of Mining Terrain Deformation Characteristics with Deformation Information System

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Milczarek, Wojciech; Grzempowski, Piotr

2014-05-01

Mapping and prediction of mining related deformations of the earth surface is an important measure for minimising threat to surface infrastructure, human population, the environment and safety of the mining operation itself arising from underground extraction of useful minerals. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and increasing with the development of geographical information technologies. These include for example: terrestrial geodetic measurements, global positioning systems, remote sensing, spatial interpolation, finite element method modelling, GIS based modelling, geological modelling, empirical modelling using the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The aim of this paper is to introduce the concept of an integrated Deformation Information System (DIS) developed in geographic information systems environment for analysis and modelling of various spatial data related to mining activity and demonstrate its applications for mapping and visualising, as well as identifying possible mining terrain deformation areas with various spatial modelling methods. The DIS concept is based on connected modules that include: the spatial database - the core of the system, the spatial data collection module formed by: terrestrial, satellite and remote sensing measurements of the ground changes, the spatial data mining module for data discovery and extraction, the geological modelling module, the spatial data modeling module with data processing algorithms for spatio-temporal analysis and mapping of mining deformations and their characteristics (e.g. deformation parameters: tilt, curvature and horizontal strain), the multivariate spatial data classification module and the visualization module allowing two-dimensional interactive and static mapping and three-dimensional visualizations of mining ground characteristics. The Systems's functionality has been presented on the case study of a coal mining region in SW Poland where it has been applied to study characteristics and map mining induced ground deformations in a city in the last two decades of underground coal extraction and in the first decade after the end of mining. The mining subsidence area and its deformation parameters (tilt and curvature) have been calculated and the latter classified and mapped according to the Polish regulations. In addition possible areas of ground deformation have been indicated based on multivariate spatial data analysis of geological and mining operation characteristics with the geographically weighted regression method.

Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.

PubMed

Al-Saggaf, Yeslam; Islam, Md Zahidul

2015-08-01

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.

Mars Methane Analogue Mission (M3): Analytical Techniques and Operations

NASA Astrophysics Data System (ADS)

Cloutis, E.; Vrionis, H.; Qadi, A.; Bell, J. F.; Berard, G.; Boivin, A.; Ellery, A.; Jamroz, W.; Kruzelecky, R.; Mann, P.; Samson, C.; Stromberg, J.; Strong, K.; Tremblay, A.; Whyte, L.; Wing, B.

2011-03-01

The Mars Methane Analogue Mission (M3) project is designed to simulate a rover-based search for, and analysis of, methane sources on Mars at a serpentinite open pit mine in Quebec, using a variety of instruments.

An application of data mining in district heating substations for improving energy performance

NASA Astrophysics Data System (ADS)

Xue, Puning; Zhou, Zhigang; Chen, Xin; Liu, Jing

2017-11-01

Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.

Stand characteristics and productivity potential of Indiana surface mines reclaimed under SMCRA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groninger, J.W.; Fillmore, S.D.; Rathfon, R.A.

The Surface Mining Control and Reclamation Act of 1977 (SMCRA) addresses a wide range of environmental concerns. However, its impacts on forest stand development and productive potential have only recently been investigated. We surveyed the vegetation and forest productivity on 22 surface mine sites throughout the coal-bearing region of Indiana that were reclaimed to forest cover under the provisions of SMCRA 7-14 years prior to sampling. Black locust (Robinia pseudoacacia) and green ash (Fraxinus pennsylvanica) were the most widely occurring tree species. Tall fescue and goldenrod were the most widely occurring nonarborescent species. Median site index (base age 50 formore » black oak) was 30 ft. Although satisfying forest cover stocking requirements for bond release, these reclaimed surface mines almost always displayed a level of productivity far below those of native forests typical of this region. Reclamation techniques differing from those used on these study sites are needed to restore forest productivity to surface-mined lands while still complying with SMCRA.« less

Data Mining: Going beyond Traditional Statistics

ERIC Educational Resources Information Center

Zhao, Chun-Mei; Luan, Jing

2006-01-01

The authors provide an overview of data mining, giving special attention to the relationship between data mining and statistics to unravel some misunderstandings about the two techniques. (Contains 1 figure.)

Mine Water Treatment in Hongai Coal Mines

NASA Astrophysics Data System (ADS)

Dang, Phuong Thao; Dang, Vu Chi

2018-03-01

Acid mine drainage (AMD) is recognized as one of the most serious environmental problem associated with mining industry. Acid water, also known as acid mine drainage forms when iron sulfide minerals found in the rock of coal seams are exposed to oxidizing conditions in coal mining. Until 2009, mine drainage in Hongai coal mines was not treated, leading to harmful effects on humans, animals and aquatic ecosystem. This report has examined acid mine drainage problem and techniques for acid mine drainage treatment in Hongai coal mines. In addition, selection and criteria for the design of the treatment systems have been presented.

Spatial decision support system for tobacco enterprise based on spatial data mining

NASA Astrophysics Data System (ADS)

Mei, Xin; Liu, Junyi; Zhang, Xuexia; Cui, Weihong

2007-11-01

Tobacco enterprise is a special enterprise, which has strong correlation to regional geography. But in the past research and application, the combination between tobacco and GIS is limited to use digital maps to assist cigarette distribution. How to comprehensively import 3S technique and spatial data mining (SDM) to construct spatial decision support system (SDSS) of tobacco enterprise is the main research aspect in this paper. The paper concretely analyzes the GIS requirements in tobacco enterprise for planning location of production, monitoring production management and product sale at the beginning. Then holistic solution is presented and frame design for tobacco enterprise spatial decision based on SDM is given. This paper describes how to use spatial analysis and data mining to realize the spatial decision processing such as monitoring tobacco planted acreage, analyzing and planning the cigarette sale network and so on.

New approach to generating insights for aging research based on literature mining and knowledge integration

PubMed Central

Kwon, Yeondae; Natori, Yukikazu

2017-01-01

The proportion of the elderly population in most countries worldwide is increasing dramatically. Therefore, social interest in the fields of health, longevity, and anti-aging has been increasing as well. However, the basic research results obtained from a reductionist approach in biology and a bioinformatic approach in genome science have limited usefulness for generating insights on future health, longevity, and anti-aging-related research on a case by case basis. We propose a new approach that uses our literature mining technique and bioinformatics, which lead to a better perspective on research trends by providing an expanded knowledge base to work from. We demonstrate that our approach provides useful information that deepens insights on future trends which differs from data obtained conventionally, and this methodology is already paving the way for a new field in aging-related research based on literature mining. One compelling example of this is how our new approach can be a useful tool in drug repositioning. PMID:28817730

Mobile Formative Assessment Tool Based on Data Mining Techniques for Supporting Web-Based Learning

ERIC Educational Resources Information Center

Chen, Chih-Ming; Chen, Ming-Chuan

2009-01-01

Current trends clearly indicate that online learning has become an important learning mode. However, no effective assessment mechanism for learning performance yet exists for e-learning systems. Learning performance assessment aims to evaluate what learners learned during the learning process. Traditional summative evaluation only considers final…

Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining

PubMed Central

Sadesh, S.; Suganthe, R. C.

2015-01-01

Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP) framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches) regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio. PMID:26221626

In-situ identification of anti-personnel mines using acoustic resonant spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perry, R L; Roberts, R S

1999-02-01

A new technique for identifying buried Anti-Personnel Mines is described, and a set of preliminary experiments designed to assess the feasibility of this technique is presented. Analysis of the experimental results indicates that the technique has potential, but additional work is required to bring the technique to fruition. In addition to the experimental results presented here, a technique used to characterize the sensor employed in the experiments is detailed.

Responses of Terrestrial Herpetofauna to Persistent, Novel Ecosystems Resulting from Mountaintop Removal Mining

Treesearch

Jennifer M. Williams; Donald J. Brown; Petra B. Wood

2017-01-01

Mountaintop removal mining is a large-scale surface mining technique that removes entire floral and faunal communities, along with soil horizons located above coal seams. In West Virginia, the majority of this mining occurs on forested mountaintops. However, after mining ceases the land is typically reclaimed to grasslands and shrublands, resulting in novel ecosystems...

Using Open Web APIs in Teaching Web Mining

ERIC Educational Resources Information Center

Chen, Hsinchun; Li, Xin; Chau, M.; Ho, Yi-Jen; Tseng, Chunju

2009-01-01

With the advent of the World Wide Web, many business applications that utilize data mining and text mining techniques to extract useful business information on the Web have evolved from Web searching to Web mining. It is important for students to acquire knowledge and hands-on experience in Web mining during their education in information systems…

Evaluating uses of data mining techniques in propensity score estimation: a simulation study.

PubMed

Setoguchi, Soko; Schneeweiss, Sebastian; Brookhart, M Alan; Glynn, Robert J; Cook, E Francis

2008-06-01

In propensity score modeling, it is a standard practice to optimize the prediction of exposure status based on the covariate information. In a simulation study, we examined in what situations analyses based on various types of exposure propensity score (EPS) models using data mining techniques such as recursive partitioning (RP) and neural networks (NN) produce unbiased and/or efficient results. We simulated data for a hypothetical cohort study (n = 2000) with a binary exposure/outcome and 10 binary/continuous covariates with seven scenarios differing by non-linear and/or non-additive associations between exposure and covariates. EPS models used logistic regression (LR) (all possible main effects), RP1 (without pruning), RP2 (with pruning), and NN. We calculated c-statistics (C), standard errors (SE), and bias of exposure-effect estimates from outcome models for the PS-matched dataset. Data mining techniques yielded higher C than LR (mean: NN, 0.86; RPI, 0.79; RP2, 0.72; and LR, 0.76). SE tended to be greater in models with higher C. Overall bias was small for each strategy, although NN estimates tended to be the least biased. C was not correlated with the magnitude of bias (correlation coefficient [COR] = -0.3, p = 0.1) but increased SE (COR = 0.7, p < 0.001). Effect estimates from EPS models by simple LR were generally robust. NN models generally provided the least numerically biased estimates. C was not associated with the magnitude of bias but was with the increased SE.

The Geomatics Contribution for the Valorisation Project in the Rocca of San Silvestro Landscape Site

NASA Astrophysics Data System (ADS)

Brocchini, D.; Chiabrando, F.; Colucci, E.; Sammartano, G.; Spanò, A.; Teppati Losè, L.; Villa, A.

2017-05-01

This paper proposes an emblematic project where several multi-sensor strategies for spatial data acquisition and management, range based and image based, were combined to create a series of integrated territorial and architectural scale products characterized by a rich multi-content nature. The work presented here was finalized in a test site that is composed by an ensemble of diversified cultural deposits; the objects that were surveyed and modelled range from the landscape with its widespread mining sites, the main tower with its defensive role, the urban configuration of the settlement, the building systems and techniques, a medieval mine. For this reason, the Rocca of San Silvestro represented a perfect test case, due to its complex and multi-stratified character. This archaeological site is a medieval fortified village near the municipality of Campiglia Marittima (LI), Italy. The Rocca is part of an Archaeological Mines Park and is included in the Parchi della Val di Cornia (a system of archaeological parks, natural parks and museums in the south-west of Tuscany). The fundamental role of a deep knowledge about a cultural artefact before the planning of a restoration and valorisation project is globally recognized; the qualitative and quantitative knowledge provided by geomatics techniques is part of this process. The paper will present the different techniques that were used, the products that were obtained and will focus on some mapping and WEB GIS applications and analyses that were performed and considerations that were made.

Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

Vathsala, H.; Koolagudi, Shashidhar G.

2017-01-01

In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good.

Digital Family History Data Mining with Neural Networks: A Pilot Study.

PubMed

Hoyt, Robert; Linnville, Steven; Thaler, Stephen; Moore, Jeffrey

2016-01-01

Following the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, electronic health records were widely adopted by eligible physicians and hospitals in the United States. Stage 2 meaningful use menu objectives include a digital family history but no stipulation as to how that information should be used. A variety of data mining techniques now exist for these data, which include artificial neural networks (ANNs) for supervised or unsupervised machine learning. In this pilot study, we applied an ANN-based simulation to a previously reported digital family history to mine the database for trends. A graphical user interface was created to display the input of multiple conditions in the parents and output as the likelihood of diabetes, hypertension, and coronary artery disease in male and female offspring. The results of this pilot study show promise in using ANNs to data mine digital family histories for clinical and research purposes.

Studies of short and long memory in mining-induced seismic processes

NASA Astrophysics Data System (ADS)

Węglarczyk, Stanisław; Lasocki, Stanisław

2009-09-01

Memory of a stochastic process implies its predictability, understood as a possibility to gain information on the future above the random guess level. Here we search for memory in the mining-induced seismic process (MIS), that is, a process induced or triggered by mining operations. Long memory is investigated by means of the Hurst rescaled range analysis, and the autocorrelation function estimate is used to test for short memory. Both methods are complemented with result uncertainty analyses based on different resampling techniques. The analyzed data comprise event series from Rudna copper mine in Poland. The studies show that the interevent time and interevent distance processes have both long and short memory. MIS occurrences and locations are internally interrelated. Internal relations among the sizes of MIS events are apparently weaker than those of other two studied parameterizations and are limited to long term interactions.

Blasting for abandoned-mine land reclamation (closure of individual subsidence features and erratic, undocumented underground coal-mine workings). Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Workman, J.L.; Thompson, J.

1991-01-01

The study has examined the feasibility of blasting for mitigating various abandoned mine land features on AML sites. The investigation included extensive field trial blasts at sites in North Dakota and Montana. A blasting technique was used that was based on spherical cratering concepts. At the Beulah, North Dakota site thirteen individual vertical openings (sinkholes) were blasted with the intent to fill the voids. The blasts were designed to displace material laterally into the void. Good success was had in filling the sinkholes. At the White site in Montana erratic underground rooms with no available documentation were collapsed. An aditmore » leading into the mine was also blasted. Both individual room blasting and area pattern blasting were studied. A total of eight blasts were fired on the one acre area. Exploration requirements and costs were found to be extensive.« less

The ``battle of gold'' under the light of green economics: a case study from Greece

NASA Astrophysics Data System (ADS)

Damigos, D.; Kaliampakos, D.

2006-05-01

Mining firms stimulate local and national economies but this comes at a certain cost. In the light of increasing public concern, external costs of environmental degradation and social disruption are no longer of pure academic interest. The assessment of mining projects on the grounds of sustainable development is critical in order to decide whether the exploitation of mineral resources is socially desirable. In practice, few steps have been taken towards this end. In this paper, a case study is illustrated that provides the means for evaluating the social worthiness of mining projects. The analysis, which is the first of its kind in Greece, deals with a major problem of the mining industry: the gold debate on the grounds of green economics. The assessment is based on the social cost benefit approach. Well-established techniques (e.g. benefit transfer) and innovative approaches have been adopted to overcome various practical problems

Data-Mining Technologies for Diabetes: A Systematic Review

PubMed Central

Marinov, Miroslav; Mosa, Abu Saleh Mohammad; Yoo, Illhoi; Boren, Suzanne Austin

2011-01-01

Background The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research. Method We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes. Results The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. Conclusions Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients. PMID:22226277

Surface mining

Treesearch

Robert Leopold; Bruce Rowland; Reed Stalder

1979-01-01

The surface mining process consists of four phases: (1) exploration; (2) development; (3) production; and (4) reclamation. A variety of surface mining methods has been developed, including strip mining, auger, area strip, open pit, dredging, and hydraulic. Sound planning and design techniques are essential to implement alternatives to meet the myriad of laws,...

A Survey of Educational Data-Mining Research

ERIC Educational Resources Information Center

Huebner, Richard A.

2013-01-01

Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…

A Comparative Study of Data Mining Techniques on Football Match Prediction

NASA Astrophysics Data System (ADS)

Rosli, Che Mohamad Firdaus Che Mohd; Zainuri Saringat, Mohd; Razali, Nazim; Mustapha, Aida

2018-05-01

Data prediction have become a trend in today’s business or organization. This paper is set to predict match outcomes for association football from the perspective of football club managers and coaches. This paper explored different data mining techniques used for predicting the match outcomes where the target class is win, draw and lose. The main objective of this research is to find the most accurate data mining technique that fits the nature of football data. The techniques tested are Decision Trees, Neural Networks, Bayesian Network, and k-Nearest Neighbors. The results from the comparative experiments showed that Decision Trees produced the highest average prediction accuracy in the domain of football match prediction by 99.56%.

Knowledge Discovery and Data Mining: An Overview

NASA Technical Reports Server (NTRS)

Fayyad, U.

1995-01-01

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

Application of Information-Theoretic Data Mining Techniques in a National Ambulatory Practice Outcomes Research Network

PubMed Central

Wright, Adam; Ricciardi, Thomas N.; Zwick, Martin

2005-01-01

The Medical Quality Improvement Consortium data warehouse contains de-identified data on more than 3.6 million patients including their problem lists, test results, procedures and medication lists. This study uses reconstructability analysis, an information-theoretic data mining technique, on the MQIC data warehouse to empirically identify risk factors for various complications of diabetes including myocardial infarction and microalbuminuria. The risk factors identified match those risk factors identified in the literature, demonstrating the utility of the MQIC data warehouse for outcomes research, and RA as a technique for mining clinical data warehouses. PMID:16779156

Prediction of pork quality parameters by applying fractals and data mining on MRI.

PubMed

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés; Amigo, José Manuel; Dahl, Anders B; ErsbØll, Bjarne K; Antequera, Teresa

2017-09-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy. Copyright © 2017. Published by Elsevier Ltd.

Customizing FP-growth algorithm to parallel mining with Charm++ library

NASA Astrophysics Data System (ADS)

Puścian, Marek

2017-08-01

This paper presents a frequent item mining algorithm that was customized to handle growing data repositories. The proposed solution applies Master Slave scheme to frequent pattern growth technique. Efficient utilization of available computation units is achieved by dynamic reallocation of tasks. Conditional frequent trees are assigned to parallel workers basing on their workload. Proposed enhancements have been successfully implemented using Charm++ library. This paper discusses results of the performance of parallelized FP-growth algorithm against different datasets. The approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer.

Data Mining and Knowledge Management in Higher Education -Potential Applications.

ERIC Educational Resources Information Center

Luan, Jing

This paper introduces a new decision support tool, data mining, in the context of knowledge management. The most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a…

Evolutionary optimization of radial basis function classifiers for data mining applications.

PubMed

Buchtala, Oliver; Klimek, Manuel; Sick, Bernhard

2005-10-01

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

Energy Crunch is Stimulant for Coal Research

ERIC Educational Resources Information Center

Chemical and Engineering News, 1973

1973-01-01

Presents views of the first International Coal Research Conference, involving problems facing reconversion to a coal-based energy economy, organization and funding of coal research units, development of new techniques for mining and using coal; and transportation of coal products to users. (CC)

A Proposal for Kelly CriterionBased Lossy Network Compression

DTIC Science & Technology

2016-03-01

warehousing and data mining techniques for cyber security. New York (NY): Springer; 2007. p. 83–108. 34. Münz G, Li S, Carle G. Traffic anomaly...p. 188–196. 48. Kim NU, Park MW, Park SH, Jung SM, Eom JH, Chung TM. A study on ef- fective hash-based load balancing scheme for parallel nids. In

Forecasting Reading Anxiety for Promoting English-Language Reading Performance Based on Reading Annotation Behavior

ERIC Educational Resources Information Center

Chen, Chih-Ming; Wang, Jung-Ying; Chen, Yong-Ting; Wu, Jhih-Hao

2016-01-01

To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In…

A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

PubMed Central

Zou, Wei; She, Jianwen; Tolstikov, Vladimir V.

2013-01-01

Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow. PMID:24958150

Text Mining in Organizational Research

PubMed Central

Kobayashi, Vladimer B.; Berkers, Hannah A.; Kismihók, Gábor; Den Hartog, Deanne N.

2017-01-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies. PMID:29881248

Text Mining in Organizational Research.

PubMed

Kobayashi, Vladimer B; Mol, Stefan T; Berkers, Hannah A; Kismihók, Gábor; Den Hartog, Deanne N

2018-07-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.

Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network.

PubMed

Alizadeh, Behrouz; Safdari, Reza; Zolnoori, Maryam; Bashiri, Azadeh

2015-08-01

Lack of proper diagnosis and inadequate treatment of asthma, leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different modes was made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. So considering the data mining approaches due to the nature of medical data is necessary.

Data Mining of Macromolecular Structures.

PubMed

van Beusekom, Bart; Perrakis, Anastassis; Joosten, Robbie P

2016-01-01

The use of macromolecular structures is widespread for a variety of applications, from teaching protein structure principles all the way to ligand optimization in drug development. Applying data mining techniques on these experimentally determined structures requires a highly uniform, standardized structural data source. The Protein Data Bank (PDB) has evolved over the years toward becoming the standard resource for macromolecular structures. However, the process selecting the data most suitable for specific applications is still very much based on personal preferences and understanding of the experimental techniques used to obtain these models. In this chapter, we will first explain the challenges with data standardization, annotation, and uniformity in the PDB entries determined by X-ray crystallography. We then discuss the specific effect that crystallographic data quality and model optimization methods have on structural models and how validation tools can be used to make informed choices. We also discuss specific advantages of using the PDB_REDO databank as a resource for structural data. Finally, we will provide guidelines on how to select the most suitable protein structure models for detailed analysis and how to select a set of structure models suitable for data mining.

Application of Three Existing Stope Boundary Optimisation Methods in an Operating Underground Mine

NASA Astrophysics Data System (ADS)

Erdogan, Gamze; Yavuz, Mahmut

2017-12-01

The underground mine planning and design optimisation process have received little attention because of complexity and variability of problems in underground mines. Although a number of optimisation studies and software tools are available and some of them, in special, have been implemented effectively to determine the ultimate-pit limits in an open pit mine, there is still a lack of studies for optimisation of ultimate stope boundaries in underground mines. The proposed approaches for this purpose aim at maximizing the economic profit by selecting the best possible layout under operational, technical and physical constraints. In this paper, the existing three heuristic techniques including Floating Stope Algorithm, Maximum Value Algorithm and Mineable Shape Optimiser (MSO) are examined for optimisation of stope layout in a case study. Each technique is assessed in terms of applicability, algorithm capabilities and limitations considering the underground mine planning challenges. Finally, the results are evaluated and compared.

Evaluation of the environmental contamination at an abandoned mining site using multivariate statistical techniques--the Rodalquilar (Southern Spain) mining district.

PubMed

Bagur, M G; Morales, S; López-Chicano, M

2009-11-15

Unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal component analysis, factor analysis and linear discriminant analysis have been applied to water samples recollected in Rodalquilar mining district (Southern Spain) in order to identify different sources of environmental pollution caused by the abandoned mining industry. The effect of the mining activity on waters was monitored determining the concentration of eleven elements (Mn, Ba, Co, Cu, Zn, As, Cd, Sb, Hg, Au and Pb) by inductively coupled plasma mass spectrometry (ICP-MS). The Box-Cox transformation has been used to transform the data set in normal form in order to minimize the non-normal distribution of the geochemical data. The environmental impact is affected mainly by the mining activity developed in the zone, the acid drainage and finally by the chemical treatment used for the benefit of gold.

Using temporarily coherent point interferometric synthetic aperture radar for land subsidence monitoring in a mining region of western China

NASA Astrophysics Data System (ADS)

Fan, Hongdong; Xu, Qiang; Hu, Zhongbo; Du, Sen

2017-04-01

Yuyang mine is located in the semiarid western region of China where, due to serious land subsidence caused by underground coal exploitation, the local ecological environment has become more fragile. An advanced interferometric synthetic aperture radar (InSAR) technique, temporarily coherent point InSAR, is applied to measure surface movements caused by different mining conditions. Fifteen high-resolution TerraSAR-X images acquired between October 2, 2012, and March 27, 2013, were processed to generate time-series data for ground deformation. The results show that the maximum accumulated values of subsidence and velocity were 86 mm and 162 mm/year, respectively; these measurements were taken above the fully mechanized longwall caving faces. Based on the dynamic land subsidence caused by the exploitation of one working face, the land subsidence range was deduced to have increased 38 m in the mining direction with 11 days' coal extraction. Although some mining faces were ceased in 2009, they could also have contributed to a small residual deformation of overlying strata. Surface subsidence of the backfill mining region was quite small, the maximum only 21 mm, so backfill exploitation is an effective method for reducing the land subsidence while coal is mined.

Energy Partitioning of Seismic Phases: Current Datasets and Techniques Aimed at Improving the Future of Event Identification

NASA Astrophysics Data System (ADS)

Bonner, J.

2006-05-01

Differences in energy partitioning of seismic phases from earthquakes and explosions provide the opportunity for event identification. In this talk, I will briefly review teleseismic Ms:mb and P/S ratio techniques that help identify events based on differences in compressional, shear, and surface wave energy generation from explosions and earthquakes. With the push to identify smaller yield explosions, the identification process has become increasingly complex as varied types of explosions, including chemical, mining, and nuclear, must be identified at regional distances. Thus, I will highlight some of the current views and problems associated with the energy partitioning of seismic phases from single- and delay-fired chemical explosions. One problem yet to have a universally accepted answer is whether the explosion and earthquake populations, based on the Ms:mb discriminants, should be separated at smaller magnitudes. I will briefly describe the datasets and theory that support either converging or parallel behavior of these populations. Also, I will discuss improvement to the currently used methods that will better constrain this problem in the future. I will also discuss the role of regional P/S ratios in identifying explosions. In particular, recent datasets from South Africa, Scandinavia, and the Western United States collected from earthquakes, single-fired chemical explosions, and/or delay-fired mining explosions have provide new insight into regional P, S, Lg, and Rg energy partitioning. Data from co-located mining and chemical explosions suggest that some mining explosions may be used for limited calibration of regional discriminants in regions where no historic explosion data is available.

Introduction to the mining of clinical data.

PubMed

Harrison, James H

2008-03-01

The increasing volume of medical data online, including laboratory data, represents a substantial resource that can provide a foundation for improved understanding of disease presentation, response to therapy, and health care delivery processes. Data mining supports these goals by providing a set of techniques designed to discover similarities and relationships between data elements in large data sets. Currently, medical data have several characteristics that increase the difficulty of applying these techniques, although there have been notable medical data mining successes. Future developments in integrated medical data repositories, standardized data representation, and guidelines for the appropriate research use of medical data will decrease the barriers to mining projects.

Standardized plant disease evaluations will enhance resistance gene discovery

USDA-ARS?s Scientific Manuscript database

Gene discovery and marker development using DNA-based tools require plant populations with well documented phenotypes. If dissimilar phenotype evaluation methods or data scoring techniques are employed with different crops, or at different labs for the same crops, then data mining for genetic marker...

A data mining approach to dinoflagellate clustering according to sterol composition: Correlations with evolutionary history.

USDA-ARS?s Scientific Manuscript database

This study examined the sterol compositions of 102 dinoflagellates (including several previously unexamined species) using clustering techniques as a means of determining the relatedness of the organisms. In addition, dinoflagellate sterol-based relationships were compared statistically to dinoflag...

Deriving preference order of post-mining land-uses through MLSA framework: application of an outranking technique

NASA Astrophysics Data System (ADS)

Soltanmohammadi, Hossein; Osanloo, Morteza; Aghajani Bazzazi, Abbas

2009-08-01

This study intends to take advantage of a previously developed framework for mined land suitability analysis (MLSA) consisted of economical, social, technical and mine site factors to achieve a partial and also a complete pre-order of feasible post-mining land-uses. Analysis by an outranking multi-attribute decision-making (MADM) technique, called PROMETHEE (preference ranking organization method for enrichment evaluation), was taken into consideration because of its clear advantages on the field of MLSA as compared with MADM ranking techniques. Application of the proposed approach on a mined land can be completed through some successive steps. First, performance of the MLSA attributes is scored locally by each individual decision maker (DM). Then the assigned performance scores are normalized and the deviation amplitudes of non-dominated alternatives are calculated. Weights of the attributes are calculated by another MADM technique namely, analytical hierarchy process (AHP) in a separate procedure. Using the Gaussian preference function beside the weights, the preference indexes of the land-use alternatives are obtained. Calculation of the outgoing and entering flows of the alternatives and one by one comparison of these values will lead to partial pre-order of them and calculation of the net flows, will lead to a ranked preference for each land-use. At the final step, utilizing the PROMETHEE group decision support system which incorporates judgments of all the DMs, a consensual ranking can be derived. In this paper, preference order of post-mining land-uses for a hypothetical mined land has been derived according to judgments of one DM to reveal applicability of the proposed approach.

Application of remote-sensing techniques to hydrologic studies in selected coal-mine areas of southeastern Kansas

USGS Publications Warehouse

Kenny, J.F.; McCauley, J.R.

1983-01-01

Disturbances resulting from intensive coal mining in the Cherry Creek basin of southeastern Kansas were investigated using color and color-infrared aerial photography in conjunction with water-quality data from simultaneously acquired samples. Imagery was used to identify the type and extent of vegetative cover on strip-mined lands and the extent and success of reclamation practices. Drainage patterns, point sources of acid mine drainage, and recharge areas for underground mines were located for onsite inspection. Comparison of these interpretations with water-quality data illustrated differences between the eastern and western parts of the Cherry Creek basin. Contamination in the eastern part is due largely to circulation of water from unreclaimed strip mines and collapse features through the network of underground mines and subsequent discharge of acidic drainage through seeps. Contamination in the western part is primarily caused by runoff and seepage from strip-mined lands in which surfaces have frequently been graded and limed but are generally devoid of mature stands of soil-anchoring vegetation. The successful use of aerial photography in the study of Cherry Creek basin indicates the potential of using remote-sensing techniques in studies of other coal-mined regions. (USGS)

Macroscopic Streamer Growths in Acidic, Metal-Rich Mine Waters in North Wales Consist of Novel and Remarkably Simple Bacterial Communities

PubMed Central

Hallberg, Kevin B.; Coupland, Kris; Kimura, Sakurako; Johnson, D. Barrie

2006-01-01

The microbial composition of acid streamers (macroscopic biofilms) in acidic, metal-rich waters in two locations (an abandoned copper mine and a chalybeate spa) in north Wales was studied using cultivation-based and biomolecular techniques. Known chemolithotrophic and heterotrophic acidophiles were readily isolated from disrupted streamers, but they accounted for only <1 to 7% of the total microorganisms present. Fluorescent in situ hybridization (FISH) revealed that 80 to 90% of the microbes in both types of streamers were β-Proteobacteria. Terminal restriction fragment length polymorphism analysis of the streamers suggested that a single bacterial species was dominant in the copper mine streamers, while two distinct bacteria (one of which was identical to the bacterium found in the copper mine streamers) accounted for about 90% of the streamers in the spa water. 16S rRNA gene clone libraries showed that the β-proteobacterium found in both locations was closely related to a clone detected previously in acid mine drainage in California and that its closest characterized relatives were neutrophilic ammonium oxidizers. Using a modified isolation technique, this bacterium was isolated from the copper mine streamers and shown to be a novel acidophilic autotrophic iron oxidizer. The β-proteobacterium found only in the spa streamers was closely related to the neutrophilic iron oxidizer Gallionella ferruginea. FISH analysis using oligonucleotide probes that targeted the two β-proteobacteria confirmed that the biodiversity of the streamers in both locations was very limited. The microbial compositions of the acid streamers found at the two north Wales sites are very different from the microbial compositions of the previously described acid streamers found at Iron Mountain, California, and the Rio Tinto, Spain. PMID:16517651

Remediation and rehabilitation of abandoned mining sites in Cyprus

NASA Astrophysics Data System (ADS)

Helsen, S.; Rommens, T.; De Ridder, A.; Panayiotou, C.; Colpaert, J.

2009-04-01

Due to a particular geological setting, Cyprus is rich in ore deposits, many of them subject to extensive mining. Most of the mines have a long history, sometimes dating back to prehistorical times. These abandoned mines cause severe off-site environmental problems and health risks for the local population. Groundwater supplies are affected by the leaching of pollutants, surface water is contaminated because of water erosion, and harmful dust containing heavy metals or asbestos is spread due to wind erosion. In addition to the environmental risks associated with the abandoned mines, many of these sites are aestethically unattractive, and remain an economic burden to stakeholders and the public in general, due to the downgrading of surrounding areas, non-development and hence loss of revenue. These factors are important in Cyprus where tourism is a significant source of income for local communities. An EUREKA-project addresses the issue of abandoned mine clean-up and restoration. The main objectives of this study are : (1) To develop phytostabilization and -remediation techniques to stabilize and clean up sites characterized by high nickel and copper concentrations in the soil, using endemic plants (Alyssum spp. and mycorrhizal Pinus brutia). In some old mines, efforts were already made to stabilize slopes in an attempt to minimize soil erosion and spreading of pollutants. These restoration efforts, however, remained largely unsuccessful because vegetation that was planted could not cope with the harsh hydrogeochemical soil characteristics. Regeneration of the vegetation cover therefore failed ; (2) to demonstrate the risks associated to the environmental hazard of metal polluted mine spoils and outline a method by which to accomplish this type of risk assessment ; (3) to analyse costs and benefits of phytostabilization- and phytoremediation-based solution for the problem. Results of the first experiments are still preliminary and incomplete. However, it is expected that a better knowledge on growing conditions of the selected plant species will contribute to the development of a phytoremediation technique for a low-cost and sustainable restoration of the old mine sites. Moreover, this will have direct utility to other areas in the Mediterranean region, that are similarly threatened by the presence of heavy metals in the environment.

Pattern mining of user interaction logs for a post-deployment usability evaluation of a radiology PACS client.

PubMed

Jorritsma, Wiard; Cnossen, Fokie; Dierckx, Rudi A; Oudkerk, Matthijs; van Ooijen, Peter M A

2016-01-01

To perform a post-deployment usability evaluation of a radiology Picture Archiving and Communication System (PACS) client based on pattern mining of user interaction log data, and to assess the usefulness of this approach compared to a field study. All user actions performed on the PACS client were logged for four months. A data mining technique called closed sequential pattern mining was used to automatically extract frequently occurring interaction patterns from the log data. These patterns were used to identify usability issues with the PACS. The results of this evaluation were compared to the results of a field study based usability evaluation of the same PACS client. The interaction patterns revealed four usability issues: (1) the display protocols do not function properly, (2) the line measurement tool stays active until another tool is selected, rather than being deactivated after one use, (3) the PACS's built-in 3D functionality does not allow users to effectively perform certain 3D-related tasks, (4) users underuse the PACS's customization possibilities. All usability issues identified based on the log data were also found in the field study, which identified 48 issues in total. Post-deployment usability evaluation based on pattern mining of user interaction log data provides useful insights into the way users interact with the radiology PACS client. However, it reveals few usability issues compared to a field study and should therefore not be used as the sole method of usability evaluation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

30 CFR 282.28 - Environmental protection measures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... recent research or improved monitoring techniques. (5) When prototype test mining is proposed, the lessee...) The sampling techniques and procedures to be used to acquire the needed data and information; (ii) The... evaluation of the approved Delineation, Testing, or Mining Plan. The Director's review of the air quality...

Analyzing Teaching Performance of Instructors Using Data Mining Techniques

ERIC Educational Resources Information Center

Mardikyan, Sona; Badur, Bertain

2011-01-01

Student evaluations to measure the teaching effectiveness of instructor's are very frequently applied in higher education for many years. This study investigates the factors associated with the assessment of instructors teaching performance using two different data mining techniques; stepwise regression and decision trees. The data collected…

Monitoring and inversion on land subsidence over mining area with InSAR technique

USGS Publications Warehouse

Wang, Y.; Zhang, Q.; Zhao, C.; Lu, Z.; Ding, X.

2011-01-01

The Wulanmulun town, located in Inner Mongolia, is one of the main mining areas of Shendong Company such as Shangwan coal mine and Bulianta coal mine, which has been suffering serious mine collapse with the underground mine withdrawal. We use ALOS/PALSAR data to extract land deformation under these regions, in which Small Baseline Subsets (SBAS) method was applied. Then we compared InSAR results with the underground mining activities, and found high correlations between them. Lastly we applied Distributed Dislocation (Okada) model to invert the mine collapse mechanism. ?? 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

15 CFR 970.600 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

A comprehensive review on privacy preserving data mining.

PubMed

Aldeen, Yousra Abdul Alsahib S; Salleh, Mazleena; Razzaque, Mohammad Abdur

2015-01-01

Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nurhandoko, Bagus Endar B.; Wely, Woen; Setiadi, Herlan

It is already known that tomography has a great impact for analyzing and mapping unknown objects based on inversion, travel time as well as waveform inversion. Therefore, tomography has used in wide area, not only in medical but also in petroleum as well as mining. Recently, tomography method is being applied in several mining industries. A case study of tomography imaging has been carried out in DOZ ( Deep Ore Zone ) block caving mine, Tembagapura, Papua. Many researchers are undergoing to investigate the properties of DOZ cave not only outside but also inside which is unknown. Tomography takes amore » part for determining this objective.The sources are natural from the seismic events that caused by mining induced seismicity and rocks deformation activity, therefore it is called as passive seismic. These microseismic travel time data are processed by Simultaneous Iterative Reconstruction Technique (SIRT). The result of the inversion can be used for DOZ cave monitoring. These information must be used for identifying weak zone inside the cave. In addition, these results of tomography can be used to determine DOZ and cave information to support mine activity in PT. Freeport Indonesia.« less

VRLane: a desktop virtual safety management program for underground coal mine

NASA Astrophysics Data System (ADS)

Li, Mei; Chen, Jingzhu; Xiong, Wei; Zhang, Pengpeng; Wu, Daozheng

2008-10-01

VR technologies, which generate immersive, interactive, and three-dimensional (3D) environments, are seldom applied to coal mine safety work management. In this paper, a new method that combined the VR technologies with underground mine safety management system was explored. A desktop virtual safety management program for underground coal mine, called VRLane, was developed. The paper mainly concerned about the current research advance in VR, system design, key techniques and system application. Two important techniques were introduced in the paper. Firstly, an algorithm was designed and implemented, with which the 3D laneway models and equipment models can be built on the basis of the latest mine 2D drawings automatically, whereas common VR programs established 3D environment by using 3DS Max or the other 3D modeling software packages with which laneway models were built manually and laboriously. Secondly, VRLane realized system integration with underground industrial automation. VRLane not only described a realistic 3D laneway environment, but also described the status of the coal mining, with functions of displaying the run states and related parameters of equipment, per-alarming the abnormal mining events, and animating mine cars, mine workers, or long-wall shearers. The system, with advantages of cheap, dynamic, easy to maintenance, provided a useful tool for safety production management in coal mine.

Text mining and its potential applications in systems biology.

PubMed

Ananiadou, Sophia; Kell, Douglas B; Tsujii, Jun-ichi

2006-12-01

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.

Application of LANDSAT data to monitor land reclamation progress in Belmont County, Ohio

NASA Technical Reports Server (NTRS)

Bloemer, H. H. L.; Brumfield, J. O.; Campbell, W. J.; Witt, R. G.; Bly, B. G.

1981-01-01

Strip and contour mining techniques are reviewed as well as some studies conducted to determine the applicability of LANDSAT and associated digital image processing techniques to the surficial problems associated with mining operations. A nontraditional unsupervised classification approach to multispectral data is considered which renders increased classification separability in land cover analysis of surface mined areas. The approach also reduces the dimensionality of the data and requires only minimal analytical skills in digital data processing.

Definition of redox and pH influence in the AMD mine system using a fuzzy qualitative tool (Iberian Pyrite Belt, SW Spain).

PubMed

de la Torre, M L; Grande, J A; Valente, T; Perez-Ostalé, E; Santisteban, M; Aroba, J; Ramos, I

2016-03-01

Poderosa Mine is an abandoned pyrite mine, located in the Iberian Pyrite Belt which pours its acid mine drainage (AMD) waters into the Odiel river (South-West Spain). This work focuses on establishing possible reasons for interdependence between the potential redox and pH, with the load of metals and sulfates, as well as a set of variables that define the physical chemistry of the water-conductivity, temperature, TDS, and dissolved oxygen-transported by a channel from Poderosa mine affected by acid mine drainage, through the use of techniques of artificial intelligence: fuzzy logic and data mining. The sampling campaign was carried out in May of 2012. There were a total of 16 sites, the first inside the tunnel and the last at the mouth of the river Odiel, with a distance of approximately 10 m between each pair of measuring stations. While the tools of classical statistics, which are widely used in this context, prove useful for defining proximity ratios between variables based on Pearson's correlations, in addition to making it easier to handle large volumes of data and producing easier-to-understand graphs, the use of fuzzy logic tools and data mining results in better definition of the variations produced by external stimuli on the set of variables. This tool is adaptable and can be extrapolated to any system polluted by acid mine drainage using simple, intuitive reasoning.

Brain tumor classification using AFM in combination with data mining techniques.

PubMed

Huml, Marlene; Silye, René; Zauner, Gerald; Hutterer, Stephan; Schilcher, Kurt

2013-01-01

Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

COMPARISON OF DATA FROM SYNTHETIC LEACHATE AND DIRECT SAMPLING OF ACID DRAINAGE FROM MINE WASTES: IMPLICATIONS FOR MERCURY TRANSPORT AND WASTE MANAGEMENT

EPA Science Inventory

The Sulphur Bank Mercury Mine (SBMM) in Lake County, California operated from the 1860s through the 1950's. Mining for sulfur started with surface operations and progressed to shaft, then open pit techniques to obtain mercury. Mining has resulted in deposition of approximately ...

Examining Online Learning Patterns with Data Mining Techniques in Peer-Moderated and Teacher-Moderated Courses

ERIC Educational Resources Information Center

Hung, Jui-Long; Crooks, Steven M.

2009-01-01

The student learning process is important in online learning environments. If instructors can "observe" online learning behaviors, they can provide adaptive feedback, adjust instructional strategies, and assist students in establishing patterns of successful learning activities. This study used data mining techniques to examine and…

Site investigation report mine research project GUE 70-14.10, Guernsey, Ohio.

DOT National Transportation Integrated Search

2003-06-01

Geophysical investigative techniques can be a valuable supplement to standard subsurface investigations for the : evaluation of abandoned underground coal mine workings and their potential impacts at the ground surface. The GUE : 70 - 14.10 Mine Rese...

Data-Mining-Based Intelligent Differential Relaying for Transmission Lines Including UPFC and Wind Farms.

PubMed

Jena, Manas Kumar; Samantaray, Subhransu Ranjan

2016-01-01

This paper presents a data-mining-based intelligent differential relaying scheme for transmission lines, including flexible ac transmission system device, such as unified power flow controller (UPFC) and wind farms. Initially, the current and voltage signals are processed through extended Kalman filter phasor measurement unit for phasor estimation, and 21 potential features are computed at both ends of the line. Once the features are extracted at both ends, the corresponding differential features are derived. These differential features are fed to a data-mining model known as decision tree (DT) to provide the final relaying decision. The proposed technique has been extensively tested for single-circuit transmission line, including UPFC and wind farms with in-feed, double-circuit line with UPFC on one line and wind farm as one of the substations with wide variations in operating parameters. The test results obtained from simulation as well as in real-time digital simulator testing indicate that the DT-based intelligent differential relaying scheme is highly reliable and accurate with a response time of 2.25 cycles from the fault inception.

A Data mining Technique for Analyzing and Predicting the success of Movie

NASA Astrophysics Data System (ADS)

Meenakshi, K.; Maragatham, G.; Agarwal, Neha; Ghosh, Ishitha

2018-04-01

In real world prediction models and mechanisms can be used to predict the success of a movie. The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty. An attempt is made to predict the past as well as the future of movie for the purpose of business certainty or simply a theoretical condition in which decision making [the success of the movie] is without risk, because the decision maker [movie makers and stake holders] has all the information about the exact outcome of the decision, before he or she makes the decision [release of the movie]. With over two million spectators a day and films exported to over 100 countries, the impact of Bollywood film industry is formidable We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the success prediction of movies, such as whether any particular actors or actresses are likely to help a movie to succeed. The paper additionally reports on the techniques used, giving their implementation and utility. Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.

Developing a hybrid dictionary-based bio-entity recognition technique.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2015-01-01

Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.

Developing a hybrid dictionary-based bio-entity recognition technique

PubMed Central

2015-01-01

Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. PMID:26043907

Analysis of Hospital Processes with Process Mining Techniques.

PubMed

Orellana García, Arturo; Pérez Alfonso, Damián; Larrea Armenteros, Osvaldo Ulises

2015-01-01

Process mining allows for discovery, monitoring, and improving processes identified in information systems from their event logs. In hospital environments, process analysis has been a crucial factor for cost reduction, control and proper use of resources, better patient care, and achieving service excellence. This paper presents a new component for event logs generation in the Hospital Information System or HIS, developed at University of Informatics Sciences. The event logs obtained are used for analysis of hospital processes with process mining techniques. The proposed solution intends to achieve the generation of event logs in the system with high quality. The performed analyses allowed for redefining functions in the system and proposed proper flow of information. The study exposed the need to incorporate process mining techniques in hospital systems to analyze the processes execution. Moreover, we illustrate its application for making clinical and administrative decisions for the management of hospital activities.

Visual cues for data mining

NASA Astrophysics Data System (ADS)

Rogowitz, Bernice E.; Rabenhorst, David A.; Gerth, John A.; Kalin, Edward B.

1996-04-01

This paper describes a set of visual techniques, based on principles of human perception and cognition, which can help users analyze and develop intuitions about tabular data. Collections of tabular data are widely available, including, for example, multivariate time series data, customer satisfaction data, stock market performance data, multivariate profiles of companies and individuals, and scientific measurements. In our approach, we show how visual cues can help users perform a number of data mining tasks, including identifying correlations and interaction effects, finding clusters and understanding the semantics of cluster membership, identifying anomalies and outliers, and discovering multivariate relationships among variables. These cues are derived from psychological studies on perceptual organization, visual search, perceptual scaling, and color perception. These visual techniques are presented as a complement to the statistical and algorithmic methods more commonly associated with these tasks, and provide an interactive interface for the human analyst.

The Role of Economic Uncertainty on the Block Economic Value - a New Valuation Approach / Rola Czynnika Niepewności Przy Obliczaniu Wskaźnika Rentowności - Nowe Podejście

NASA Astrophysics Data System (ADS)

Dehghani, H.; Ataee-Pour, M.

2012-12-01

The block economic value (EV) is one of the most important parameters in mine evaluation. This parameter can affect significant factors such as mining sequence, final pit limit and net present value. Nowadays, the aim of open pit mine planning is to define optimum pit limits and an optimum life of mine production scheduling that maximizes the pit value under some technical and operational constraints. Therefore, it is necessary to calculate the block economic value at the first stage of the mine planning process, correctly. Unrealistic block economic value estimation may cause the mining project managers to make the wrong decision and thus may impose inexpiable losses to the project. The effective parameters such as metal price, operating cost, grade and so forth are always assumed certain in the conventional methods of EV calculation. While, obviously, these parameters have uncertain nature. Therefore, usually, the conventional methods results are far from reality. In order to solve this problem, a new technique is used base on an invented binomial tree which is developed in this research. This method can calculate the EV and project PV under economic uncertainty. In this paper, the EV and project PV were initially determined using Whittle formula based on certain economic parameters and a multivariate binomial tree based on the economic uncertainties such as the metal price and cost uncertainties. Finally the results were compared. It is concluded that applying the metal price and cost uncertainties causes the calculated block economic value and net present value to be more realistic than certain conditions.

A new approach to preserve privacy data mining based on fuzzy theory in numerical database

NASA Astrophysics Data System (ADS)

Cui, Run; Kim, Hyoung Joong

2014-01-01

With the rapid development of information techniques, data mining approaches have become one of the most important tools to discover the in-deep associations of tuples in large-scale database. Hence how to protect the private information is quite a huge challenge, especially during the data mining procedure. In this paper, a new method is proposed for privacy protection which is based on fuzzy theory. The traditional fuzzy approach in this area will apply fuzzification to the data without considering its readability. A new style of obscured data expression is introduced to provide more details of the subsets without reducing the readability. Also we adopt a balance approach between the privacy level and utility when to achieve the suitable subgroups. An experiment is provided to show that this approach is suitable for the classification without a lower accuracy. In the future, this approach can be adapted to the data stream as the low computation complexity of the fuzzy function with a suitable modification.

Chemical named entities recognition: a review on approaches and applications.

PubMed

Eltyeb, Safaa; Salim, Naomie

2014-01-01

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.

Applying Data Mining Techniques to Improve Breast Cancer Diagnosis.

PubMed

Diz, Joana; Marreiros, Goreti; Freitas, Alberto

2016-09-01

In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

GyneScan

PubMed Central

Acharya, U. Rajendra; Sree, S. Vinitha; Kulshreshtha, Sanjeev; Molinari, Filippo; Koh, Joel En Wei; Saba, Luca; Suri, Jasjit S.

2014-01-01

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor. PMID:24325128

Data Mining Techniques for Customer Relationship Management

NASA Astrophysics Data System (ADS)

Guo, Feng; Qin, Huilin

2017-10-01

Data mining have made customer relationship management (CRM) a new area where firms can gain a competitive advantage, and play a key role in the firms’ management decision. In this paper, we first analyze the value and application fields of data mining techniques for CRM, and further explore how data mining applied to Customer churn analysis. A new business culture is developing today. The conventional production centered and sales purposed market strategy is gradually shifting to customer centered and service purposed. Customers’ value orientation is increasingly affecting the firms’. And customer resource has become one of the most important strategic resources. Therefore, understanding customers’ needs and discriminating the most contributed customers has become the driving force of most modern business.

30 CFR 282.23 - Testing Plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, REGULATION, AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR... lessee needs more information to develop a detailed Mining Plan than is obtainable under an approved... techniques or technology or mining equipment, or to determine environmental effects by a pilot test mining...

Intelligent and integrated techniques for coalbed methane (CBM) recovery and reduction of greenhouse gas emission.

PubMed

Qianting, Hu; Yunpei, Liang; Han, Wang; Quanle, Zou; Haitao, Sun

2017-07-01

Coalbed methane (CBM) recovery is a crucial approach to realize the exploitation of a clean energy and the reduction of the greenhouse gas emission. In the past 10 years, remarkable achievements on CBM recovery have been obtained in China. However, some key difficulties still exist such as long borehole drilling in complicated geological condition, and poor gas drainage effect due to low permeability. In this study, intelligent and integrated techniques for CBM recovery are introduced. These integrated techniques mainly include underground CBM recovery techniques and ground well CBM recovery techniques. The underground CBM recovery techniques consist of the borehole formation technique, gas concentration improvement technique, and permeability enhancement technique. According to the division of mining-induced disturbance area, the ground well arrangement area and well structure type in mining-induced disturbance developing area and mining-induced disturbance stable area are optimized to significantly improve the ground well CBM recovery. Besides, automatic devices such as drilling pipe installation device are also developed to achieve remote control of data recording, which makes the integrated techniques intelligent. These techniques can provide key solutions to some long-term difficulties in CBM recovery.

Unravelling associations between unassigned mass spectrometry peaks with frequent itemset mining techniques.

PubMed

Vu, Trung Nghia; Mrzic, Aida; Valkenborg, Dirk; Maes, Evelyne; Lemière, Filip; Goethals, Bart; Laukens, Kris

2014-01-01

Mass spectrometry-based proteomics experiments generate spectra that are rich in information. Often only a fraction of this information is used for peptide/protein identification, whereas a significant proportion of the peaks in a spectrum remain unexplained. In this paper we explore how a specific class of data mining techniques termed "frequent itemset mining" can be employed to discover patterns in the unassigned data, and how such patterns can help us interpret the origin of the unexpected/unexplained peaks. First a model is proposed that describes the origin of the observed peaks in a mass spectrum. For this purpose we use the classical correlative database search algorithm. Peaks that support a positive identification of the spectrum are termed explained peaks. Next, frequent itemset mining techniques are introduced to infer which unexplained peaks are associated in a spectrum. The method is validated on two types of experimental proteomic data. First, peptide mass fingerprint data is analyzed to explain the unassigned peaks in a full scan mass spectrum. Interestingly, a large numbers of experimental spectra reveals several highly frequent unexplained masses, and pattern mining on these frequent masses demonstrates that subsets of these peaks frequently co-occur. Further evaluation shows that several of these co-occurring peaks indeed have a known common origin, and other patterns are promising hypothesis generators for further analysis. Second, the proposed methodology is validated on tandem mass spectrometral data using a public spectral library, where associations within the mass differences of unassigned peaks and peptide modifications are explored. The investigation of the found patterns illustrates that meaningful patterns can be discovered that can be explained by features of the employed technology and found modifications. This simple approach offers opportunities to monitor accumulating unexplained mass spectrometry data for emerging new patterns, with possible applications for the development of mass exclusion lists, for the refinement of quality control strategies and for a further interpretation of unexplained spectral peaks in mass spectrometry and tandem mass spectrometry.

Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies.

PubMed

Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie

2013-01-16

The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields significantly better results. Before applying text-mining techniques, one must pay careful attention to the structure of the analyzed corpora. While the importance of data cleaning has been known for low-level text characteristics (e.g., encoding and spelling), high-level and difficult-to-quantify corpus characteristics, such as naturally occurring redundancy, can also hurt text mining. Fingerprinting enables text-mining techniques to leverage available data in the EHR corpus, while avoiding the bias introduced by redundancy.

SEASONAL VARIATIONS OF DISSOLVED MERCURY CONCENTRATIONS AT THE SULPHUR BANK MERCURY MINE, CLEAR LAKE, CALIFORNIA: IMPLICATIONS FOR MINE DRAINAGE MONITORING

EPA Science Inventory

The Sulphur Bank Mercury Mine in Lake County, California (SBMM) was operated from the 1860s through the 1950s. Mining for sulfur started with surface operations and then progressed to shaft and later open pit techniques to obtain mercury. SBMM is located adjacent to the shore o...

Data Mining in Social Media

NASA Astrophysics Data System (ADS)

Barbier, Geoffrey; Liu, Huan

The rise of online social media is providing a wealth of social network data. Data mining techniques provide researchers and practitioners the tools needed to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.

HYPGEO - A collaboration between geophysics and remote sensing for mineral exploration

NASA Astrophysics Data System (ADS)

Meyer, Uwe; Frei, Michaela; Petersen, Hauke; Papenfuß, Anne; Ibs-von Seht, Malte; Stolz, Ronny; Queitsch, Matthias; Buchholz, Peter; Siemon, Bernhard

2017-04-01

The German Federal Institute for Geosciences and Natural Resources (BGR) aims to promote and design application oriented, generic techniques for the detection and 3D-characterisation of mineral deposits. Most newly developed mineral mining structures are still exploiting near surface sources. Since exploration and exploitation of mineral resources are increasingly under public review concerning environmental issues and social acceptance, non-invasive methods using satellites, fixed-wing aircraft, helicopters or unmanned aerial vehicles are preferred techniques within this investigation. Therefore, a data combination of helicopter-borne gamma ray spectrometry, hyperspectral imagery and full tensor gradient magnetometry is being evaluated. Test areas are open pit mining structures in Aznalcollar and Tharsis within the Pyrite Belt of southern Spain. First test flights using gamma-ray spectrometry and gradient magnetometry using SQUID-based sensors have been performed. Hyperspectral imagery has been applied on ground. Rock and core samples from the mines have been taken or investigated for further analysis. The basic idea is to combine surface triggered signals from gamma-ray spectrometry and hyperspectral imagery to enhance the detection of shallow mineralisation structures. In order to investigate whether these structures are connected with near-surface ore veins, gradient magnetometry was applied to model subsurface formations. To verify that good correlations between the applied methods are given, open pit mining structures were chosen, where the mineral content and the local to regional geology is well known.

ALES: An Innovative Argument-Learning Environment

ERIC Educational Resources Information Center

Abbas, Safia; Sawamura, Hajime

2010-01-01

This paper presents the development of an Argument-Learning System (ALES). The idea is based on the AIF (argumentation interchange format) ontology using "Walton theory". ALES uses different mining techniques to manage a highly structured arguments repository. This repository was designed, developed and implemented by the authors. The aim is to…

Mitigating Impacts Of Arsenic Contaminated Materials Via Two (2) Stabilization Methods Based On Polymeric And Cement Binders

EPA Science Inventory

The primary objective of this study was to evaluate the performance of two selected chemical stabilization and solidification (S/S) techniques to treat three types of arsenic-contaminated wastes 1) chromated copper arsenate (CCA) wood treater waste, 2) La Trinidad Mine tailings, ...

Tree-based approach for exploring marine spatial patterns with raster datasets.

PubMed

Liao, Xiaohan; Xue, Cunjin; Su, Fenzhen

2017-01-01

From multiple raster datasets to spatial association patterns, the data-mining technique is divided into three subtasks, i.e., raster dataset pretreatment, mining algorithm design, and spatial pattern exploration from the mining results. Comparison with the former two subtasks reveals that the latter remains unresolved. Confronted with the interrelated marine environmental parameters, we propose a Tree-based Approach for eXploring Marine Spatial Patterns with multiple raster datasets called TAXMarSP, which includes two models. One is the Tree-based Cascading Organization Model (TCOM), and the other is the Spatial Neighborhood-based CAlculation Model (SNCAM). TCOM designs the "Spatial node→Pattern node" from top to bottom layers to store the table-formatted frequent patterns. Together with TCOM, SNCAM considers the spatial neighborhood contributions to calculate the pattern-matching degree between the specified marine parameters and the table-formatted frequent patterns and then explores the marine spatial patterns. Using the prevalent quantification Apriori algorithm and a real remote sensing dataset from January 1998 to December 2014, a successful application of TAXMarSP to marine spatial patterns in the Pacific Ocean is described, and the obtained marine spatial patterns present not only the well-known but also new patterns to Earth scientists.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

PubMed

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

PubMed Central

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

A novel method for predicting kidney stone type using ensemble learning.

PubMed

Kazemi, Yassaman; Mirroshandel, Seyed Abolghasem

2018-01-01

The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the world. Advanced data mining techniques such as classification can help in the early prediction of this disease and reduce its incidence and associated costs. The objective of the present study is to derive a model for the early detection of the type of kidney stone and the most influential parameters with the aim of providing a decision-support system. Information was collected from 936 patients with nephrolithiasis at the kidney center of the Razi Hospital in Rasht from 2012 through 2016. The prepared dataset included 42 features. Data pre-processing was the first step toward extracting the relevant features. The collected data was analyzed with Weka software, and various data mining models were used to prepare a predictive model. Various data mining algorithms such as the Bayesian model, different types of Decision Trees, Artificial Neural Networks, and Rule-based classifiers were used in these models. We also proposed four models based on ensemble learning to improve the accuracy of each learning algorithm. In addition, a novel technique for combining individual classifiers in ensemble learning was proposed. In this technique, for each individual classifier, a weight is assigned based on our proposed genetic algorithm based method. The generated knowledge was evaluated using a 10-fold cross-validation technique based on standard measures. However, the assessment of each feature for building a predictive model was another significant challenge. The predictive strength of each feature for creating a reproducible outcome was also investigated. Regarding the applied models, parameters such as sex, acid uric condition, calcium level, hypertension, diabetes, nausea and vomiting, flank pain, and urinary tract infection (UTI) were the most vital parameters for predicting the chance of nephrolithiasis. The final ensemble-based model (with an accuracy of 97.1%) was a robust one and could be safely applied to future studies to predict the chances of developing nephrolithiasis. This model provides a novel way to study stone disease by deciphering the complex interaction among different biological variables, thus helping in an early identification and reduction in diagnosis time. Copyright © 2017 Elsevier B.V. All rights reserved.

Physics Mining of Multi-Source Data Sets

NASA Technical Reports Server (NTRS)

Helly, John; Karimabadi, Homa; Sipes, Tamara

2012-01-01

Powerful new parallel data mining algorithms can produce diagnostic and prognostic numerical models and analyses from observational data. These techniques yield higher-resolution measures than ever before of environmental parameters by fusing synoptic imagery and time-series measurements. These techniques are general and relevant to observational data, including raster, vector, and scalar, and can be applied in all Earth- and environmental science domains. Because they can be highly automated and are parallel, they scale to large spatial domains and are well suited to change and gap detection. This makes it possible to analyze spatial and temporal gaps in information, and facilitates within-mission replanning to optimize the allocation of observational resources. The basis of the innovation is the extension of a recently developed set of algorithms packaged into MineTool to multi-variate time-series data. MineTool is unique in that it automates the various steps of the data mining process, thus making it amenable to autonomous analysis of large data sets. Unlike techniques such as Artificial Neural Nets, which yield a blackbox solution, MineTool's outcome is always an analytical model in parametric form that expresses the output in terms of the input variables. This has the advantage that the derived equation can then be used to gain insight into the physical relevance and relative importance of the parameters and coefficients in the model. This is referred to as physics-mining of data. The capabilities of MineTool are extended to include both supervised and unsupervised algorithms, handle multi-type data sets, and parallelize it.

Science for watershed decisions on abandoned mine lands; review of preliminary results, Denver, Colorado, February 4-5, 1998

USGS Publications Warehouse

Nimick, David A.; Von Guerard, Paul

1998-01-01

From the Preface: There are thousands of abandoned or inactive mines on or adjacent to public lands administered by the U.S. Forest Service, Bureau of Land Management, and National Park Service. Mine wastes from many of these abandoned mines adversely affect resources on public lands. In 1995, an interdepartmental work group within the Federal government developed a strategy to address remediation of the many abandoned mines on public lands. This strategy is based on using a watershed approach to address the abandoned mine lands (AML) problem. The USGS, working closely with the Federal land-management agencies (FLMAs), is key for the success of this watershed approach. In support of this watershed approach, the USGS developed an AML Initiative with pilot studies in the Boulder River in Montana and the Animas River in Colorado. The goal of these studies is to design and implement a reliable strategy that will supply the scientific information to the FLMAs so that land managers can develop efficient and cost-effective remediation of AML. The symposium 'Science for Watershed Decisions on Abandoned Mine Lands: Review of Preliminary Results' held in Denver, Colorado, on February 4-5, 1998, provided the FLMAs a first look at the techniques, data, and interpretations being generated by the USGS pilot studies. This multidisciplined effort already is proving very valuable to land managers in making science-based AML cleanup decisions and will continue to be of increasing value as additional and more complete information is obtained. Ongoing interaction between scientists and land managers is essential to insure the efficient continuation and success of AML cleanup efforts.

Uranium aqueous speciation in the vicinity of the former uranium mining sites using the diffusive gradients in thin films and ultrafiltration techniques.

PubMed

Drozdzak, Jagoda; Leermakers, Martine; Gao, Yue; Elskens, Marc; Phrommavanh, Vannapha; Descostes, Michael

2016-03-24

The performance of the Diffusive Gradients in Thin films (DGT) technique with Chelex(®)-100, Metsorb™ and Diphonix(®) as binding phases was evaluated in the vicinity of the former uranium mining sites of Chardon and L'Ecarpière (Loire-Atlantique department in western France). This is the first time that the DGT technique with three different binding agents was employed for the aqueous U determination in the context of uranium mining environments. The fractionation and speciation of uranium were investigated using a multi-methodological approach using filtration (0.45 μm, 0.2 μm), ultrafiltration (500 kDa, 100 kDa and 10 kDa) coupled to geochemical speciation modelling (PhreeQC) and the DGT technique. The ultrafiltration data showed that at each sampling point uranium was present mostly in the 10 kDa truly dissolved fraction and the geochemical modelling speciation calculations indicated that U speciation was markedly predominated by CaUO2(CO3)3(2-). In natural waters, no significant difference was observed in terms of U uptake between Chelex(®)-100 and Metsorb™, while similar or inferior U uptake was observed on Diphonix(®) resin. In turn, at mining influenced sampling spots, the U accumulation on DGT-Diphonix(®) was higher than on DGT-Chelex(®)-100 and DGT-Metsorb™, probably because their performance was disturbed by the extreme composition of the mining waters. The use of Diphonix(®) resin leads to a significant advance in the application and development of the DGT technique for determination of U in mining influenced environments. This investigation demonstrated that such multi-technique approach provides a better picture of U speciation and enables to assess more accurately the potentially bioavailable U pool. Copyright © 2016 Elsevier B.V. All rights reserved.

Data Mining: The Art of Automated Knowledge Extraction

NASA Astrophysics Data System (ADS)

Karimabadi, H.; Sipes, T.

2012-12-01

Data mining algorithms are used routinely in a wide variety of fields and they are gaining adoption in sciences. The realities of real world data analysis are that (a) data has flaws, and (b) the models and assumptions that we bring to the data are inevitably flawed, and/or biased and misspecified in some way. Data mining can improve data analysis by detecting anomalies in the data, check for consistency of the user model assumptions, and decipher complex patterns and relationships that would not be possible otherwise. The common form of data collected from in situ spacecraft measurements is multi-variate time series which represents one of the most challenging problems in data mining. We have successfully developed algorithms to deal with such data and have extended the algorithms to handle streaming data. In this talk, we illustrate the utility of our algorithms through several examples including automated detection of reconnection exhausts in the solar wind and flux ropes in the magnetotail. We also show examples from successful applications of our technique to analysis of 3D kinetic simulations. With an eye to the future, we provide an overview of our upcoming plans that include collaborative data mining, expert outsourcing data mining, computer vision for image analysis, among others. Finally, we discuss the integration of data mining algorithms with web-based services such as VxOs and other Heliophysics data centers and the resulting capabilities that it would enable.

Isotope biogeochemical assessment of natural biodegradation processes in open cast pit mining landscapes

NASA Astrophysics Data System (ADS)

Jeschke, Christina; Knöller, Kay; Koschorreck, Matthias; Ussath, Maria; Hoth, Nils

2014-05-01

In Germany, a major share of the energy production is based on the burning of lignite from open cast pit mines. The remediation and re-cultivation of the former mining areas in the Lusatian and Central German lignite mining district is an enormous technical and economical challenge. After mine closures, the surrounding landscapes are threatened by acid mine drainage (AMD), i.e. the acidification and mineralization of rising groundwater with metals and inorganic contaminants. The high content of sulfur (sulfuric acid, sulfate), nitrogen (ammonium) and iron compounds (iron-hydroxides) deteriorates the groundwater quality and decelerates sustainable development of tourism in (former) mining landscapes. Natural biodegradation or attenuation (NA) processes of inorganic contaminants are considered to be a technically low impact and an economically beneficial solution. The investigations of the stable isotope compositions of compounds involved in NA processes helps clarify the dynamics of natural degradation and provides specific informations on retention processes of sulfate and nitrogen-compounds in mine dump water, mine dump sediment, and residual pit lakes. In an active mine dump we investigated zones where the process of bacterial sulfate reduction, as one very important NA process, takes place and how NA can be enhanced by injecting reactive substrates. Stable isotopes signatures of sulfur and nitrogen components were examined and evaluated in concert with hydrogeochemical data. In addition, we delineated the sources of ammonium pollution in mine dump sediments and investigated nitrification by 15N-labeling techniques to calculate the limit of the conversion of harmful ammonium to nitrate in residual mining lakes. Ultimately, we provided an isotope biogeochemical assessment of natural attenuation of sulfate and ammonium at mine dump sites and mining lakes. Also, we estimated the risk potential for water in different compartments of the hydrological system. In laboratory experiments, we tested reactive materials that may speed up the process of bacterial sulfate reduction. In in-situ experiments, we quantified nitrification rates. Based on the results, we are able to suggest promising technical measures that enhance natural attenuation processes at mine dump site and in mining lakes. The natural water cycle in lignite mining landscapes is heavily impacted by human activities. Basically, nature is capable of cleaning itself to a certain extent after mining activities stopped. However, it is our responsibility to support biogeochemical processes to make them more efficient and more sustainable. Isotopic monitoring proved to be an excellent tool for assessing the relevance and performance of different re-cultivation measures for a positive long-term development of the water quality in large-scale aquatic systems affected by the impact of lignite mining.

Blasting methods for heterogeneous rocks in hillside open-pit mines with high and steep slopes

NASA Astrophysics Data System (ADS)

Chen, Y. J.; Chang, Z. G.; Chao, X. H.; Zhao, J. F.

2017-06-01

In the arid desert areas in Xinjiang, most limestone quarries are hillside open-pit mines (OPMs) where the limestone is hard, heterogeneous, and fractured, and can be easily broken into large blocks by blasting. This study tried to find effective technical methods for blasting heterogeneous rocks in such quarries based on an investigation into existing problems encountered in actual mining at Hongshun Limestone Quarry in Xinjiang. This study provided blasting schemes for hillside OPMs with different heights and slopes. These schemes involve the use of vertical deep holes, oblique shallow holes, and downslope hole-by-hole sublevel or simultaneous detonation techniques. In each bench, the detonations of holes in a detonation unit occur at intervals of 25-50 milliseconds. The research findings can offer technical guidance on how to blast heterogeneous rocks in hillside limestone quarries.

Characterization of Uranium Ore Concentrate Chemical Composition via Raman Spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Su, Yin-Fong; Tonkyn, Russell G.; Sweet, Lucas E.

Uranium Ore Concentrate (UOC, often called yellowcake) is a generic term that describes the initial product resulting from the mining and subsequent milling of uranium ores en route to production of the U-compounds used in the fuel cycle. Depending on the mine, the ore, the chemical process, and the treatment parameters, UOC composition can vary greatly. With the recent advent of handheld spectrometers, we have chosen to investigate whether either commercial off-the-shelf (COTS) handheld devices or laboratory-grade Raman instruments might be able to i) identify UOC materials, and ii) differentiate UOC samples based on chemical composition and thus suggest themore » mining or milling process. Twenty-eight UOC samples were analyzed via FT-Raman spectroscopy using both 1064 nm and 785 nm excitation wavelengths. These data were also compared with results from a newly developed handheld COTS Raman spectrometer using a technique that lowers background fluorescence signal. Initial chemometric analysis was able to differentiate UOC samples based on mine location. Additional compositional information was obtained from the samples by performing XRD analysis on a subset of samples. The compositional information was integrated with chemometric analysis of the spectroscopic dataset allowing confirmation that class identification is possible based on compositional differences between the UOC samples, typically involving species such as U3O8, α-UO2(OH)2, UO4•2H2O (metastudtite), K(UO2)2O3, etc. While there are clearly excitation λ sensitivities, especially for dark samples, Raman analysis coupled with chemometric data treatment can nicely differentiate UOC samples based on composition and even mine origin.« less

Process Mining Online Assessment Data

ERIC Educational Resources Information Center

Pechenizkiy, Mykola; Trcka, Nikola; Vasilyeva, Ekaterina; van der Aalst, Wil; De Bra, Paul

2009-01-01

Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for…

Analysis of the current rib support practices and techniques in U.S. coal mines

PubMed Central

Mohamed, Khaled M.; Murphy, Michael M.; Lawson, Heather E.; Klemetti, Ted

2016-01-01

Design of rib support systems in U.S. coal mines is based primarily on local practices and experience. A better understanding of current rib support practices in U.S. coal mines is crucial for developing a sound engineering rib support design tool. The objective of this paper is to analyze the current practices of rib control in U.S. coal mines. Twenty underground coal mines were studied representing various coal basins, coal seams, geology, loading conditions, and rib control strategies. The key findings are: (1) any rib design guideline or tool should take into account external rib support as well as internal bolting; (2) rib bolts on their own cannot contain rib spall, especially in soft ribs subjected to significant load—external rib control devices such as mesh are required in such cases to contain rib sloughing; (3) the majority of the studied mines follow the overburden depth and entry height thresholds recommended by the Program Information Bulletin 11-29 issued by the Mine Safety and Health Administration; (4) potential rib instability occurred when certain geological features prevailed—these include draw slate and/or bone coal near the rib/roof line, claystone partings, and soft coal bench overlain by rock strata; (5) 47% of the studied rib spall was classified as blocky—this could indicate a high potential of rib hazards; and (6) rib injury rates of the studied mines for the last three years emphasize the need for more rib control management for mines operating at overburden depths between 152.4 m and 304.8 m. PMID:27648341

Study on online community user motif using web usage mining

NASA Astrophysics Data System (ADS)

Alphy, Meera; Sharma, Ajay

2016-04-01

The Web usage mining is the application of data mining, which is used to extract useful information from the online community. The World Wide Web contains at least 4.73 billion pages according to Indexed Web and it contains at least 228.52 million pages according Dutch Indexed web on 6th august 2015, Thursday. It’s difficult to get needed data from these billions of web pages in World Wide Web. Here is the importance of web usage mining. Personalizing the search engine helps the web user to identify the most used data in an easy way. It reduces the time consumption; automatic site search and automatic restore the useful sites. This study represents the old techniques to latest techniques used in pattern discovery and analysis in web usage mining from 1996 to 2015. Analyzing user motif helps in the improvement of business, e-commerce, personalisation and improvement of websites.

Exploring patterns of epigenetic information with data mining techniques.

PubMed

Aguiar-Pulido, Vanessa; Seoane, José A; Gestal, Marcos; Dorado, Julián

2013-01-01

Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.

Mississippi State University Center for Air Sea Technology. FY93 and FY 94 Research Program in Navy Ocean Modeling and Prediction

DTIC Science & Technology

1994-09-30

relational versus object oriented DBMS, knowledge discovery, data models, rnetadata, data filtering, clustering techniques, and synthetic data. A secondary...The first was the investigation of Al/ES Lapplications (knowledge discovery, data mining, and clustering ). Here CAST collabo.rated with Dr. Fred Petry...knowledge discovery system based on clustering techniques; implemented an on-line data browser to the DBMS; completed preliminary efforts to apply object

Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data.

PubMed

Luna, Jose Maria; Padillo, Francisco; Pechenizkiy, Mykola; Ventura, Sebastian

2017-09-27

Pattern mining is one of the most important tasks to extract meaningful and useful information from raw data. This task aims to extract item-sets that represent any type of homogeneity and regularity in data. Although many efficient algorithms have been developed in this regard, the growing interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper is to propose new efficient pattern mining algorithms to work in big data. To this aim, a series of algorithms based on the MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be divided into three main groups. First, two algorithms [Apriori MapReduce (AprioriMR) and iterative AprioriMR] with no pruning strategy are proposed, which extract any existing item-set in data. Second, two algorithms (space pruning AprioriMR and top AprioriMR) that prune the search space by means of the well-known anti-monotone property are proposed. Finally, a last algorithm (maximal AprioriMR) is also proposed for mining condensed representations of frequent patterns. To test the performance of the proposed algorithms, a varied collection of big data datasets have been considered, comprising up to 3 · 10#x00B9;⁸ transactions and more than 5 million of distinct single-items. The experimental stage includes comparisons against highly efficient and well-known pattern mining algorithms. Results reveal the interest of applying MapReduce versions when complex problems are considered, and also the unsuitability of this paradigm when dealing with small data.

Segmentation of fluorescence microscopy cell images using unsupervised mining.

PubMed

Du, Xian; Dua, Sumeet

2010-05-28

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

The application of a novel high-resolution mass spectrometry-based analytical strategy to rapid metabolite profiling of a dual drug combination in humans.

PubMed

Xing, Jie; Zang, Meitong; Liu, Huixiang

2017-11-15

Metabolite profiling of combination drugs in complex matrix is a big challenge. Development of an effective data mining technique for simultaneously extracting metabolites of one parent drug from both background matrix and combined drug-related signals could be a solution. This study presented a novel high resolution mass spectrometry (HRMS)-based data-mining strategy to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was verapamil-irbesartan (VER-IRB), which is widely used in clinic to treat hypertension. First, mass defect filter (MDF), as a targeted data mining tool, worked effectively except for those metabolites with similar MDF values. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, was able to recover all relevant metabolites of VER-IRB from the full-scan MS dataset except for trace metabolites buried in the background noise and/or combined drug-related signals. Third, the novel ring double bond (RDB; valence values of elements in structure) filter, could show rich structural information in more sensitive full-scan MS chromatograms; however, it had a low capability to remove background noise and was difficult to differentiate the metabolites with RDB coverage. Fourth, an integrated strategy, i.e., untargeted BS followed by RDB, was effective for metabolite identification of VER and IRB, which have different RDB values. Majority of matrix signals were firstly removed using BS. Metabolite ions for each parent drug were then isolated from remaining background matrix and combined drug-related signals by imposing of preset RDB values/ranges around the parent drug and selected core substructures. In parallel, MDF was used to recover potential metabolites with similar RDB. As a result, a total of 74 metabolites were found for VER-IRB in human plasma and urine, among which ten metabolites have not been previously reported in human. The results demonstrated that the combination of accurate mass-based multiple data-mining techniques, i.e., untargeted background subtraction followed by ring double bond filtering in parallel with targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drug. Copyright © 2017 Elsevier B.V. All rights reserved.

Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

ERIC Educational Resources Information Center

Hung, Jui-Long; Zhang, Ke

2012-01-01

This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

Restoration of tropical moist forest on bauxite mined lands in the Brazilian Amazon

Treesearch

John A Parrotta; Oliver H. Knowles

1999-01-01

We evaluated forest structure and composition in 9- to 13-year-old stands established on a bauxite-mined site at Trombetas (Pará), Brazil, using four different reforestation techniques following initial site preparation and topsoil replacement. These techniques included reliance on natural forest regeneration, mixed commercial species plantings of mostly exotic timber...

Use of data mining to predict significant factors and benefits of bilateral cochlear implantation.

PubMed

Ramos-Miguel, Angel; Perez-Zaballos, Teresa; Perez, Daniel; Falconb, Juan Carlos; Ramosb, Angel

2015-11-01

Data mining (DM) is a technique used to discover pattern and knowledge from a big amount of data. It uses artificial intelligence, automatic learning, statistics, databases, etc. In this study, DM was successfully used as a predictive tool to assess disyllabic speech test performance in bilateral implanted patients with a success rate above 90%. 60 bilateral sequentially implanted adult patients were included in the study. The DM algorithms developed found correlations between unilateral medical records and Audiological test results and bilateral performance by establishing relevant variables based on two DM techniques: the classifier and the estimation. The nearest neighbor algorithm was implemented in the first case, and the linear regression in the second. The results showed that patients with unilateral disyllabic test results below 70% benefited the most from a bilateral implantation. Finally, it was observed that its benefits decrease as the inter-implant time increases.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways.

PubMed

Saidi, Rabie; Boudellioua, Imane; Martin, Maria J; Solovyev, Victor

2017-01-01

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition.

PubMed

Sayago, Ana; González-Domínguez, Raúl; Beltrán, Rafael; Fernández-Recamales, Ángeles

2018-09-30

This work explores the potential of multi-element fingerprinting in combination with advanced data mining strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several unsupervised and supervised multivariate statistical techniques were used to build classification models and investigate the relationship between mineral composition of olive oils and their provenance. Results showed that Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated on the basis of their origin in accordance with three geographical areas: Atlantic coast (Huelva province), Mediterranean coast and inland regions. Furthermore, statistical modelling yielded high sensitivity and specificity, principally when random forest and support vector machines were employed, thus demonstrating the utility of these techniques in food traceability and authenticity research. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Evaluation of Text Mining Tools as Applied to Selected Scientific and Engineering Literature.

ERIC Educational Resources Information Center

Trybula, Walter J.; Wyllys, Ronald E.

2000-01-01

Addresses an approach to the discovery of scientific knowledge through an examination of data mining and text mining techniques. Presents the results of experiments that investigated knowledge acquisition from a selected set of technical documents by domain experts. (Contains 15 references.) (Author/LRW)

Illustrated surface mining methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1979-01-01

This manual provides a visual synopsis of surface coal mining methods in the United States. The manual presents various surface mining methods and techniques through artist renderings and appropriate descriptions. The productive coal fields of the United States were divided into four regions according to geology and physiography. A glossay of terminology is included. (DP)

Analyzing Student Inquiry Data Using Process Discovery and Sequence Classification

ERIC Educational Resources Information Center

Emond, Bruno; Buffett, Scott

2015-01-01

This paper reports on results of applying process discovery mining and sequence classification mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and…

General geology and mines of the East Tintic mining district, Utah and Juab counties, Utah, with sections on the geology of the Burgin mine and the geology of the Trixie mine

USGS Publications Warehouse

Morris, H.T.; Lovering, Thomas Seward; Mogensen, A.P.; Shepard, W.M.; Perry, L.I.; Smith, S.M.

1979-01-01

This report is a study of the rocks, geologic structures, and mines of a highly productive silver, gold, and base-metal mining district in the east-central Great Basin. The East Tintic mining district is in the east-central part of the East Tintic Mountains, near the east margin of the Basin and Range province in Utah and Juab Counties, Utah. The district occupies the northeastern part of the Eureka quadrangle and is about 5 mi (8 km) wide and 6 mi (9.7 km) long. Officially it is within the designated boundaries of the Tintic mining district, but it generally though erroneously has been regarded as a separate district since the late 1800's.Prospecting was first undertaken in East Tintic in 1870; although small quantities of ore were produced in 1899 and from 1909 to 1913, the district first achieved prominence in 1916 with the discovery of the totally concealed Central ore body of the Tintic Standard mine. Within a few years of this discovery, the Tintic Standard became one of the most productive silver mines in the world. Additional discoveries of important concealed ore deposits have continued to be made in the district, including the North Lily mine in 1927, the Eureka Lilly and Eureka Standard mines in 1928, the Burgin mine in 1958, and the Trixie mine in 1969.To December 31, 1975, the East Tintic mining district has yielded approximately 4.83 million short tons (4.38 million tonnes) of silver, gold, and base-metal ores, largely from concealed deposits overlain by many hundreds of feet of barren rocks. These ores have a gross valuation of approximately $231 million. The district first achieved prominence in 1916 with the discovery of the ore bodies of the Tintic Standard mine, which for a time was the world's richest silver producer (Lindgren, 1933, p. 588). By 1946 this deposit and a number of other deposits discovered and developed nearby had been exhausted, and the district became dormant. A dramatic revival of, mining activities in the East Tintic district began in 1956 after the discovery and subsequent development of the concealed Burgin ore bodies in an area 1 mile (1.6 km) southeast of the Tintic Standard that previously had been only superficially prospected. As in the earlier history of the district, the Burgin development has led to the discovery of other concealed deposits, focusing international attention on the revitalization of a nearly abandoned mining district by the application of geologic and geochemical techniques.

Mining subspace clusters from DNA microarray data using large itemset techniques.

PubMed

Chang, Ye-In; Chen, Jiun-Rung; Tsai, Yueh-Chi

2009-05-01

Mining subspace clusters from the DNA microarrays could help researchers identify those genes which commonly contribute to a disease, where a subspace cluster indicates a subset of genes whose expression levels are similar under a subset of conditions. Since in a DNA microarray, the number of genes is far larger than the number of conditions, those previous proposed algorithms which compute the maximum dimension sets (MDSs) for any two genes will take a long time to mine subspace clusters. In this article, we propose the Large Itemset-Based Clustering (LISC) algorithm for mining subspace clusters. Instead of constructing MDSs for any two genes, we construct only MDSs for any two conditions. Then, we transform the task of finding the maximal possible gene sets into the problem of mining large itemsets from the condition-pair MDSs. Since we are only interested in those subspace clusters with gene sets as large as possible, it is desirable to pay attention to those gene sets which have reasonable large support values in the condition-pair MDSs. From our simulation results, we show that the proposed algorithm needs shorter processing time than those previous proposed algorithms which need to construct gene-pair MDSs.

Sensitive test for sea mine identification based on polarization-aided image processing.

PubMed

Leonard, I; Alfalou, A; Brosseau, C

2013-12-02

Techniques are widely sought to detect and identify sea mines. This issue is characterized by complicated mine shapes and underwater light propagation dependencies. In a preliminary study we use a preprocessing step for denoising underwater images before applying the algorithm for mine detection. Once a mine is detected, the protocol for identifying it is activated. Among many correlation filters, we have focused our attention on the asymmetric segmented phase-only filter for quantifying the recognition rate because it allows us to significantly increase the number of reference images in the fabrication of this filter. Yet they are not entirely satisfactory in terms of recognition rate and the obtained images revealed to be of low quality. In this report, we propose a way to improve upon this preliminary study by using a single wavelength polarimetric camera in order to denoise the images. This permits us to enhance images and improve depth visibility. We present illustrative results using in situ polarization imaging of a target through a milk-water mixture and demonstrate that our challenging objective of increasing the detection rate and decreasing the false alarm rate has been achieved.

Geomechanical analysis applied to geological carbon dioxide sequestration, induced seismicity in deep mines, and detection of stress-induced velocity anisotropy in sub-salt environments

NASA Astrophysics Data System (ADS)

Lucier, Amie Marie

The role of geomechanical analysis in characterizing the feasibility of CO2 sequestration in deep saline aquifers is addressed in two investigations. The first investigation was completed as part of the Ohio River Valley CO2 Storage Project. We completed a geomechanical analysis of the Rose Run Sandstone, a potential injection zone, and its adjacent formations at the American Electric Power's 1.3 GW Mountaineer Power Plant in New Haven, West Virginia. The results of this analysis were then used to evaluate the feasibility of anthropogenic CO2 sequestration in the potential injection zone. First, we incorporated the results of the geomechanical analysis with a geostatistical aquifer model in CO2 injection flow simulations to test the effects of introducing a hydraulic fracture to increase injectivity. Then, we determined that horizontal injection wells at the Mountaineer site are feasible because the high rock strength ensures that such wells would be stable in the local stress state. Finally, we evaluated the potential for injection-induced seismicity. The second investigation concerning CO2 sequestration was motivated by the modeling and fluid flow simulation results from the first study. The geomechanics-based assessment workflow follows a bottom-up approach for evaluating regional deep saline aquifer CO2 injection and storage feasibility. The CO2 storage capacity of an aquifer is a function of its porous volume as well as its CO2 injectivity. For a saline aquifer to be considered feasible in this assessment it must be able to store a specified amount of CO2 at a reasonable cost per ton of CO 2. The proposed assessment workflow has seven steps. The workflow was applied to a case study of the Rose Run sandstone in the eastern Ohio River Valley. We found that it is feasible in this region to inject and store 113 Mt CO2/yr for 30 years at an associated well cost of less than 1.31 US$/t CO2, but only if injectivity enhancement techniques such as hydraulic fracturing and injection induced micro-seismicity are implemented. The second issue to which we apply geomechanical analysis in this thesis is mining-induced stress perturbations and induced seismicity in the TauTona gold mine, which is located in the Witwatersrand Basin of South Africa and is one of the deepest underground mines in the world. In the first investigation, we developed and tested a new technique for determining the virgin stress state near the TauTona gold mine. This technique follows an iterative forward modeling approach that combines observations of drilling induced borehole failures in borehole images, boundary element modeling of the mining-induced stress perturbations, and forward modeling of borehole failures based on the results of the boundary element modeling. The final result was a well constrained range of principal stress orientations and magnitudes that are consistent with all the observed failures and other stress indicators. In the second investigation, we used this constrained stress state to examine the likelihood of faulting to occur both on pre-existing fault planes that are optimally oriented to the virgin stress state and on faults affected by the mining-perturbed stress field, the latter of which is calculated with boundary element modeling. We made several recommendations that could potentially increase safety in deep South African mines as development continues. Finally, the third issue addressed in this thesis is the detection of stress-induced shear wave velocity anisotropy in a sub-salt environment. In this study, we tested a technique proposed by Boness and Zoback (2006) to identify structure-induced velocity anisotropy and isolate possible stress-induced velocity anisotropy. The investigation used cross-dipole sonic data from three deep water sub-salt wells in the Gulf of Mexico. First, we determined the parameters necessary to ensure the quality of the fast azimuth data used in our analysis. We then characterized the quality controlled measured fast directions as either structure-induced or stress-induced based on the results of the Boness and Zoback (2006) technique. We found that this technique supplements the use of dispersion curve analysis for characterizing anisotropy mechanisms. We also find that this technique has the potential to provide information on the stresses that can be used to validate numerical models of salt-related stress perturbations. (Abstract shortened by UMI.)

Haneş and Valea Vinului (Romania) closed mines Acid Mine Drainages (AMDs)--actual condition and passive treatment remediation proposal.

PubMed

Măicăneanu, Andrada; Bedelean, Horea; Ardelean, Marius; Burcă, Silvia; Stanca, Maria

2013-10-01

Acid Mine Drainages (AMDs) from Haneş and Valea Vinului (Romania) closed mines were considered for characterization and treatment using a local zeolitic volcanic tuff, ZVT, (Măcicaş, Cluj County, Romania). Water samples were collected from two locations, before and after discharging point in case of Haneş mine, and on three horizons in case of Valea Vinului mine. Physico-chemical (pH, total solid, heavy metal ions concentration) analyses showed that the environment is strongly affected by these AMD discharges even if the mines were closed years ago. Iron, manganese and zinc were the main pollutants identified in Haneş mine AMD, while zinc is the one mainly present in case of Valea Vinului AMD. A batch technique (no stirring) in which the ZVT was put in contact with the AMD sample was proposed as a passive remediation technique. ZVT successfully remove heavy metal ion from AMD. According to heavy metal ion concentrations, removal efficiencies are reaching 100%, varying as follows, Fe(2+)>Zn(2+)>Mn(2+). When the ZVT was compared with two cationic resins (strong, SAR and weak acid, WAR) the following series was depicted, SAR>ZVT>WAR. Copyright © 2013 Elsevier Ltd. All rights reserved.

Review of Mobile Learning Trends 2010-2015: A Meta-Analysis

ERIC Educational Resources Information Center

Chee, Ken Nee; Yahaya, Noraffandy; Ibrahim, Nor Hasniza; Hasan, Mohamed Noor

2017-01-01

This study examined the longitudinal trends of mobile learning (M-Learning) research using text mining techniques in a more comprehensive manner. One hundred and forty four (144) refereed journal articles were retrieved and analyzed from the Social Science Citation Index database selected from top six major educational technology-based learning…

Supporting read-across predictions of chemical toxicity using high-throughput text-mining (ACS 2017 Spring meeting )

EPA Science Inventory

Read-across is a technique used to fill data gaps within chemical safety assessments. It is based on the premise that chemicals with similar structures are likely to have similar biological activities. Known information on the property of a chemical (source) is used to make a pre...

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

PKDE4J: Entity and relation extraction for public knowledge discovery.

PubMed

Song, Min; Kim, Won Chul; Lee, Dahee; Heo, Go Eun; Kang, Keun Young

2015-10-01

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction. Copyright © 2015 Elsevier Inc. All rights reserved.

Petascale Kinetic Simulations in Space Sciences: New Simulations and Data Discovery Techniques and Physics Results

NASA Astrophysics Data System (ADS)

Karimabadi, Homa

2012-03-01

Recent advances in simulation technology and hardware are enabling breakthrough science where many longstanding problems can now be addressed for the first time. In this talk, we focus on kinetic simulations of the Earth's magnetosphere and magnetic reconnection process which is the key mechanism that breaks the protective shield of the Earth's dipole field, allowing the solar wind to enter the Earth's magnetosphere. This leads to the so-called space weather where storms on the Sun can affect space-borne and ground-based technological systems on Earth. The talk will consist of three parts: (a) overview of a new multi-scale simulation technique where each computational grid is updated based on its own unique timestep, (b) Presentation of a new approach to data analysis that we refer to as Physics Mining which entails combining data mining and computer vision algorithms with scientific visualization to extract physics from the resulting massive data sets. (c) Presentation of several recent discoveries in studies of space plasmas including the role of vortex formation and resulting turbulence in magnetized plasmas.

Knowledge Discovery in Medical Mining by using Genetic Algorithms and Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Srivathsa, P. K.

2011-12-01

Medical Data mining could be thought of as the search for relationships and patterns within the medical data, which facilitates the acquisition of useful knowledge for effective medical diagnosis. Consequently, the predictability of disease will become more effective and the early detection of disease certainly facilitates an increased exposure to required patient care with focused treatment, economic feasibility and improved cure rates. So, the present investigation is carried on medical data(PIMA) using DM and GA based Neural Network technique and the results predict that the methodology is not only reliable but also helps in furthering the scope of the subject.

Identification of mine rescue equipment reduction gears technical condition

NASA Astrophysics Data System (ADS)

Gerike, B. L.; Klishin, V. I.; Kuzin, E. G.

2017-09-01

The article presents the reasons for adopting intelligent service of mine belt conveyer drives concerning evaluation of their technical condition based on the diagnostic techniques instead of regular preventative maintenance. The article reveals the diagnostic results of belt conveyer drive reduction gears condition taking into account the parameters of lubricating oil, vibration and temperature. Usage of a complex approach to evaluate technical conditions allows reliability of the forecast to be improved, which makes it possible not only to prevent accidental breakdowns and eliminate unscheduled downtime, but also to bring sufficient economic benefits through reduction of the term and scope of work during overhauls.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

NASA Astrophysics Data System (ADS)

Prabakaran, S.; Mitra, Shilpa

2018-04-01

Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

Supporting Solar Physics Research via Data Mining

NASA Astrophysics Data System (ADS)

Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

2012-05-01

In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

New efforts using helicopter-borne and ground based electromagnetics for mineral exploration

NASA Astrophysics Data System (ADS)

Meyer, U.; Siemon, B.; Noell, U.; Gutzmer, J.; Spitzer, K.; Becken, M.

2014-12-01

Throughout the last decades mineral resources, especially rare earth elements, gained a steadily growing importance in industry and therefore as well in exploration. New targets for mineral investigations came into focus and known sources have been and will be revisited. Since most of the mining for mineral resources in the past took place in the upper hundred metres below surface new techniques made deeper mining economically feasible. Consequently, mining engineers need the best possible knowledge about the full spatial extent of prospective geological structures, including their maximum depths. Especially in Germany and Europe, politics changed in terms not to rely only on the global mineral trade market but on national resources, if available. BGR and partners therefore started research programs on different levels to evaluate and develop new technologies on environmental friendly, non-invasive spatial exploration using airborne and partly ground-based electromagnetic methods. Mining waste heaps have been explored for valuable residual minerals (research project ROBEHA), a promising tin bearing ore body is being explored by airborne electromagnetics (research project E3) and a new airborne technology is aimed at to be able to reach investigation depths of about 1 km (research project DESMEX). First results of the projects ROBEHA and E3 will be presented and the project layout of DESMEX will be discussed.

A Comparative Study of Frequent and Maximal Periodic Pattern Mining Algorithms in Spatiotemporal Databases

NASA Astrophysics Data System (ADS)

Obulesu, O.; Rama Mohan Reddy, A., Dr; Mahendra, M.

2017-08-01

Detecting regular and efficient cyclic models is the demanding activity for data analysts due to unstructured, vigorous and enormous raw information produced from web. Many existing approaches generate large candidate patterns in the occurrence of huge and complex databases. In this work, two novel algorithms are proposed and a comparative examination is performed by considering scalability and performance parameters. The first algorithm is, EFPMA (Extended Regular Model Detection Algorithm) used to find frequent sequential patterns from the spatiotemporal dataset and the second one is, ETMA (Enhanced Tree-based Mining Algorithm) for detecting effective cyclic models with symbolic database representation. EFPMA is an algorithm grows models from both ends (prefixes and suffixes) of detected patterns, which results in faster pattern growth because of less levels of database projection compared to existing approaches such as Prefixspan and SPADE. ETMA uses distinct notions to store and manage transactions data horizontally such as segment, sequence and individual symbols. ETMA exploits a partition-and-conquer method to find maximal patterns by using symbolic notations. Using this algorithm, we can mine cyclic models in full-series sequential patterns including subsection series also. ETMA reduces the memory consumption and makes use of the efficient symbolic operation. Furthermore, ETMA only records time-series instances dynamically, in terms of character, series and section approaches respectively. The extent of the pattern and proving efficiency of the reducing and retrieval techniques from synthetic and actual datasets is a really open & challenging mining problem. These techniques are useful in data streams, traffic risk analysis, medical diagnosis, DNA sequence Mining, Earthquake prediction applications. Extensive investigational outcomes illustrates that the algorithms outperforms well towards efficiency and scalability than ECLAT, STNR and MAFIA approaches.

Data mining and visualization techniques

DOEpatents

Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

2004-03-23

Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.

Evaluation of the mining techniques in constructing a traditional Chinese-language nursing recording system.

PubMed

Liao, Pei-Hung; Chu, William; Chu, Woei-Chyn

2014-05-01

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.

Data mining in child welfare.

PubMed

Schoech, D; Quinn, A; Rycraft, J R

2000-01-01

Data mining is the sifting through of voluminous data to extract knowledge for decision making. This article illustrates the context, concepts, processes, techniques, and tools of data mining, using statistical and neural network analyses on a dataset concerning employee turnover. The resulting models and their predictive capability, advantages and disadvantages, and implications for decision support are highlighted.

Redundancy and Novelty Mining in the Business Blogosphere

ERIC Educational Resources Information Center

Tsai, Flora S.; Chan, Kap Luk

2010-01-01

Purpose: The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before. Design/methodology/approach: Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which…

Reforestation of mined land in the northeastern and north-central U.S.

Treesearch

Walter H. Davidson; Russell J. Hutnik; Delbert E. Parr

1984-01-01

This paper reviews the state of the art of surface mine reclamation for forestry in Pennsylvania, Maryland, West Virginia, Ohio, Indiana, and Illinois. Legislative constraints, socioeconomic issues, factors limiting the success of reforestation efforts, post-mining land-use trends, species options, and establishment techniques are discussed. Sources of assistance to...

Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

ERIC Educational Resources Information Center

Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

Increasing the Reliability of the Work of Artificial Filtering Arrays for the Purification of Quarry Waste Water

NASA Astrophysics Data System (ADS)

Tyulenev, Maxim; Lesin, Yury; Litvin, Oleg; Maliukhina, Elena; Abay, Asmelash

2017-11-01

Features of geological structure of the Kuznetsk coal basin stipulate the application of a low-cost open technique of coal mining, which is more advantageous both from the economic standpoint, and by safety criteria of mining. However, open mining affects significantly the water resources of region. Intensive pollution of reservoirs and water courses, exhaustion of the underground water-bearing layers, violation of a hydrographic network, etc. be-long to the main disadvantages of an open technique of coal mining. Besides, the volume of the water coming into the mining producers exceeds signi-ficantly the needed quantity. According to the data of annual reports of ecology and natural resources department, 348.277 million m3 of water were ta-ken away during production of soft coal, brown coal and lignum fossil from waters of Kemerovo region in 2013 (mostly from underground water objects (96,5%) when draining of mine openings). At the same time, only 87.018 million m3 of water (25%) has been used within a year.

Methods and costs of thin-seam mining. Final report, 25 September 1977-24 January 1979. [Thin seam in association with a thick seam

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finch, T.E.; Fidler, E.L.

1981-02-01

This report defines the state of the art (circa 1978) in removing thin coal seams associated with vastly thicker seams found in the surface coal mines of the western United States. New techniques are evaluated and an innovative method and machine is proposed. Western states resource recovery regulations are addressed and representative mining operations are examined. Thin seam recovery is investigated through its effect on (1) overburden removal, (2) conventional seam extraction methods, and (3) innovative techniques. Equations and graphs are used to accommodate the variable stratigraphic positions in the mining sequence on which thin seams occur. Industrial concern andmore » agency regulations provided the impetus for this study of total resource recovery. The results are a compendium of thin seam removal methods and costs. The work explains how the mining industry recovers thin coal seams in western surface mines where extremely thick seams naturally hold the most attention. It explains what new developments imply and where to look for new improvements and their probable adaptability.« less

Field test of an alternative longwall gate road design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cox, R.M.; Vandergrift, T.L.; McDonnell, J.P.

1994-01-01

The US Bureau of Mines (USBM) MULSIM/ML modeling technique has been used to analyze anticipated stress distributions for a proposed alternative longwall gate road design for a western Colorado coal mine. The model analyses indicated that the alternative gate road design would reduce stresses in the headgate entry. To test the validity of the alternative gate road design under actual mining conditions, a test section of the alternative system was incorporated into a subsequent set of gate roads developed at the mine. The alternative gate road test section was instrumented with borehole pressure cells, as part of an ongoing USBMmore » research project to monitor ground pressure changes as longwall mining progressed. During the excavation of the adjacent longwall panels, the behavior of the alternative gate road system was monitored continuously using the USBM computer-assisted Ground Control Management System. During these field tests, the alternative gate road system was first monitored and evaluated as a headgate, and later monitored and evaluated as a tailgate. The results of the field tests confirmed the validity of using the MULSIM/NL modeling technique to evaluate mine designs.« less

Reduced-order model for underwater target identification using proper orthogonal decomposition

NASA Astrophysics Data System (ADS)

Ramesh, Sai Sudha; Lim, Kian Meng

2017-03-01

Research on underwater acoustics has seen major development over the past decade due to its widespread applications in domains such as underwater communication/navigation (SONAR), seismic exploration and oceanography. In particular, acoustic signatures from partially or fully buried targets can be used in the identification of buried mines for mine counter measures (MCM). Although there exist several techniques to identify target properties based on SONAR images and acoustic signatures, these methods first employ a feature extraction method to represent the dominant characteristics of a data set, followed by the use of an appropriate classifier based on neural networks or the relevance vector machine. The aim of the present study is to demonstrate the applications of proper orthogonal decomposition (POD) technique in capturing dominant features of a set of scattered pressure signals, and subsequent use of the POD modes and coefficients in the identification of partially buried underwater target parameters such as its location, size and material density. Several numerical examples are presented to demonstrate the performance of the system identification method based on POD. Although the present study is based on 2D acoustic model, the method can be easily extended to 3D models and thereby enables cost-effective representations of large-scale data.

All-Optical Fibre Networks For Coal Mines

NASA Astrophysics Data System (ADS)

Zientkiewicz, Jacek K.

1987-09-01

A topic of the paper is fiber-optic integrated network (FOIN) suited to the most hostile environments existing in coal mines. The use of optical fibres for transmission of mine instrumentation data offers the prospects of improved safety and immunity to electromagnetic interference (EMI). The feasibility of optically powered sensors has opened up new opportunities for research into optical signal processing architectures. This article discusses a new fibre-optic sensor network involving a time domain multiplexing(TDM)scheme and optical signal processing techniques. The pros and cons of different FOIN topologies with respect to coal mine applications are considered. The emphasis has been placed on a recently developed all-optical fibre network using spread spectrum code division multiple access (COMA) techniques. The all-optical networks have applications in explosive environments where electrical isolation is required.

Issues of data governance associated with data mining in medical research: experiences from an empirical study.

PubMed

Nahar, Jesmin; Imam, Tasadduq; Tickle, Kevin S; Garcia-Alonso, Debora

2013-01-01

This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions. The chapter will begin with an overview of computational intelligence concepts, followed by details on different classification algorithms. Use of association learning, a well recognised data mining procedure, will also be discussed. Many of the datasets considered in existing medical data mining research are imbalanced, and the chapter focuses on this issue as well. Lastly, the chapter outlines the need of data governance in this research domain.

REMOTE LAND MINE(FIELD) DETECTION. An Overview of Techniques (DETECTIE VAN LANDMIJNEN EN MIJNENVELDEN OP AFSTAND. Een Overzicht van de technieken),

DTIC Science & Technology

1994-09-01

titel DETECTIE VAN LANDMIJNEN EN MIJNENVELDEN OP AFSTAND, een overzicht van de technieken auteur (s) Drs. J.S. Groot, Ir. Y.H.L. Janssen datum september...functions based on set theory . The fundamental theory is developed in the sixties. This theory was applicable to binary images (black-and-white images...held at TNO-FEL. Various subjects related to fusion techniques: Dempster Shafer theory , Bayesian inference, Kalman filtering, fuzzy logic. [A15], [B4

Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.

PubMed

Caballero, Daniel; Antequera, Teresa; Caro, Andrés; Ávila, María Del Mar; G Rodríguez, Pablo; Perez-Palacios, Trinidad

2017-07-01

Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Multisensor fusion for the detection of mines and minelike targets

NASA Astrophysics Data System (ADS)

Hanshaw, Terilee

1995-06-01

The US Army's Communications and Electronics Command through the auspices of its Night Vision and Electronics Sensors Directorate (CECOM-NVESD) is actively applying multisensor techniques to the detection of mine targets. This multisensor research results from the 'detection activity' with its broad range of operational conditions and targets. Multisensor operation justifies significant attention by yielding high target detection and low false alarm statistics. Furthermore, recent advances in sensor and computing technologies make its practical application realistic and affordable. The mine detection field-of-endeavor has since its WWI baptismal investigated the known spectra for applicable mine observation phenomena. Countless sensors, algorithms, processors, networks, and other techniques have been investigated to determine candidacy for mine detection. CECOM-NVESD efforts have addressed a wide range of sensors spanning the spectrum from gravity field perturbations, magentic field disturbances, seismic sounding, electromagnetic fields, earth penetrating radar imagery, and infrared/visible/ultraviolet surface imaging technologies. Supplementary analysis has considered sensor candidate applicability by testing under field conditions (versus laboratory), in determination of fieldability. As these field conditions directly effect the probability of detection and false alarms, sensor employment and design must be considered. Consequently, as a given sensor's performance is influenced directly by the operational conditions, tradeoffs are necessary. At present, mass produced and fielded mine detection techniques are limited to those incorporating a single sensor/processor methodology such as, pulse induction and megnetometry, as found in hand held detectors. The most sensitive fielded systems can detect minute metal components in small mine targets but result in very high false alarm rates reducing velocity in operation environments. Furthermore, the actual speed of advance for the entire mission (convoy, movement to engagement, etc.) is determined by the level of difficulty presented in clearance or avoidance activities required in response to the potential 'targets' marked throughout a detection activity. Therefore the application of fielded hand held systems to convoy operations in clearly impractical. CECOM-NVESD efforts are presently seeking to overcome these operational limitations by substantially increasing speed of detection while reducing the false alarm rate through the application of multisensor techniques. The CECOM-NVESD application of multisensor techniques through integration/fusion methods will be defined in this paper.

Two modelling approaches to water-quality simulation in a flooded iron-ore mine (Saizerais, Lorraine, France): a semi-distributed chemical reactor model and a physically based distributed reactive transport pipe network model.

PubMed

Hamm, V; Collon-Drouaillet, P; Fabriol, R

2008-02-19

The flooding of abandoned mines in the Lorraine Iron Basin (LIB) over the past 25 years has degraded the quality of the groundwater tapped for drinking water. High concentrations of dissolved sulphate have made the water unsuitable for human consumption. This problematic issue has led to the development of numerical tools to support water-resource management in mining contexts. Here we examine two modelling approaches using different numerical tools that we tested on the Saizerais flooded iron-ore mine (Lorraine, France). A first approach considers the Saizerais Mine as a network of two chemical reactors (NCR). The second approach is based on a physically distributed pipe network model (PNM) built with EPANET 2 software. This approach considers the mine as a network of pipes defined by their geometric and chemical parameters. Each reactor in the NCR model includes a detailed chemical model built to simulate quality evolution in the flooded mine water. However, in order to obtain a robust PNM, we simplified the detailed chemical model into a specific sulphate dissolution-precipitation model that is included as sulphate source/sink in both a NCR model and a pipe network model. Both the NCR model and the PNM, based on different numerical techniques, give good post-calibration agreement between the simulated and measured sulphate concentrations in the drinking-water well and overflow drift. The NCR model incorporating the detailed chemical model is useful when a detailed chemical behaviour at the overflow is needed. The PNM incorporating the simplified sulphate dissolution-precipitation model provides better information of the physics controlling the effect of flow and low flow zones, and the time of solid sulphate removal whereas the NCR model will underestimate clean-up time due to the complete mixing assumption. In conclusion, the detailed NCR model will give a first assessment of chemical processes at overflow, and in a second time, the PNM model will provide more detailed information on flow and chemical behaviour (dissolved sulphate concentrations, remaining mass of solid sulphate) in the network. Nevertheless, both modelling methods require hydrological and chemical parameters (recharge flow rate, outflows, volume of mine voids, mass of solids, kinetic constants of the dissolution-precipitation reactions), which are commonly not available for a mine and therefore call for calibration data.

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH CONVEYORS. JIM WALTER RESOURCES INC. MINING DIVISION OPERATES FOUR UNDERGROUND COAL MINES IN THE BLUE CREEK COAL FIELD OF BIRMINGHAM DISTRICT, THREE IN TUSCALOOSA COUNTY AND ONE IN JEFFERSON COUNTY. TOTAL ANNUAL PRODUCTION IS 8,000,000 TONS. AT 2,300 DEEP, JIM WALTER'S BROOKWOOD MINES ARE THE DEEPEST UNDERGROUND COAL MINES IN NORTH AMERICA. THEY PRODUCE A HIGH-GRADE MEDIUM VOLATILE LOW SULPHUR METALLURGICAL COAL. THE BROOKWOOD NO. 5 MINE (PICTURED IN THIS PHOTOGRAPH) EMPLOYS THE LONGWALL MINING TECHNIQUES WITH BELTS CONVEYING COAL FROM UNDERGROUND OPERATIONS TO THE SURFACE. - JIm Walter Resources, Incorporated, Brookwood No. 5 Mine, 12972 Lock 17 Road, Brookwood, Tuscaloosa County, AL

Weighting Criteria and Prioritizing of Heat stress indices in surface mining using a Delphi Technique and Fuzzy AHP-TOPSIS Method.

PubMed

Asghari, Mehdi; Nassiri, Parvin; Monazzam, Mohammad Reza; Golbabaei, Farideh; Arabalibeik, Hossein; Shamsipour, Aliakbar; Allahverdy, Armin

2017-01-01

Heat stress as a physical harmful agent can increase the risk of health and safety problems in different workplaces such as mining. Although there are different indices to assess the heat stress imposed on workers, choosing the best index for a specific workplace is so important. Since various criteria affect an index applicability, extracting the most effective ones and determining their weights help to prioritize the existing indices and select the optimal index. In order to achieve this aim, present study compared some heat stress indices using effective methods. The viewpoints of occupational health experts and the qualitative Delphi methods were used to extract the most important criteria. Then, the weights of 11 selected criteria were determined by Fuzzy Analytic Hierarchy Process. Finally, fuzzy TOPSIS technique was applied for choosing the most suitable heat stress index. According to result, simplicity, reliability, being low cost, and comprehensiveness were the most determinative criteria for a heat stress index. Based on these criteria and their weights, the existing indices were prioritized. Eventually, wet bulb glob temperature appropriated the first priority and it was proposed as an applicable index for evaluating the heat stress at outdoor hot environments such as surface mines. The use of these strong methods allows introducing the most simple, precise, and applicable tool for evaluation the heat stress in hot environments. It seems that WBGT acts as an appropriate index for assessing the heat stress in mining activities at outdoors.

A Recommendation Algorithm for Automating Corollary Order Generation

PubMed Central

Klann, Jeffrey; Schadow, Gunther; McCoy, JM

2009-01-01

Manual development and maintenance of decision support content is time-consuming and expensive. We explore recommendation algorithms, e-commerce data-mining tools that use collective order history to suggest purchases, to assist with this. In particular, previous work shows corollary order suggestions are amenable to automated data-mining techniques. Here, an item-based collaborative filtering algorithm augmented with association rule interestingness measures mined suggestions from 866,445 orders made in an inpatient hospital in 2007, generating 584 potential corollary orders. Our expert physician panel evaluated the top 92 and agreed 75.3% were clinically meaningful. Also, at least one felt 47.9% would be directly relevant in guideline development. This automated generation of a rough-cut of corollary orders confirms prior indications about automated tools in building decision support content. It is an important step toward computerized augmentation to decision support development, which could increase development efficiency and content quality while automatically capturing local standards. PMID:20351875

A recommendation algorithm for automating corollary order generation.

PubMed

Klann, Jeffrey; Schadow, Gunther; McCoy, J M

2009-11-14

Manual development and maintenance of decision support content is time-consuming and expensive. We explore recommendation algorithms, e-commerce data-mining tools that use collective order history to suggest purchases, to assist with this. In particular, previous work shows corollary order suggestions are amenable to automated data-mining techniques. Here, an item-based collaborative filtering algorithm augmented with association rule interestingness measures mined suggestions from 866,445 orders made in an inpatient hospital in 2007, generating 584 potential corollary orders. Our expert physician panel evaluated the top 92 and agreed 75.3% were clinically meaningful. Also, at least one felt 47.9% would be directly relevant in guideline development. This automated generation of a rough-cut of corollary orders confirms prior indications about automated tools in building decision support content. It is an important step toward computerized augmentation to decision support development, which could increase development efficiency and content quality while automatically capturing local standards.

Remote sensing of strippable coal reserves and mine inventory in part of the Warrior Coal Field in Alabama

NASA Technical Reports Server (NTRS)

Joiner, T. J.; Copeland, C. W., Jr.; Russell, D. D.; Evans, F. E., Jr.; Sapp, C. D.; Boone, P. A.

1978-01-01

Methods by which estimates of the remaining reserves of strippable coal in Alabama could be made were developed. Information acquired from NASA's Earth Resources Office was used to analyze and map existing surface mines in a four-quadrangle area in west central Alabama. Using this information and traditional methods for mapping coal reserves, an estimate of remaining strippable reserves was derived. Techniques for the computer analysis of remotely sensed data and other types of available coal data were developed to produce an estimate of strippable coal reserves for a second four-quadrangle area. Both areas lie in the Warrior coal field, the most prolific and active of Alabama's coal fields. They were chosen because of the amount and type of coal mining in the area, their location relative to urban areas, and the amount and availability of base data necessary for this type of study.

Using ontology network structure in text mining.

PubMed

Berndt, Donald J; McCart, James A; Luther, Stephen L

2010-11-13

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

Analysis of Mining-Induced Subsidence Prediction by Exponent Knothe Model Combined with Insar and Leveling

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Liguo; Tang, Yixian; Zhang, Hong

2018-04-01

The principle of exponent Knothe model was introduced in detail and the variation process of mining subsidence with time was analysed based on the formulas of subsidence, subsidence velocity and subsidence acceleration in the paper. Five scenes of radar images and six levelling measurements were collected to extract ground deformation characteristics in one coal mining area in this study. Then the unknown parameters of exponent Knothe model were estimated by combined levelling data with deformation information along the line of sight obtained by InSAR technique. By compared the fitting and prediction results obtained by InSAR and levelling with that obtained only by levelling, it was shown that the accuracy of fitting and prediction combined with InSAR and levelling was obviously better than the other that. Therefore, the InSAR measurements can significantly improve the fitting and prediction accuracy of exponent Knothe model.

Use of an automatic earth resistivity system for detection of abandoned mine workings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, W.R.; Burdick, R.

1982-04-01

Under the sponsorship of the US Bureau of Mines, a surface-operated automatic high resolution earth resistivity system and associated computer data processing techniques have been designed and constructed for use as a potential means of detecting abandoned coal mine workings. The hardware and software aspects of the new system are described together with applications of the method to the survey and mapping of abandoned mine workings.

Promoter Sequences Prediction Using Relational Association Rule Mining

PubMed Central

Czibula, Gabriela; Bocicor, Maria-Iuliana; Czibula, Istvan Gergely

2012-01-01

In this paper we are approaching, from a computational perspective, the problem of promoter sequences prediction, an important problem within the field of bioinformatics. As the conditions for a DNA sequence to function as a promoter are not known, machine learning based classification models are still developed to approach the problem of promoter identification in the DNA. We are proposing a classification model based on relational association rules mining. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a data set. Our classifier is based on the discovery of relational association rules for predicting if a DNA sequence contains or not a promoter region. An experimental evaluation of the proposed model and comparison with similar existing approaches is provided. The obtained results show that our classifier overperforms the existing techniques for identifying promoter sequences, confirming the potential of our proposal. PMID:22563233

Information Gain Based Dimensionality Selection for Classifying Text Documents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dumidu Wijayasekara; Milos Manic; Miles McQueen

2013-06-01

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexitymore » is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.« less

Process-based upscaling of surface-atmosphere exchange

NASA Astrophysics Data System (ADS)

Keenan, T. F.; Prentice, I. C.; Canadell, J.; Williams, C. A.; Wang, H.; Raupach, M. R.; Collatz, G. J.; Davis, T.; Stocker, B.; Evans, B. J.

2015-12-01

Empirical upscaling techniques such as machine learning and data-mining have proven invaluable tools for the global scaling of disparate observations of surface-atmosphere exchange, but are not based on a theoretical understanding of the key processes involved. This makes spatial and temporal extrapolation outside of the training domain difficult at best. There is therefore a clear need for the incorporation of knowledge of ecosystem function, in combination with the strength of data mining. Here, we present such an approach. We describe a novel diagnostic process-based model of global photosynthesis and ecosystem respiration, which is directly informed by a variety of global datasets relevant to ecosystem state and function. We use the model framework to estimate global carbon cycling both spatially and temporally, with a specific focus on the mechanisms responsible for long-term change. Our results show the importance of incorporating process knowledge into upscaling approaches, and highlight the effect of key processes on the terrestrial carbon cycle.

Educational Data Mining Application for Estimating Students Performance in Weka Environment

NASA Astrophysics Data System (ADS)

Gowri, G. Shiyamala; Thulasiram, Ramasamy; Amit Baburao, Mahindra

2017-11-01

Educational data mining (EDM) is a multi-disciplinary research area that examines artificial intelligence, statistical modeling and data mining with the data generated from an educational institution. EDM utilizes computational ways to deal with explicate educational information keeping in mind the end goal to examine educational inquiries. To make a country stand unique among the other nations of the world, the education system has to undergo a major transition by redesigning its framework. The concealed patterns and data from various information repositories can be extracted by adopting the techniques of data mining. In order to summarize the performance of students with their credentials, we scrutinize the exploitation of data mining in the field of academics. Apriori algorithmic procedure is extensively applied to the database of students for a wider classification based on various categorizes. K-means procedure is applied to the same set of databases in order to accumulate them into a specific category. Apriori algorithm deals with mining the rules in order to extract patterns that are similar along with their associations in relation to various set of records. The records can be extracted from academic information repositories. The parameters used in this study gives more importance to psychological traits than academic features. The undesirable student conduct can be clearly witnessed if we make use of information mining frameworks. Thus, the algorithms efficiently prove to profile the students in any educational environment. The ultimate objective of the study is to suspect if a student is prone to violence or not.

Mercury Mining in Mexico: I. Community Engagement to Improve Health Outcomes from Artisanal Mining.

PubMed

Camacho, Andrea; Van Brussel, Evelyn; Carrizales, Leticia; Flores-Ramírez, Rogelio; Verduzco, Beatriz; Huerta, Selene Ruvalcaba-Aranda; Leon, Mauricio; Díaz-Barriga, Fernando

2016-01-01

Mercury is an element that cannot be destroyed and is a global threat to human and environmental health. In Latin America and the Caribbean, artisanal and small-scale gold mining represents the main source of mercury emissions, releases, and consumption. However, another source of concern is the primary production of mercury. In the case of Mexico, in the past 2 years the informal production of mercury mining has increased 10-fold. Considering this scenario, an intervention program was initiated to reduce health risks in the mining communities. The program's final goal is to introduce different alternatives in line to stop the mining of mercury, but introducing at the same time, a community-based development program. The aim of this study was to present results from a preliminary study in the community of Plazuela, located in the municipality of Peñamiller in the State of Queretaro, Mexico. Total mercury was measured in urine and environmental samples using atomic absorption spectrometry by cold vapor technique. Urine samples were collected from children aged 6-14 years and who had lived in the selected area from birth. Urine samples were also collected from miners who were currently working in the mine. To confirm the presence of mercury in the community, mining waste, water, soil, and sediment samples were collected from those high-risk areas identified by members of the community. Children, women, and miners were heavily exposed to mercury (urine samples); and in agreement, we registered high concentrations of mercury in soils and sediments. Considering these results and taking into account that the risk perception toward mercury toxicity is very low in the community (mining is the only economic activity), an integral intervention program has started. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates.

PubMed

Richardson, Claire; Rutherford, Shannon; Agranovski, Igor

2018-06-01

Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and to inform regulatory decisions. The currently available emission estimation methods for Australian open-cut coal mines relate primarily to total suspended particulates and PM 10 (particulate matter with an aerodynamic diameter <10 μm), and limited data are available relating to the PM 2.5 (<2.5 μm) size fraction. To provide an initial analysis of the appropriateness of the currently available emission estimation techniques, this paper presents results of sampling completed at three open-cut coal mines in Australia. The monitoring data demonstrate that the particulate size fraction varies for different mining activities, and that the region in which the mine is located influences the characteristics of the particulates emitted to the atmosphere. The proportion of fine particulates in the sample increased with distance from the source, with the coarse fraction being a more significant proportion of total suspended particulates close to the source of emissions. In terms of particulate composition, the results demonstrate that the particulate emissions are predominantly sourced from naturally occurring geological material, and coal comprises less than 13% of the overall emissions. The size fractionation exhibited by the sampling data sets is similar to that adopted in current Australian emission estimation methods but differs from the size fractionation presented in the U.S. Environmental Protection Agency methodology. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Comprehensive air quality monitoring was undertaken, and corresponding recommendations were provided.

Statistical sensor fusion analysis of near-IR polarimetric and thermal imagery for the detection of minelike targets

NASA Astrophysics Data System (ADS)

Weisenseel, Robert A.; Karl, William C.; Castanon, David A.; DiMarzio, Charles A.

1999-02-01

We present an analysis of statistical model based data-level fusion for near-IR polarimetric and thermal data, particularly for the detection of mines and mine-like targets. Typical detection-level data fusion methods, approaches that fuse detections from individual sensors rather than fusing at the level of the raw data, do not account rationally for the relative reliability of different sensors, nor the redundancy often inherent in multiple sensors. Representative examples of such detection-level techniques include logical AND/OR operations on detections from individual sensors and majority vote methods. In this work, we exploit a statistical data model for the detection of mines and mine-like targets to compare and fuse multiple sensor channels. Our purpose is to quantify the amount of knowledge that each polarimetric or thermal channel supplies to the detection process. With this information, we can make reasonable decisions about the usefulness of each channel. We can use this information to improve the detection process, or we can use it to reduce the number of required channels.

Cost estimation and analysis using the Sherpa Automated Mine Cost Engineering System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stebbins, P.E.

1993-09-01

The Sherpa Automated Mine Cost Engineering System is a menu-driven software package designed to estimate capital and operating costs for proposed surface mining operations. The program is engineering (as opposed to statistically) based, meaning that all equipment, manpower, and supply requirements are determined from deposit geology, project design and mine production information using standard engineering techniques. These requirements are used in conjunction with equipment, supply, and labor cost databases internal to the program to estimate all associated costs. Because virtually all on-site cost parameters are interrelated within the program, Sherpa provides an efficient means of examining the impact of changesmore » in the equipment mix on total capital and operating costs. If any aspect of the operation is changed, Sherpa immediately adjusts all related aspects as necessary. For instance, if the user wishes to examine the cost ramifications of selecting larger trucks, the program not only considers truck purchase and operation costs, it also automatically and immediately adjusts excavator requirements, operator and mechanic needs, repair facility size, haul road construction and maintenance costs, and ancillary equipment specifications.« less

Mercury pollution in Asia: a review of the contaminated sites.

PubMed

Li, P; Feng, X B; Qiu, G L; Shang, L H; Li, Z G

2009-09-15

This article describes the mercury contaminated sites in Asia. Among the various regions, Asia has become the largest contributor of anthropogenic atmospheric mercury (Hg), responsible for over half of the global emission. Based on different emission source categories, the mercury contaminated sites in Asia were divided into various types, such as Hg pollution from Hg mining, gold mining, chemical industry, metal smelting, coal combustion, metropolitan cities, natural resources and agricultural sources. By the review of a large number of studies, serious Hg pollutions to the local environment were found in the area influenced by chemical industry, mercury mining and gold mining. With the probable effects of a unique combination of climatic (e.g. subtropical climate), environmental (e.g. acid rain), economic (e.g. swift growth) and social factors (e.g. high population density), more effort is still needed to understand the biogeochemistry cycle of Hg and associated health effects in Asia. Safer alternatives and cleaner technologies must be developed and effectively implemented to reduce mercury emission; remedial techniques are also required to restore the historical mercury pollution in Asia.

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

PubMed Central

Sun, Wencheng; Li, Yangyang; Liu, Fang; Fang, Shengqun; Wang, Guoyan

2018-01-01

Currently, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work. PMID:29849998

Ecological engineering alternatives for remediation and restoration of a drastically disturbed landscape

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nairn, R.W.; Hare, L.; Mercer, M.

As part of a Fall 1998 Environmental Science graduate seminar in Ecological Engineering at the University of Oklahoma, students were asked to submit a proposal for the holistic and sustainable restoration of the Tar Creek Superfund Site, Ottawa county, Oklahoma. the Tar Creek site is a portion of an abandoned lead and zinc mining area known as the Tri-State Mining District (OL, KS and MO) and includes approximately 104 square kilometers of disturbed land surface and contaminated water resources in extreme northeastern Oklahoma. Approximately 94 million cubic meters of contaminated water currently exist in the underground voids. In 1979, acidic,more » metal-rich waters began to discharge into Tar Creek from natural springs, bore holes and mine shafts. In addition, approximately 37 million cubic meters of processed mine waste materials (chat) litter their surface in large piles. Approximately 324 hectares of contaminated tailings settling ponds also exist on site. Student submitted proposals addressed the following four subject areas: passive treatment options for stream water quality improvement, surface reclamation and revegetation, stream habitat restoration and joint ecological and economic sustainability. Proposed designs for passive treatment of the contaminated mine drainage included unique constructed wetland designs that relief on a combination of biological and geochemical processes, use of microbial mats for luxury metal uptake, enhanced iron oxidation via windmill-based aeration and fly ash injection. proposed surface reclamation methods included minimal regrading following by biosolid, ash and other organic amendment applications and several phytoremediation techniques, especially the use of hyperaccumulators. The stream and riparian restoration portion of the proposals focused on chat removal, phytoremediation and species reintroduction. proposed joint ecological and economic sustainability ventures included development of recreational facilities, mining-based tourism and an Ecotechnology Research Park.« less

Geotechnical approach for occupational safety risk analysis of critical slope in open pit mining as implication for earthquake hazard

NASA Astrophysics Data System (ADS)

Munirwansyah; Irsyam, Masyhur; Munirwan, Reza P.; Yunita, Halida; Zulfan Usrina, M.

2018-05-01

Occupational safety and health (OSH) is a planned effort to prevent accidents and diseases caused by work. In conducting mining activities often occur work accidents caused by unsafe field conditions. In open mine area, there is often a slump due to unstable slopes, which can disrupt the activities and productivity of mining companies. Based on research on stability of open pit slopes conducted by Febrianti [8], the Meureubo coal mine located in Aceh Barat district, on the slope of mine was indicated unsafe slope conditions, it will be continued research on OSH for landslide which is to understand the stability of the excavation slope and the shape of the slope collapse. Plaxis software was used for this research. After analyzing the slope stability and the effect of landslide on OSH with Job Safety Analysis (JSA) method, to identify the hazard to work safety, risk management analysis will be conducted to classified hazard level and its handling technique. This research aim is to know the level of risk of work accident at the company and its prevention effort. The result of risk analysis research is very high-risk value that is > 350 then the activity must be stopped until the risk can be reduced to reach the risk value limit < 20 which is allowed or accepted.

D Reconstruction and Modeling of Subterranean Landscapes in Collaborative Mining Archeology Projects: Techniques, Applications and Experiences

NASA Astrophysics Data System (ADS)

Arles, A.; Clerc, P.; Sarah, G.; Téreygeol, F.; Bonnamour, G.; Heckes, J.; Klein, A.

2013-07-01

Mining and underground archaeology are two domains of expertise where three-dimensional data take an important part in the associated researches. Up to now, archaeologists study mines and underground networks from line-plot surveys, cross-section of galleries, and from tool marks surveys. All this kind of information can be clearly recorded back from the field from threedimensional models with a more cautious and extensive approach. Besides, the volumes of the underground structures that are very important data to explain the mining activities are difficult to evaluate from "traditional" hand-made recordings. They can now be calculated more accurately from a 3D model. Finally, reconstructed scenes are a powerful tool as thinking aid to look back again to a structure in the office or in future times. And the recorded models, rendered photo-realistically, can also be used for cultural heritage documentation presenting inaccessible and sometimes dangerous places to the public. Nowadays, thanks to modern computer technologies and highly developed software tools paired with sophisticated digital camera equipment, complex photogrammetric processes are available for moderate costs for research teams. Recognizing these advantages the authors develop and utilize image-based workflows in order to document ancient mining monuments and underground sites as a basis for further historical and archaeological researches, performed in collaborative partnership during recent projects on medieval silver mines and preventive excavations of undergrounds in France.

Relating landscape characteristics to non-point source pollution in mine waste-located watersheds using geospatial techniques.

PubMed

Xiao, Huaguo; Ji, Wei

2007-01-01

Landscape characteristics of a watershed are important variables that influence surface water quality. Understanding the relationship between these variables and surface water quality is critical in predicting pollution potential and developing watershed management practices to eliminate or reduce pollution risk. To understand the impacts of landscape characteristics on water quality in mine waste-located watersheds, we conducted a case study in the Tri-State Mining District which is located in the conjunction of three states (Missouri, Kansas and Oklahoma). Severe heavy metal pollution exists in that area resulting from historical mining activities. We characterized land use/land cover over the last three decades by classifying historical multi-temporal Landsat imagery. Landscape metrics such as proportion, edge density and contagion were calculated based on the classified imagery. In-stream water quality data over three decades were collected, including lead, zinc, iron, cadmium, aluminum and conductivity which were used as key water quality indicators. Statistical analyses were performed to quantify the relationship between landscape metrics and surface water quality. Results showed that landscape characteristics in mine waste-located watersheds could account for as much as 77% of the variation of water quality indicators. A single landscape metric alone, such as proportion of mine waste area, could be used to predict surface water quality; but its predicting power is limited, usually accounting for less than 60% of the variance of water quality indicators.

Mapping informal small-scale mining features in a data-sparse tropical environment with a small UAS

USGS Publications Warehouse

Chirico, Peter G.; Dewitt, Jessica D.

2017-01-01

This study evaluates the use of a small unmanned aerial system (UAS) to collect imagery over artisanal mining sites in West Africa. The purpose of this study is to consider how very high-resolution imagery and digital surface models (DSMs) derived from structure-from-motion (SfM) photogrammetric techniques from a small UAS can fill the gap in geospatial data collection between satellite imagery and data gathered during field work to map and monitor informal mining sites in tropical environments. The study compares both wide-angle and narrow field of view camera systems in the collection and analysis of high-resolution orthoimages and DSMs of artisanal mining pits. The results of the study indicate that UAS imagery and SfM photogrammetric techniques permit DSMs to be produced with a high degree of precision and relative accuracy, but highlight the challenges of mapping small artisanal mining pits in remote and data sparse terrain.

Analysis of Nature of Science Included in Recent Popular Writing Using Text Mining Techniques

NASA Astrophysics Data System (ADS)

Jiang, Feng; McComas, William F.

2014-09-01

This study examined the inclusion of nature of science (NOS) in popular science writing to determine whether it could serve supplementary resource for teaching NOS and to evaluate the accuracy of text mining and classification as a viable research tool in science education research. Four groups of documents published from 2001 to 2010 were analyzed: Scientific American, Discover magazine, winners of the Royal Society Winton Prize for Science Books, and books from NSTA's list of Outstanding Science Trade Books. Computer analysis categorized passages in the selected documents based on their inclusions of NOS. Human analysis assessed the frequency, context, coverage, and accuracy of the inclusions of NOS within computer identified NOS passages. NOS was rarely addressed in selected document sets but somewhat more frequently addressed in the letters section of the two magazines. This result suggests that readers seem interested in the discussion of NOS-related themes. In the popular science books analyzed, NOS presentations were found more likely to be aggregated in the beginning and the end of the book, rather than scattered throughout. The most commonly addressed NOS elements in the analyzed documents are science and society and empiricism in science. Only one inaccurate presentation of NOS were identified in all analyzed documents. The text mining technique demonstrated exciting performance, which invites more applications of the technique to analyze other aspects of science textbooks, popular science writing, or other materials involved in science teaching and learning.

Computer-aided visual assessment in mine planning and design

Treesearch

Michael Hatfield; A. J. LeRoy Balzer; Roger E. Nelson

1979-01-01

A computer modeling technique is described for evaluating the visual impact of a proposed surface mine located within the viewshed of a national park. A computer algorithm analyzes digitized USGS baseline topography and identifies areas subject to surface disturbance visible from the park. Preliminary mine and reclamation plan information is used to describe how the...

A Quantitative Analysis of Organizational Factors That Relate to Data Mining Success

ERIC Educational Resources Information Center

Huebner, Richard A.

2017-01-01

The ubiquity of data in various forms has fueled the need for advanced data-mining techniques within organizations. The advent of data mining methods used to uncover hidden nuggets of information buried within large data sets has also fueled the need for determining how these unique projects can be successful. There are many challenges associated…

Educational Data Mining Applications and Tasks: A Survey of the Last 10 Years

ERIC Educational Resources Information Center

Bakhshinategh, Behdad; Zaiane, Osmar R.; ElAtia, Samira; Ipperciel, Donald

2018-01-01

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning…

Combining Natural Language Processing and Statistical Text Mining: A Study of Specialized versus Common Languages

ERIC Educational Resources Information Center

Jarman, Jay

2011-01-01

This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…

Caleb Phillips | NREL

Science.gov Websites

, Statistical Analysis and Data Mining: The ASA Data Science Journal (2017) Using GIS-Based Methods and Lidar techniques to the problem of large area coverage mapping for wireless networks. He has also done work in -4297 Dr. Caleb Phillips is a data scientist with the Computational Science Center at NREL. Caleb comes

An integrtated approach to the use of Landsat TM data for gold exploration in west central Nevada

NASA Technical Reports Server (NTRS)

Mouat, D. A.; Myers, J. S.; Miller, N. L.

1987-01-01

This paper represents an integration of several Landsat TM image processing techniques with other data to discriminate the lithologies and associated areas of hydrothermal alteration in the vicinity of the Paradise Peak gold mine in west central Nevada. A microprocessor-based image processing system and an IDIMS system were used to analyze data from a 512 X 512 window of a Landsat-5 TM scene collected on June 30, 1984. Image processing techniques included simple band composites, band ratio composites, principal components composites, and baseline-based composites. These techniques were chosen based on their ability to discriminate the spectral characteristics of the products of hydrothermal alteration as well as of the associated regional lithologies. The simple band composite, ratio composite, two principal components composites, and the baseline-based composites separately can define the principal areas of alteration. Combined, they provide a very powerful exploration tool.

Application of Modern Tools and Techniques for Mine Safety & Disaster Management

NASA Astrophysics Data System (ADS)

Kumar, Dheeraj

2016-04-01

The implementation of novel systems and adoption of improvised equipment in mines help mining companies in two important ways: enhanced mine productivity and improved worker safety. There is a substantial need for adoption of state-of-the-art automation technologies in the mines to ensure the safety and to protect health of mine workers. With the advent of new autonomous equipment used in the mine, the inefficiencies are reduced by limiting human inconsistencies and error. The desired increase in productivity at a mine can sometimes be achieved by changing only a few simple variables. Significant developments have been made in the areas of surface and underground communication, robotics, smart sensors, tracking systems, mine gas monitoring systems and ground movements etc. Advancement in information technology in the form of internet, GIS, remote sensing, satellite communication, etc. have proved to be important tools for hazard reduction and disaster management. This paper is mainly focused on issues pertaining to mine safety and disaster management and some of the recent innovations in the mine automations that could be deployed in mines for safe mining operations and for avoiding any unforeseen mine disaster.

A proposed configurable approach for recommendation systems via data mining techniques

NASA Astrophysics Data System (ADS)

Khedr, Ayman E.; Idrees, Amira M.; Hegazy, Abd El-Fatah; El-Shewy, Samir

2018-02-01

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.

Market basket analysis visualization on a spherical surface

NASA Astrophysics Data System (ADS)

Hao, Ming C.; Hsu, Meichun; Dayal, Umeshwar; Wei, Shu F.; Sprenger, Thomas; Holenstein, Thomas

2001-05-01

This paper discusses the visualization of the relationships in e-commerce transactions. To date, many practical research projects have shown the usefulness of a physics-based mass- spring technique to layout data items with close relationships on a graph. We describe a market basket analysis visualization system using this technique. This system is described as the following: (1) integrates a physics-based engine into a visual data mining platform; (2) use a 3D spherical surface to visualize the cluster of related data items; and (3) for large volumes of transactions, uses hidden structures to unclutter the display. Several examples of market basket analysis are also provided.

Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting

NASA Astrophysics Data System (ADS)

Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon

In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.

Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges.

PubMed

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area.

Data mining and medical world: breast cancers’ diagnosis, treatment, prognosis and challenges

PubMed Central

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area. PMID:28401016

Determining the radon exhalation rate from a gold mine tailings dump by measuring the gamma radiation.

PubMed

Ongori, Joash N; Lindsay, Robert; Newman, Richard T; Maleka, Peane P

2015-02-01

The mining activities taking place in Gauteng province, South Africa have caused millions of tons of rocks to be taken from underground to be milled and processed to extract gold. The uranium bearing tailings are placed in an estimated 250 dumps covering a total area of about 7000 ha. These tailings dumps contain considerable amounts of radium and have therefore been identified as large sources of radon. The size of these dumps make traditional radon exhalation measurements time consuming and it is difficult to get representative measurements for the whole dump. In this work radon exhalation measurements from the non-operational Kloof mine dump have been performed by measuring the gamma radiation from the dump fairly accurately over an area of more than 1 km(2). Radon exhalation from the mine dump have been inferred from this by laboratory-based and in-situ gamma measurements. Thirty four soil samples were collected at depths of 30 cm and 50 cm. The weighted average activity concentrations in the soil samples were 308 ± 7 Bq kg(-1), 255 ± 5 Bq kg(-1) and 18 ± 1 Bq kg(-1) for (238)U, (40)K and (232)Th, respectively. The MEDUSA (Multi-Element Detector for Underwater Sediment Activity) γ-ray detection system was used for field measurements. The radium concentrations were then used with soil parameters to obtain the radon flux using different approaches such as the IAEA (International Atomic Energy Agency) formula. Another technique the MEDUSA Laboratory Technique (MELT) was developed to map radon exhalation based on (1) recognising that radon exhalation does not affect (40)K and (232)Th activity concentrations and (2) that the ratio of the activity concentration of the field (MEDUSA) to the laboratory (HPGe) for (238)U and (40)K or (238)U and (232)Th will give a measure of the radon exhalation at a particular location in the dump. The average, normalised radon flux was found to be 0.12 ± 0.02 Bq m(-2) s(-1) for the mine dump. Copyright © 2014 Elsevier Ltd. All rights reserved.

Estimating natural background groundwater chemistry, Questa molybdenum mine, New Mexico

USGS Publications Warehouse

Verplanck, Phillip L.; Nordstrom, D. Kirk; Plumlee, Geoffrey S.; Walker, Bruce M.; Morgan, Lisa A.; Quane, Steven L.

2010-01-01

This 2 1/2 day field trip will present an overview of a U.S. Geological Survey (USGS) project whose objective was to estimate pre-mining groundwater chemistry at the Questa molybdenum mine, New Mexico. Because of intense debate among stakeholders regarding pre-mining groundwater chemistry standards, the New Mexico Environment Department and Chevron Mining Inc. (formerly Molycorp) agreed that the USGS should determine pre-mining groundwater quality at the site. In 2001, the USGS began a 5-year, multidisciplinary investigation to estimate pre-mining groundwater chemistry utilizing a detailed assessment of a proximal natural analog site and applied an interdisciplinary approach to infer pre-mining conditions. The trip will include a surface tour of the Questa mine and key locations in the erosion scar areas and along the Red River. The trip will provide participants with a detailed understanding of geochemical processes that influence pre-mining environmental baselines in mineralized areas and estimation techniques for determining pre-mining baseline conditions.

Unmanned Mine of the 21st Centuries

NASA Astrophysics Data System (ADS)

Semykina, Irina; Grigoryev, Aleksandr; Gargayev, Andrey; Zavyalov, Valeriy

2017-11-01

The article is analytical. It considers the construction principles of the automation system structure which realize the concept of «unmanned mine». All of these principles intend to deal with problems caused by a continuous complication of mining-and-geological conditions at coalmine such as the labor safety and health protection, the weak integration of different mining automation subsystems and the deficiency of optimal balance between a quantity of resource and energy consumed by mining machines and their throughput. The authors describe the main problems and neck stage of mining machines autonomation and automation subsystem. The article makes a general survey of the applied «unmanned technology» in the field of mining such as the remotely operated autonomous complexes, the underground positioning systems of mining machines using infrared radiation in mine workings etc. The concept of «unmanned mine» is considered with an example of the robotic road heading machine. In the final, the authors analyze the techniques and methods that could solve the task of underground mining without human labor.

Integration of MODIS data and Short Baseline Subset (SBAS) technique for land subsidence monitoring in Datong, China

NASA Astrophysics Data System (ADS)

Zhao, Chao-ying; Zhang, Qin; Yang, Chengsheng; Zou, Weibao

2011-07-01

Datong is located in the north of Shanxi Province, which is famous for its old-fashioned coal-mining preservation in China. Some serious issues such as land subsidence, ground fissures, mining collapse, and earthquake hazards have occurred over this area for a long time resulting in significant damages to buildings and roads. In order to monitor and mitigate these natural man-made hazards, Short Baseline Subsets (SBAS) InSAR technique with ten Envisat ASAR data is applied to detect the surface deformation over an area of thousands of square kilometers. Then, five MODIS data are used to check the atmospheric effects on InSAR interferograms. Finally, nine nonlinear land subsidence cumulative results during September 2004 and February 2008 are obtained. Based on the deformation data, three kinds of land subsidence are clearly detected, caused by mine extraction, underground water withdrawal and construction of new economic zones, respectively. The annual mean velocity of subsidence can reach 1 to 4 cm/year in different subsidence areas. A newly designed high-speed railway (HSR) with speeds of 350 km/h will cross through the Datong hi-tech zone. Special measures should be taken for the long run of this project. In addition, another two subsidence regions need further investigation to mitigate such hazards.

Mining the National Career Assessment Examination Result Using Clustering Algorithm

NASA Astrophysics Data System (ADS)

Pagudpud, M. V.; Palaoag, T. T.; Padirayon, L. M.

2018-03-01

Education is an essential process today which elicits authorities to discover and establish innovative strategies for educational improvement. This study applied data mining using clustering technique for knowledge extraction from the National Career Assessment Examination (NCAE) result in the Division of Quirino. The NCAE is an examination given to all grade 9 students in the Philippines to assess their aptitudes in the different domains. Clustering the students is helpful in identifying students’ learning considerations. With the use of the RapidMiner tool, clustering algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), k-means, k-medoid, expectation maximization clustering, and support vector clustering algorithms were analyzed. The silhouette indexes of the said clustering algorithms were compared, and the result showed that the k-means algorithm with k = 3 and silhouette index equal to 0.196 is the most appropriate clustering algorithm to group the students. Three groups were formed having 477 students in the determined group (cluster 0), 310 proficient students (cluster 1) and 396 developing students (cluster 2). The data mining technique used in this study is essential in extracting useful information from the NCAE result to better understand the abilities of students which in turn is a good basis for adopting teaching strategies.

Data Mining Methods for Recommender Systems

NASA Astrophysics Data System (ADS)

Amatriain, Xavier; Jaimes*, Alejandro; Oliver, Nuria; Pujol, Josep M.

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.

PubMed

Nasr Azadani, Mozhgan; Ghadiri, Nasser; Davoodijam, Ensieh

2018-06-12

Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. We perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. The carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain. Copyright © 2018. Published by Elsevier Inc.

Exploring context and content links in social media: a latent space method.

PubMed

Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

2012-05-01

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

Integrated mined-area reclamation and land-use planning. Volume 3C. A case study of surface mining and reclamation planning: Georgia Kaolin Company Clay Mines, Washington County, Georgia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guernsey, J L; Brown, L A; Perry, A O

1978-02-01

This case study examines the reclamation practices of the Georgia Kaolin's American Industrial Clay Company Division, a kaolin producer centered in Twiggs, Washington, and Wilkinson Counties, Georgia. The State of Georgia accounts for more than one-fourth of the world's kaolin production and about three-fourths of U.S. kaolin output. The mining of kaolin in Georgia illustrates the effects of mining and reclaiming lands disturbed by area surface mining. The disturbed areas are reclaimed under the rules and regulations of the Georgia Surface Mining Act of 1968. The natural conditions influencing the reclamation methodologies and techniques are markedly unique from those ofmore » other mining operations. The environmental disturbances and procedures used in reclaiming the kaolin mined lands are reviewed and implications for planners are noted.« less

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

PubMed

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Use of an automatic resistivity system for detecting abandoned mine workings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, W.R.; Burdick, R.G.

1983-01-01

A high-resolution earth resistivity system has been designed and constructed for use as a means of detecting abandoned coal mine workings. The automatic pole-dipole earth resistivity technique has already been applied to the detection of subsurface voids for military applications. The hardware and software of the system are described, together with applications for surveying and mapping abandoned coal mine workings. Field tests are presented to illustrate the detection of both air-filled and water-filled mine workings.

Artisanal and Small-Scale Gold Mining Without Mercury

EPA Pesticide Factsheets

Mercury-free techniques are safer for miners, their families and local communities. They can also help miners qualify for certification under fair-mined standards, potentially allowing them to market their gold at higher prices.

The Pollution Detectives: Part II. Lead and Zinc Mining.

ERIC Educational Resources Information Center

Sanderson, P. L.

1988-01-01

Describes a field trip taken to an old mining area to study water pollution. Discussed are methods for silt analysis, reagent preparation, color charts, techniques, fieldwork, field results, and a laboratory study. (CW)

An integrated environment monitoring system for underground coal mines--Wireless Sensor Network subsystem with multi-parameter monitoring.

PubMed

Zhang, Yu; Yang, Wei; Han, Dongsheng; Kim, Young-Il

2014-07-21

Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected.

LLNL electro-optical mine detection program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, C.; Aimonetti, W.; Barth, M.

1994-09-30

Under funding from the Advanced Research Projects Agency (ARPA) and the US Marine Corps (USMC), Lawrence Livermore National Laboratory (LLNL) has directed a program aimed at improving detection capabilities against buried mines and munitions. The program has provided a national test facility for buried mines in arid environments, compiled and distributed an extensive data base of infrared (IR), ground penetrating radar (GPR), and other measurements made at that site, served as a host for other organizations wishing to make measurements, made considerable progress in the use of ground penetrating radar for mine detection, and worked on the difficult problem ofmore » sensor fusion as applied to buried mine detection. While the majority of our effort has been concentrated on the buried mine problem, LLNL has worked with the U.S.M.C. on surface mine problems as well, providing data and analysis to support the COBRA (Coastal Battlefield Reconnaissance and Analysis) program. The original aim of the experimental aspect of the program was the utilization of multiband infrared approaches for the detection of buried mines. Later the work was extended to a multisensor investigation, including sensors other than infrared imagers. After an early series of measurements, it was determined that further progress would require a larger test facility in a natural environment, so the Buried Object Test Facility (BOTF) was constructed at the Nevada Test Site. After extensive testing, with sensors spanning the electromagnetic spectrum from the near ultraviolet to radio frequencies, possible paths for improvement were: improved spatial resolution providing better ground texture discrimination; analysis which involves more complicated spatial queueing and filtering; additional IR bands using imaging spectroscopy; the use of additional sensors other than IR and the use of data fusion techniques with multi-sensor data; and utilizing time dependent observables like temperature.« less

Development and Sliding Wear Response of Epoxy Composites Filled with Coal Mine Overburden Material

NASA Astrophysics Data System (ADS)

Das, Prithika; Satapathy, Alok; Mishra, M. K.

2018-03-01

The paper reports on development and characterization of epoxy based composites filled with micro-sized mine overburden material. Coal mine overburden material is typically highly heterogeneous and is considered as waste material. For excavating each ton of coal, roughly 5 tons of overburden materials are removed and is dumped nearby occupying large space. Gainful utilization of this waste is a major challenge. In the present work, this material is used as filler materials in making a new class of epoxy matrix composites. Composites with different weight proportions of fillers (0, 10, 20, 30 and 40) wt. % are prepared by hand layup technique. Compression tests are performed as per corresponding ASTM standards to assess the compressive strength of these composites. Further, dry sliding tests are performed following ASTM G99 standards using a pin on disk machine. A design of experiment approach based on Taguchi’s L16 orthogonal arrays is adopted. Tests are performed at different sliding velocities for multiple sliding distances under varying normal loads. Specific wear rates of the composites under different test conditions are obtained. The analysis of the test results revealed that the filler content and the sliding velocity are the most predominant control factors affecting the wear rate. This work thus, opens up a new avenue for the value added utilization of coal mine overburden material.

Research on position and orientation measurement method for roadheader based on vision/INS

NASA Astrophysics Data System (ADS)

Yang, Jinyong; Zhang, Guanqin; Huang, Zhe; Ye, Yaozhong; Ma, Bowen; Wang, Yizhong

2018-01-01

Roadheader which is a kind of special equipment for large tunnel excavation has been widely used in Coal Mine. It is one of the main mechanical-electrical equipment for mine production and also has been regarded as the core equipment for underground tunnel driving construction. With the deep application of the rapid driving system, underground tunnel driving methods with higher automation level are required. In this respect, the real-time position and orientation measurement technique for roadheader is one of the most important research contents. For solving the problem of position and orientation measurement automatically in real time for roadheaders, this paper analyses and compares the features of several existing measuring methods. Then a new method based on the combination of monocular vision and strap down inertial navigation system (SINS) would be proposed. By realizing five degree-of-freedom (DOF) measurement of real-time position and orientation of roadheader, this method has been verified by the rapid excavation equipment in Daliuta coal mine. Experiment results show that the accuracy of orientation measurement is better than 0.1°, the standard deviation of static drift is better than 0.25° and the accuracy of position measurement is better than 1cm. It is proved that this method can be used in real-time position and orientation measurement application for roadheader which has a broad prospect in coal mine engineering.

The LANL/LLNL/AFTAC Black Thunder Coal Mine regional mine monitoring experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pearson, D.C.; Stump, B.W.; Baker, D.F.

Cast blasting operations associated with near surface coal recovery provide relatively large explosive sources that generate regional seismograms of interest in monitoring a Comprehensive Test Ban Treaty (CTBT). This paper describes preliminary results of a series of experiments currently being conducted at the Black Thunder Coal Mine in northeast Wyoming as part of the DOE CTBT Research and Development Program. These experiments are intended to provide an integrated set of near-source and regional seismic data for the purposes of quantifying the coupling and source characterization of the explosions. The focus of this paper is on the types of data beingmore » recovered with some preliminary implications. The Black Thunder experiments are designed to assess three major questions: (1) how many mining explosions produce seismograms at regional distances that will have to be detected, located and ultimately identified by the National Data Center and what are the waveform characteristics of these particular mining explosions; (2) can discrimination techniques based on empirical studies be placed on a firm physical basis so that they can be applied to other regions where there is little monitoring experience; (3) can large scale chemical explosions (possibly mining explosions) be used to calibrate source and propagation path effects to regional stations, can source depth of burial and decoupling effects be studied in such a controlled environment? With these key questions in mind and given the cooperation of the Black Thunder Mine, a suite of experiments have been and are currently being conducted. This paper will describe the experiments and their relevance to CTBT issues.« less

A data mining technique for discovering distinct patterns of hand signs: implications in user training and computer interface design.

PubMed

Ye, Nong; Li, Xiangyang; Farley, Toni

2003-01-15

Hand signs are considered as one of the important ways to enter information into computers for certain tasks. Computers receive sensor data of hand signs for recognition. When using hand signs as computer inputs, we need to (1) train computer users in the sign language so that their hand signs can be easily recognized by computers, and (2) design the computer interface to avoid the use of confusing signs for improving user input performance and user satisfaction. For user training and computer interface design, it is important to have a knowledge of which signs can be easily recognized by computers and which signs are not distinguishable by computers. This paper presents a data mining technique to discover distinct patterns of hand signs from sensor data. Based on these patterns, we derive a group of indistinguishable signs by computers. Such information can in turn assist in user training and computer interface design.

Analysis of Sediment Transport for Rivers in South Korea based on Data Mining technique

NASA Astrophysics Data System (ADS)

Jang, Eun-kyung; Ji, Un; Yeo, Woonkwang

2017-04-01

The purpose of this study is to calculate of sediment discharge assessment using data mining in South Korea. The Model Tree was selected for this study which is the most suitable technique to explicitly analyze the relationship between input and output variables in various and diverse databases among the Data Mining. In order to derive the sediment discharge equation using the Model Tree of Data Mining used the dimensionless variables used in Engelund and Hansen, Ackers and White, Brownlie and van Rijn equations as the analytical condition. In addition, total of 14 analytical conditions were set considering the conditions dimensional variables and the combination conditions of the dimensionless variables and the dimensional variables according to the relationship between the flow and the sediment transport. For each case, the analysis results were analyzed by mean of discrepancy ratio, root mean square error, mean absolute percent error, correlation coefficient. The results showed that the best fit was obtained by using five dimensional variables such as velocity, depth, slope, width and Median Diameter. And closest approximation to the best goodness-of-fit was estimated from the depth, slope, width, main grain size of bed material and dimensionless tractive force and except for the slope in the single variable. In addition, the three types of Model Tree that are most appropriate are compared with the Ackers and White equation which is the best fit among the existing equations, the mean discrepancy ration and the correlation coefficient of the Model Tree are improved compared to the Ackers and White equation.

Growth and Heavy Metal Accumulation of Koelreuteria Paniculata Seedlings and Their Potential for Restoring Manganese Mine Wastelands in Hunan, China

PubMed Central

Huang, Zhihong; Xiang, Wenhua; Ma, Yu’e; Lei, Pifeng; Tian, Dalun; Deng, Xiangwen; Yan, Wende; Fang, Xi

2015-01-01

The planting of trees on mine wastelands is an effective, long-term technique for phytoremediation of heavy metal-contaminated wastes. In this study, a pot experiment with seedlings of Koelreuteria paniculata under six treatments of local mine wastes was designed to determine the major constraints on tree establishment and to evaluate the feasibility of planting K. paniculata on manganese mine wastelands. Results showed that K. paniculata grew well in mine tailings, and also under a regime of equal amounts of mine tailings and soil provided in adjacent halves of pots. In contrast, mine sludge did not favor survival and growth because its clay texture limited fine root development. The bio-concentration factor and the translocation factor were mostly less than 1, indicating a low phytoextraction potential for K. paniculata. K. paniculata is suited to restore manganese mine sludge by mixing the mine sludge with local mine tailings or soil. PMID:25654773

Analog Tools in Digital History Classrooms: An Activity-Theory Case Study of Learning Opportunities in Digital Humanities

ERIC Educational Resources Information Center

Craig, Kalani

2017-01-01

Digital humanities is often presented as classroom savior, a narrative that competes against the idea that technology virtually guarantees student distraction. However, these arguments are often based on advocacy and anecdote, so we lack systematic research that explores the effect of digital-humanities tools and techniques such as text mining,…

Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome.

PubMed

Gurbaxani, Brian M; Jones, James F; Goertzel, Benjamin N; Maloney, Elizabeth M

2006-04-01

To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques. An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters. Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation. Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

PubMed Central

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

PubMed

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

An Expertise Recommender using Web Mining

NASA Technical Reports Server (NTRS)

Joshi, Anupam; Chandrasekaran, Purnima; ShuYang, Michelle; Ramakrishnan, Ramya

2001-01-01

This report explored techniques to mine web pages of scientists to extract information regarding their expertise, build expertise chains and referral webs, and semi automatically combine this information with directory information services to create a recommender system that permits query by expertise. The approach included experimenting with existing techniques that have been reported in research literature in recent past , and adapted them as needed. In addition, software tools were developed to capture and use this information.

Assessing the Ability of Hyperspectral Data to Detect Lyngbya SPP.: A Potential Biological Indicator for Presence of Metal Objects in the Littoral Environment

DTIC Science & Technology

2006-12-01

environment. This concept would have potential benefits and applications in mine detection and countermeasure techniques. Using a USB2000 field...be distinguished by a different phycocyanin absorption, at 615-632 nm. 15. NUMBER OF PAGES 261 14. SUBJECT TERMS Hyperspectral... applications in mine detection and countermeasure techniques. Using a USB2000 field spectroradiometer, a spectral library was developed for the

The effects of different representations on static structure analysis of computer malware signatures.

PubMed

Narayanan, Ajit; Chen, Yi; Pang, Shaoning; Tao, Ban

2013-01-01

The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka.

The Effects of Different Representations on Static Structure Analysis of Computer Malware Signatures

PubMed Central

Narayanan, Ajit; Chen, Yi; Pang, Shaoning; Tao, Ban

2013-01-01

The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka. PMID:23983644

Machine learning approaches to analysing textual injury surveillance data: a systematic review.

PubMed

Vallmuur, Kirsten

2015-06-01

To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Systematic review. The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field. Copyright © 2015 Elsevier Ltd. All rights reserved.

Data mining for the identification of metabolic syndrome status

PubMed Central

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS. PMID:29383020

Data mining for the identification of metabolic syndrome status.

PubMed

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

Slope stability radar for monitoring mine walls

NASA Astrophysics Data System (ADS)

Reeves, Bryan; Noon, David A.; Stickley, Glen F.; Longstaff, Dennis

2001-11-01

Determining slope stability in a mining operation is an important task. This is especially true when the mine workings are close to a potentially unstable slope. A common technique to determine slope stability is to monitor the small precursory movements, which occur prior to collapse. The slope stability radar has been developed to remotely scan a rock slope to continuously monitor the spatial deformation of the face. Using differential radar interferometry, the system can detect deformation movements of a rough wall with sub-millimeter accuracy, and with high spatial and temporal resolution. The effects of atmospheric variations and spurious signals can be reduced via signal processing means. The advantage of radar over other monitoring techniques is that it provides full area coverage without the need for mounted reflectors or equipment on the wall. In addition, the radar waves adequately penetrate through rain, dust and smoke to give reliable measurements, twenty-four hours a day. The system has been trialed at three open-cut coal mines in Australia, which demonstrated the potential for real-time monitoring of slope stability during active mining operations.

Fluid placement of fixated scrubber sludge to reduce surface subsidence and to abate acid mine drainage in abandoned underground coal mines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meiers, R.J.; Golden, D.; Gray, R.

1995-12-31

Indianapolis Power and Light Company (IPL) began researching the use of fluid placement techniques of the fixated scrubber sludge (FSS) to reduce surface subsidence from underground coal mines to develop an economic alternative to low strength concrete grout. Abandoned underground coal mines surround property adjacent to IPL`s coal combustion by-product (CCBP) landfill at the Petersburg Generating Station. Landfill expansion into these areas is in question because of the high potential for sinkhole subsidence to develop. Sinkholes manifesting at the surface would put the integrity of a liner or runoff pond containment structure for a CCBP disposal facility at risk. Themore » fluid placement techniques of the FSS as a subsidence abatement technology was demonstrated during an eight week period in September, October, and November 1994 at the Petersburg Generating Station. The success of this technology will be determined by the percentage of the mine void filled, strength of the FSS placed, and the overall effects on the hydrogeologic environment. The complete report for this project will be finalized in early 1996.« less

Industrial application of semantic process mining

NASA Astrophysics Data System (ADS)

Espen Ingvaldsen, Jon; Atle Gulla, Jon

2012-05-01

Process mining relates to the extraction of non-trivial and useful information from information system event logs. It is a new research discipline that has evolved significantly since the early work on idealistic process logs. Over the last years, process mining prototypes have incorporated elements from semantics and data mining and targeted visualisation techniques that are more user-friendly to business experts and process owners. In this article, we present a framework for evaluating different aspects of enterprise process flows and address practical challenges of state-of-the-art industrial process mining. We also explore the inherent strengths of the technology for more efficient process optimisation.

LANDSAT inventory of surface-mined areas using extendible digital techniques

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schultz, D. T.; Buchman, N.

1975-01-01

Multispectral LANDSAT imagery was analyzed to provide a rapid and accurate means of identification, classification, and measurement of strip-mined surfaces in Western Maryland. Four band analysis allows distinction of a variety of strip-mine associated classes, but has limited extendibility. A method for surface area measurements of strip mines, which is both geographically and temporally extendible, has been developed using band-ratioed LANDSAT reflectance data. The accuracy of area measurement by this method, averaged over three LANDSAT scenes taken between September 1972 and July 1974, is greater than 93%. Total affected acreage of large (50 hectare/124 acre) mines can be measured to within 1.0%.

Open-source tools for data mining.

PubMed

Zupan, Blaz; Demsar, Janez

2008-03-01

With a growing volume of biomedical databases and repositories, the need to develop a set of tools to address their analysis and support knowledge discovery is becoming acute. The data mining community has developed a substantial set of techniques for computational treatment of these data. In this article, we discuss the evolution of open-source toolboxes that data mining researchers and enthusiasts have developed over the span of a few decades and review several currently available open-source data mining suites. The approaches we review are diverse in data mining methods and user interfaces and also demonstrate that the field and its tools are ready to be fully exploited in biomedical research.

A data mining methodology for predicting early stage Parkinson’s disease using non-invasive, high-dimensional gait sensor data

PubMed Central

Tucker, Conrad; Han, Yixiang; Nembhard, Harriet Black; Lewis, Mechelle; Lee, Wang-Chien; Sterling, Nicholas W; Huang, Xuemei

2017-01-01

Parkinson’s disease (PD) is the second most common neurological disorder after Alzheimer’s disease. Key clinical features of PD are motor-related and are typically assessed by healthcare providers based on qualitative visual inspection of a patient’s movement/gait/posture. More advanced diagnostic techniques such as computed tomography scans that measure brain function, can be cost prohibitive and may expose patients to radiation and other harmful effects. To mitigate these challenges, and open a pathway to remote patient-physician assessment, the authors of this work propose a data mining driven methodology that uses low cost, non-invasive sensors to model and predict the presence (or lack therefore) of PD movement abnormalities and model clinical subtypes. The study presented here evaluates the discriminative ability of non-invasive hardware and data mining algorithms to classify PD cases and controls. A 10-fold cross validation approach is used to compare several data mining algorithms in order to determine that which provides the most consistent results when varying the subject gait data. Next, the predictive accuracy of the data mining model is quantified by testing it against unseen data captured from a test pool of subjects. The proposed methodology demonstrates the feasibility of using non-invasive, low cost, hardware and data mining models to monitor the progression of gait features outside of the traditional healthcare facility, which may ultimately lead to earlier diagnosis of emerging neurological diseases. PMID:29541376

Process Mining-Based Method of Designing and Optimizing the Layouts of Emergency Departments in Hospitals.

PubMed

Rismanchian, Farhood; Lee, Young Hoon

2017-07-01

This article proposes an approach to help designers analyze complex care processes and identify the optimal layout of an emergency department (ED) considering several objectives simultaneously. These objectives include minimizing the distances traveled by patients, maximizing design preferences, and minimizing the relocation costs. Rising demand for healthcare services leads to increasing demand for new hospital buildings as well as renovating existing ones. Operations management techniques have been successfully applied in both manufacturing and service industries to design more efficient layouts. However, high complexity of healthcare processes makes it challenging to apply these techniques in healthcare environments. Process mining techniques were applied to address the problem of complexity and to enhance healthcare process analysis. Process-related information, such as information about the clinical pathways, was extracted from the information system of an ED. A goal programming approach was then employed to find a single layout that would simultaneously satisfy several objectives. The layout identified using the proposed method improved the distances traveled by noncritical and critical patients by 42.2% and 47.6%, respectively, and minimized the relocation costs. This study has shown that an efficient placement of the clinical units yields remarkable improvements in the distances traveled by patients.

Geomorphic reclmation of a coal refuse pile

NASA Astrophysics Data System (ADS)

Hopkinson, L. C.; Quaranta, J.

2017-12-01

Geomorphic reclamation is a technique that may offer opportunities to improve mine reclamation in Central Appalachia. The design approach is based on constructing a steady-state, mature landform condition and takes into account the long-term climatic conditions, soil types, terrain grade, and vegetation. Geomorphic reclamation has been applied successfully in semi-arid regions but has not yet been applied in Central Appalachia. This work describes a demonstration study where geomorphic landforming techniques are being applied to a coarse coal refuse pile in southern West Virginia, USA. The reclamation design includes four geomorphic watersheds that radially drain runoff from the pile. Each watershed has one central draining channel and incorporates compound slope profiles similarly to naturally eroded slopes. Planar slopes were also included to maintain the impacted area. The intent is to alter the hydrology to decrease water quality treatment costs. The excavation cut and fill volumes are comparable to those of more conventional refuse pile reclamation designs. If proven successful then this technique can be part of a cost-effective solution to improve water quality at active and future refuse facilities, abandoned mine lands, bond forfeiture sites, landfills, and major earthmoving activities within the region.

Blasting Rocks and Blasting Cars Applied Engineering

ScienceCinema

LBNL

2017-12-09

June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated ... June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated a program at Berkeley Lab funded under the Partnership for a New Generation of Vehicles, a collaboration between the federal government and the U.S. Council for Automotive Research. Nondestructive evaluation techniques to test a car's structural integrity are being developed for auto assembly lines.

Online breakage detection of multitooth tools using classifier ensembles for imbalanced data

NASA Astrophysics Data System (ADS)

Bustillo, Andrés; Rodríguez, Juan J.

2014-12-01

Cutting tool breakage detection is an important task, due to its economic impact on mass production lines in the automobile industry. This task presents a central limitation: real data-sets are extremely imbalanced because breakage occurs in very few cases compared with normal operation of the cutting process. In this paper, we present an analysis of different data-mining techniques applied to the detection of insert breakage in multitooth tools. The analysis applies only one experimental variable: the electrical power consumption of the tool drive. This restriction profiles real industrial conditions more accurately than other physical variables, such as acoustic or vibration signals, which are not so easily measured. Many efforts have been made to design a method that is able to identify breakages with a high degree of reliability within a short period of time. The solution is based on classifier ensembles for imbalanced data-sets. Classifier ensembles are combinations of classifiers, which in many situations are more accurate than individual classifiers. Six different base classifiers are tested: Decision Trees, Rules, Naïve Bayes, Nearest Neighbour, Multilayer Perceptrons and Logistic Regression. Three different balancing strategies are tested with each of the classifier ensembles and compared to their performance with the original data-set: Synthetic Minority Over-Sampling Technique (SMOTE), undersampling and a combination of SMOTE and undersampling. To identify the most suitable data-mining solution, Receiver Operating Characteristics (ROC) graph and Recall-precision graph are generated and discussed. The performance of logistic regression ensembles on the balanced data-set using the combination of SMOTE and undersampling turned out to be the most suitable technique. Finally a comparison using industrial performance measures is presented, which concludes that this technique is also more suited to this industrial problem than the other techniques presented in the bibliography.

An Evaluation of Pixel-Based Methods for the Detection of Floating Objects on the Sea Surface

NASA Astrophysics Data System (ADS)

Borghgraef, Alexander; Barnich, Olivier; Lapierre, Fabian; Van Droogenbroeck, Marc; Philips, Wilfried; Acheroy, Marc

2010-12-01

Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperform the more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene.

Trends in Fetal Medicine: A 10-Year Bibliometric Analysis of Prenatal Diagnosis

PubMed Central

Dhombres, Ferdinand; Bodenreider, Olivier

2018-01-01

The objective is to automatically identify trends in Fetal Medicine over the past 10 years through a bibliometric analysis of articles published in Prenatal Diagnosis, using text mining techniques. We processed 2,423 full-text articles published in Prenatal Diagnosis between 2006 and 2015. We extracted salient terms, calculated their frequencies over time, and established evolution profiles for terms, from which we derived falling, stable, and rising trends. We identified 618 terms with a falling trend, 2,142 stable terms, and 839 terms with a rising trend. Terms with increasing frequencies include those related to statistics and medical study design. The most recent of these terms reflect the new opportunities of next- generation sequencing. Many terms related to cytogenetics exhibit a falling trend. A bibliometric analysis based on text mining effectively supports identification of trends over time. This scalable approach is complementary to analyses based on metadata or expert opinion. PMID:29295220

Mining High-Dimensional Data

NASA Astrophysics Data System (ADS)

Wang, Wei; Yang, Jiong

With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality.

The application of satellite data in monitoring strip mines

NASA Technical Reports Server (NTRS)

Sharber, L. A.; Shahrokhi, F.

1977-01-01

Strip mines in the New River Drainage Basin of Tennessee were studied through use of Landsat-1 imagery and aircraft photography. A multilevel analysis, involving conventional photo interpretation techniques, densitometric methods, multispectral analysis and statistical testing was applied to the data. The Landsat imagery proved adequate for monitoring large-scale change resulting from active mining and land-reclamation projects. However, the spatial resolution of the satellite imagery rendered it inadequate for assessment of many smaller strip mines, in the region which may be as small as a few hectares.

Combined data mining/NIR spectroscopy for purity assessment of lime juice

NASA Astrophysics Data System (ADS)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

Large Scale Data Mining to Improve Usability of Data: An Intelligent Archive Testbed

NASA Technical Reports Server (NTRS)

Ramapriyan, Hampapuram; Isaac, David; Yang, Wenli; Morse, Steve

2005-01-01

Research in certain scientific disciplines - including Earth science, particle physics, and astrophysics - continually faces the challenge that the volume of data needed to perform valid scientific research can at times overwhelm even a sizable research community. The desire to improve utilization of this data gave rise to the Intelligent Archives project, which seeks to make data archives active participants in a knowledge building system capable of discovering events or patterns that represent new information or knowledge. Data mining can automatically discover patterns and events, but it is generally viewed as unsuited for large-scale use in disciplines like Earth science that routinely involve very high data volumes. Dozens of research projects have shown promising uses of data mining in Earth science, but all of these are based on experiments with data subsets of a few gigabytes or less, rather than the terabytes or petabytes typically encountered in operational systems. To bridge this gap, the Intelligent Archives project is establishing a testbed with the goal of demonstrating the use of data mining techniques in an operationally-relevant environment. This paper discusses the goals of the testbed and the design choices surrounding critical issues that arose during testbed implementation.

Assessing semantic similarity of texts - Methods and algorithms

NASA Astrophysics Data System (ADS)

Rozeva, Anna; Zerkova, Silvia

2017-12-01

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.

A review of contrast pattern based data mining

NASA Astrophysics Data System (ADS)

Zhu, Shiwei; Ju, Meilong; Yu, Junfeng; Cai, Binlei; Wang, Aiping

2015-07-01

Contrast pattern based data mining is concerned with the mining of patterns and models that contrast two or more datasets. Contrast patterns can describe similarities or differences between the datasets. They represent strong contrast knowledge and have been shown to be very successful for constructing accurate and robust clusters and classifiers. The increasing use of contrast pattern data mining has initiated a great deal of research and development attempts in the field of data mining. A comprehensive revision on the existing contrast pattern based data mining research is given in this paper. They are generally categorized into background and representation, definitions and mining algorithms, contrast pattern based classification, clustering, and other applications, the research trends in future. The primary of this paper is to server as a glossary for interested researchers to have an overall picture on the current contrast based data mining development and identify their potential research direction to future investigation.

Data mining of air traffic control operational errors

DOT National Transportation Integrated Search

2006-01-01

In this paper we present the results of : applying data mining techniques to identify patterns and : anomalies in air traffic control operational errors (OEs). : Reducing the OE rate is of high importance and remains a : challenge in the aviation saf...

Application of a Novel Liquid Nitrogen Control Technique for Heat Stress and Fire Prevention in Underground Mines.

PubMed

Shi, Bobo; Ma, Lingjun; Dong, Wei; Zhou, Fubao

2015-01-01

With the continually increasing mining depths, heat stress and spontaneous combustion hazards in high-temperature mines are becoming increasingly severe. Mining production risks from natural hazards and exposures to hot and humid environments can cause occupational diseases and other work-related injuries. Liquid nitrogen injection, an engineering control developed to reduce heat stress and spontaneous combustion hazards in mines, was successfully utilized for environmental cooling and combustion prevention in an underground mining site named "Y120205 Working Face" (Y120205 mine) of Yangchangwan colliery. Both localized humidities and temperatures within the Y120205 mine decreased significantly with liquid nitrogen injection. The maximum percentage drop in temperature and humidity of the Y120205 mine were 21.9% and 10.8%, respectively. The liquid nitrogen injection system has the advantages of economical price, process simplicity, energy savings and emission reduction. The optimized heat exchanger used in the liquid nitrogen injection process achieved superior air-cooling results, resulting in considerable economic benefits.

Flight State Information Inference with Application to Helicopter Cockpit Video Data Analysis Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Shin, Sanghyun

The National Transportation Safety Board (NTSB) has recently emphasized the importance of analyzing flight data as one of the most effective methods to improve eciency and safety of helicopter operations. By analyzing flight data with Flight Data Monitoring (FDM) programs, the safety and performance of helicopter operations can be evaluated and improved. In spite of the NTSB's effort, the safety of helicopter operations has not improved at the same rate as the safety of worldwide airlines, and the accident rate of helicopters continues to be much higher than that of fixed-wing aircraft. One of the main reasons is that the participation rates of the rotorcraft industry in the FDM programs are low due to the high costs of the Flight Data Recorder (FDR), the need of a special readout device to decode the FDR, anxiety of punitive action, etc. Since a video camera is easily installed, accessible, and inexpensively maintained, cockpit video data could complement the FDR in the presence of the FDR or possibly replace the role of the FDR in the absence of the FDR. Cockpit video data is composed of image and audio data: image data contains outside views through cockpit windows and activities on the flight instrument panels, whereas audio data contains sounds of the alarms within the cockpit. The goal of this research is to develop, test, and demonstrate a cockpit video data analysis algorithm based on data mining and signal processing techniques that can help better understand situations in the cockpit and the state of a helicopter by efficiently and accurately inferring the useful flight information from cockpit video data. Image processing algorithms based on data mining techniques are proposed to estimate a helicopter's attitude such as the bank and pitch angles, identify indicators from a flight instrument panel, and read the gauges and the numbers in the analogue gauge indicators and digital displays from cockpit image data. In addition, an audio processing algorithm based on signal processing and abrupt change detection techniques is proposed to identify types of warning alarms and to detect the occurrence times of individual alarms from cockpit audio data. Those proposed algorithms are then successfully applied to simulated and real helicopter cockpit video data to demonstrate and validate their performance.

From Process Models to Decision Making: The Use of Data Mining Techniques for Developing Effect Decision Support Systems

NASA Astrophysics Data System (ADS)

Conrads, P. A.; Roehl, E. A.

2010-12-01

Natural-resource managers face the difficult problem of controlling the interactions between hydrologic and man-made systems in ways that preserve resources while optimally meeting the needs of disparate stakeholders. Finding success depends on obtaining and employing detailed scientific knowledge about the cause-effect relations that govern the physics of these hydrologic systems. This knowledge is most credible when derived from large field-based datasets that encompass the wide range of variability in the parameters of interest. The means of converting data into knowledge of the hydrologic system often involves developing computer models that predict the consequences of alternative management practices to guide resource managers towards the best path forward. Complex hydrologic systems are typically modeled using computer programs that implement traditional, generalized, physical equations, which are calibrated to match the field data as closely as possible. This type of model commonly is limited in terms of demonstrable predictive accuracy, development time, and cost. The science of data mining presents a powerful complement to physics-based models. Data mining is a relatively new science that assists in converting large databases into knowledge and is uniquely able to leverage the real-time, multivariate data now being collected for hydrologic systems. In side-by-side comparisons with state-of-the-art physics-based hydrologic models, the authors have found data-mining solutions have been substantially more accurate, less time consuming to develop, and embeddable into spreadsheets and sophisticated decision support systems (DSS), making them easy to use by regulators and stakeholders. Three data-mining applications will be presented that demonstrate how data-mining techniques can be applied to existing environmental databases to address regional concerns of long-term consequences. In each case, data were transformed into information, and ultimately, into knowledge. In each case, DSSs were developed that facilitated the use of simulation models and analysis of model output to a broad range of end users with various technical abilities. When compared to other modeling projects of comparable scope and complexity, these DSSs were able to pass through needed technical reviews much more quickly. Unlike programs such as finite-element flow models, DSSs are by design open systems that are easy to use and readily disseminated directly to decision makers. The DSSs provide direct coupling of predictive models with the real-time databases that drive them, graphical user interfaces for point-and-click program control, and streaming displays of numerical and graphical results so that users can monitor the progress of long-term simulations. Customizations for specific problems include numerical optimization loops that invert predictive models; integrations with a three-dimensional finite-element flow model, GIS packages, and a plant ecology model; and color contouring of simulation output data.

UNDERGROUNG PLACEMENT OF COAL PROCESSING WASTE AND COAL COMBUSTION BY-PRODUCTS BASED PASTE BACKFILL FOR ENHANCED MINING ECONOMICS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Y.P. Chugh; D. Biswas; D. Deb

2002-06-01

This project has successfully demonstrated that the extraction ratio in a room-and-pillar panel at an Illinois mine can be increased from the current value of approximately 56% to about 64%, with backfilling done from the surface upon completion of all mining activities. This was achieved without significant ground control problems due to the increased extraction ratio. The mined-out areas were backfilled from the surface with gob, coal combustion by-products (CCBs), and fine coal processing waste (FCPW)-based paste backfill containing 65%-70% solids to minimize short-term and long-term surface deformations risk. This concept has the potential to increase mine productivity, reduce miningmore » costs, manage large volumes of CCBs beneficially, and improve the miner's health, safety, and environment. Two injection holes were drilled over the demonstration panel to inject the paste backfill. Backfilling was started on August 11, 1999 through the first borehole. About 9,293 tons of paste backfill were injected through this borehole with a maximum flow distance of 300-ft underground. On September 27, 2000, backfilling operation was resumed through the second borehole with a mixture of F ash and FBC ash. A high-speed auger mixer (new technology) was used to mix solids with water. About 6,000 tons of paste backfill were injected underground through this hole. Underground backfilling using the ''Groutnet'' flow model was simulated. Studies indicate that grout flow over 300-foot distance is possible. Approximately 13,000 tons of grout may be pumped through a single hole. The effect of backfilling on the stability of the mine workings was analyzed using SIUPANEL.3D computer program and further verified using finite element analysis techniques. Stiffness of the backfill mix is most critical for enhancing the stability of mine workings. Mine openings do not have to be completely backfilled to enhance their stability. Backfill height of about 50% of the seam height is adequate to minimize surface deformations. Freeman United Coal Company performed engineering economic evaluation studies for commercialization. They found that the costs for underground management at the Crown III mine would be slightly higher than surface management at this time. The developed technologies have commercial potential but each site must be analyzed on its merit. The Company maintains significant interest in commercializing the technology.« less

Remote Sensing based multi-temporal observation of North Korea mining activities : A case study of Rakyeon mine

NASA Astrophysics Data System (ADS)

Lim, J. H.; Yu, J.; Koh, S. M.; Lee, G.

2017-12-01

Mining is a major industrial business of North Korea accounting for significant portion of an export for North Korean economy. However, due to its veiled political system, details of mining activities of North Korea is rarely known. This study investigated mining activities of Rakyeon Au-Ag mine, North Korea based on remote sensing based multi-temporal observation. To monitor the mining activities, CORONA data acquired in 1960s and 1970s, SPOT and Landsat data acquired in 1980s and 1990s and KOMPSAT-2 data acquired in 2010s are utilized. The results show that mining activities of Rakyeon mine continuously carried out for the observation period expanding tailing areas of the mine. However, its expanding rate varies between the period related to North Korea's economic and political situations.

Geophysical Technologies to Image Old Mine Works

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kanaan Hanna; Jim Pfeiffer

2007-01-15

ZapataEngineering, Blackhawk Division performed geophysical void detection demonstrations for the US Department of Labor Mine Safety and Health Administration (MSHA). The objective was to advance current state-of-practices of geophysical technologies for detecting underground mine voids. The presence of old mine works above, adjacent, or below an active mine presents major health and safety hazards to miners who have inadvertently cut into locations with such features. In addition, the presence of abandoned mines or voids beneath roadways and highway structures may greatly impact the performance of the transportation infrastructure in terms of cost and public safety. Roads constructed over abandoned minesmore » are subject to potential differential settlement, subsidence, sinkholes, and/or catastrophic collapse. Thus, there is a need to utilize geophysical imaging technologies to accurately locate old mine works. Several surface and borehole geophysical imaging methods and mapping techniques were employed at a known abandoned coal mine in eastern Illinois to investigate which method best map the location and extent of old works. These methods included: 1) high-resolution seismic (HRS) using compressional P-wave (HRPW) and S-wave (HRSW) reflection collected with 3-D techniques; 2) crosshole seismic tomography (XHT); 3) guided waves; 4) reverse vertical seismic profiling (RVSP); and 5) borehole sonar mapping. In addition, several exploration borings were drilled to confirm the presence of the imaged mine voids. The results indicated that the RVSP is the most viable method to accurately detect the subsurface voids with horizontal accuracy of two to five feet. This method was then applied at several other locations in Colorado with various topographic, geologic, and cultural settings for the same purpose. This paper presents the significant results obtained from the geophysical investigations in Illinois.« less

Design risk assessment for burst-prone mines: Application in a Canadian mine

NASA Astrophysics Data System (ADS)

Cheung, David J.

A proactive stance towards improving the effectiveness and consistency of risk assessments has been adopted recently by mining companies and industry. The next 10-20 years forecasts that ore deposits accessible using shallow mining techniques will diminish. The industry continues to strive for success in "deeper" mining projects in order to keep up with the continuing demand for raw materials. Although the returns are quite profitable, many projects have been sidelined due to high uncertainty and technical risk in the mining of the mineral deposit. Several hardrock mines have faced rockbursting and seismicity problems. Within those reported, mines in countries like South Africa, Australia and Canada have documented cases of severe rockburst conditions attributed to the mining depth. Severe rockburst conditions known as "burst-prone" can be effectively managed with design. Adopting a more robust design can ameliorate the exposure of workers and equipment to adverse conditions and minimize the economic consequences, which can hinder the bottom line of an operation. This thesis presents a methodology created for assessing the design risk in burst-prone mines. The methodology includes an evaluation of relative risk ratings for scenarios with options of risk reduction through several design principles. With rockbursts being a hazard of seismic events, the methodology is based on research in the area of mining seismicity factoring in rockmass failure mechanisms, which results from a combination of mining induced stress, geological structures, rockmass properties and mining influences. The methodology was applied to case studies at Craig Mine of Xstrata Nickel in Sudbury, Ontario, which is known to contain seismically active fault zones. A customized risk assessment was created and applied to rockburst case studies, evaluating the seismic vulnerability and consequence for each case. Application of the methodology to Craig Mine demonstrates that changes in the design can reduce both exposure risk (personnel and equipment), and economical risk (revenue and costs). Fatal and catastrophic consequences can be averted through robust planning and design. Two customized approaches were developed to conduct risk assessment of case studies at Craig Mine. Firstly, the Brownfield Approach utilizes the seismic database to determine the seismic hazard from a rating system that evaluates frequency-magnitude, event size, and event-blast relation. Secondly, the Greenfield Approach utilizes the seismic database, focusing on larger magnitude events, rocktype, and geological structure. The customized Greenfield Approach can also be applied in the evaluation of design risk in deep mines with the same setting and condition as Craig Mine. Other mines with different settings and conditions can apply the principles in the methodology to evaluate design alternatives and risk reduction strategies for burst-prone mines.

Detection of buried objects by fusing dual-band infrared images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-11-01

We have conducted experiments to demonstrate the enhanced detectability of buried land mines using sensor fusion techniques. Multiple sensors, including visible imagery, infrared imagery, and ground penetrating radar (GPR), have been used to acquire data on a number of buried mines and mine surrogates. Because the visible wavelength and GPR data are currently incomplete. This paper focuses on the fusion of two-band infrared images. We use feature-level fusion and supervised learning with the probabilistic neural network (PNN) to evaluate detection performance. The novelty of the work lies in the application of advanced target recognition algorithms, the fusion of dual-band infraredmore » images and evaluation of the techniques using two real data sets.« less

Hydraulic hoisting and backfilling

NASA Astrophysics Data System (ADS)

Sauermann, H. B.

In a country such as South Africa, with its large deep level mining industry, improvements in mining and hoisting techniques could result in substantial savings. Hoisting techniques, for example, may be improved by the introduction of hydraulic hoisting. The following are some of the advantages of hydraulic hoisting as against conventional skip hoisting: (1) smaller shafts are required because the pipes to hoist the same quantity of ore hydraulically require less space in the shaft than does skip hoisting equipment; (2) the hoisting capacity of a mine can easily be increased without the necessity of sinking new shafts. Large savings in capital costs can thus be made; (3) fully automatic control is possible with hydraulic hoisting and therefore less manpower is required; and (4) health and safety conditions will be improved.

Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses.

PubMed

Magenes, G; Bellazzi, R; Malovini, A; Signorini, M G

2016-08-01

The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying data mining techniques to FHR parameters, obtained from recordings in a population of 122 fetuses (61 healthy and 61 IUGRs), through standard CTG non-stress test. We computed N=12 indices (N=4 related to time domain FHR analysis, N=4 to frequency domain and N=4 to non-linear analysis) and normalized them with respect to the gestational week. We compared, through a 10-fold crossvalidation procedure, 15 data mining techniques in order to select the more reliable approach for identifying IUGR fetuses. The results of this comparison highlight that two techniques (Random Forest and Logistic Regression) show the best classification accuracy and that both outperform the best single parameter in terms of mean AUROC on the test sets.

Sustainable rehabilitation of mining waste and acid mine drainage using geochemistry, mine type, mineralogy, texture, ore extraction and climate knowledge.

PubMed

Anawar, Hossain Md

2015-08-01

The oxidative dissolution of sulfidic minerals releases the extremely acidic leachate, sulfate and potentially toxic elements e.g., As, Ag, Cd, Cr, Cu, Hg, Ni, Pb, Sb, Th, U, Zn, etc. from different mine tailings and waste dumps. For the sustainable rehabilitation and disposal of mining waste, the sources and mechanisms of contaminant generation, fate and transport of contaminants should be clearly understood. Therefore, this study has provided a critical review on (1) recent insights in mechanisms of oxidation of sulfidic minerals, (2) environmental contamination by mining waste, and (3) remediation and rehabilitation techniques, and (4) then developed the GEMTEC conceptual model/guide [(bio)-geochemistry-mine type-mineralogy- geological texture-ore extraction process-climatic knowledge)] to provide the new scientific approach and knowledge for remediation of mining wastes and acid mine drainage. This study has suggested the pre-mining geological, geochemical, mineralogical and microtextural characterization of different mineral deposits, and post-mining studies of ore extraction processes, physical, geochemical, mineralogical and microbial reactions, natural attenuation and effect of climate change for sustainable rehabilitation of mining waste. All components of this model should be considered for effective and integrated management of mining waste and acid mine drainage. Copyright © 2015 Elsevier Ltd. All rights reserved.

75 FR 48366 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-10

...: OMB Desk Officer for the Department of Labor--Mine Safety and Health Administration (MSHA), Office of..., electronic, mechanical, or other technological collection techniques or other forms of information technology, e.g., permitting electronic submission of responses. Agency: Mine Safety and Health Administration...

Content based image retrieval using local binary pattern operator and data mining techniques.

PubMed

Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

2015-01-01

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

Cross-industry standard process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management.

PubMed

Rivo, Eduardo; de la Fuente, Javier; Rivo, Ángel; García-Fontán, Eva; Cañizares, Miguel-Ángel; Gil, Pedro

2012-01-01

The aim of this study was to assess the applicability of knowledge discovery in database methodology, based upon data mining techniques, to the investigation of lung cancer surgery. According to CRISP 1.0 methodology, a data mining (DM) project was developed on a data warehouse containing records for 501 patients operated on for lung cancer with curative intention. The modelling technique was logistic regression. The finally selected model presented the following values: sensitivity 9.68%, specificity 100%, global precision 94.02%, positive predictive value 100% and negative predictive value 93.98% for a cut-off point set at 0.5. A receiver operating characteristic (ROC) curve was constructed. The area under the curve (CI 95%) was 0.817 (0.740- 0.893) (p < 0.05). Statistical association with perioperative mortality was found for the following variables [odds ratio (CI 95%)]: age over 70 [2.3822 (1.0338-5.4891)], heart disease [2.4875 (1.0089-6.1334)], peripheral arterial disease [5.7705 (1.9296-17.2570)], pneumonectomy [3.6199 (1.4939-8.7715)] and length of surgery (min) [1.0067 (1.0008-1.0126)]. The CRISP-DM process model is very suitable for lung cancer surgery analysis, improving decision making as well as knowledge and quality management.

Study of application of ERTS-A imagery to fracture-related mine safety hazards in the coal mining industry

NASA Technical Reports Server (NTRS)

Wier, C. E.; Wobber, F. J. (Principal Investigator); Russell, O. R.; Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. The Kings Station Mine in Gibson County, Indiana has experienced considerable roof fall problems. Detailed fracture mapping of the mine area was done with ERTS-1 and aircraft imagery, and a prediction map of roof problem areas was produced in advance of a visit. The visit to the mine and discussions with the operator indicated that of four zones mapped as potential problem areas, three coincided with areas of excessive roof fall. This positive correlation of 75% lends confidence to the validity of the technique being applied in the investigation. The mine officials expressed an interest in the project and are anxious to see the final product maps which are forthcoming.

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

PubMed Central

Sun, Chongjing; Fu, Yan; Zhou, Junlin; Gao, Hui

2014-01-01

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy. PMID:25143989

Personalized privacy-preserving frequent itemset mining using randomized response.

PubMed

Sun, Chongjing; Fu, Yan; Zhou, Junlin; Gao, Hui

2014-01-01

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.

Data Mining and Complex Problems: Case Study in Composite Materials

NASA Technical Reports Server (NTRS)

Rabelo, Luis; Marin, Mario

2009-01-01

Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

Application of geostatistics to coal-resource characterization and mine planning. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kauffman, P.W.; Walton, D.R.; Martuneac, L.

1981-12-01

Geostatistics is a proven method of ore reserve estimation in many non-coal mining areas but little has been published concerning its application to coal resources. This report presents the case for using geostatistics for coal mining applications and describes how a coal mining concern can best utilize geostatistical techniques for coal resource characterization and mine planning. An overview of the theory of geostatistics is also presented. Many of the applications discussed are documented in case studies that are a part of the report. The results of an exhaustive literature search are presented and recommendations are made for needed future researchmore » and demonstration projects.« less

Web-video-mining-supported workflow modeling for laparoscopic surgeries.

PubMed

Liu, Rui; Zhang, Xiaoli; Zhang, Hao

2016-11-01

As quality assurance is of strong concern in advanced surgeries, intelligent surgical systems are expected to have knowledge such as the knowledge of the surgical workflow model (SWM) to support their intuitive cooperation with surgeons. For generating a robust and reliable SWM, a large amount of training data is required. However, training data collected by physically recording surgery operations is often limited and data collection is time-consuming and labor-intensive, severely influencing knowledge scalability of the surgical systems. The objective of this research is to solve the knowledge scalability problem in surgical workflow modeling with a low cost and labor efficient way. A novel web-video-mining-supported surgical workflow modeling (webSWM) method is developed. A novel video quality analysis method based on topic analysis and sentiment analysis techniques is developed to select high-quality videos from abundant and noisy web videos. A statistical learning method is then used to build the workflow model based on the selected videos. To test the effectiveness of the webSWM method, 250 web videos were mined to generate a surgical workflow for the robotic cholecystectomy surgery. The generated workflow was evaluated by 4 web-retrieved videos and 4 operation-room-recorded videos, respectively. The evaluation results (video selection consistency n-index ≥0.60; surgical workflow matching degree ≥0.84) proved the effectiveness of the webSWM method in generating robust and reliable SWM knowledge by mining web videos. With the webSWM method, abundant web videos were selected and a reliable SWM was modeled in a short time with low labor cost. Satisfied performances in mining web videos and learning surgery-related knowledge show that the webSWM method is promising in scaling knowledge for intelligent surgical systems. Copyright © 2016 Elsevier B.V. All rights reserved.

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

PubMed Central

Mande, Sharmila S.

2016-01-01

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm. PMID:27124399

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques.

PubMed

Tandon, Disha; Haque, Mohammed Monzoorul; Mande, Sharmila S

2016-01-01

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.

Digital mine claim density map for Federal lands in Montana, 1996

USGS Publications Warehouse

Campbell, Harry W.; Hyndman, Paul C.

1998-01-01

This report describes a digital map and data files generated by the U.S. Geological Survey (USGS) to provide digital spatial mining claim information for Federal lands in Montana as of March, 1997. Statewide, 159,704 claims had been recorded with the Bureau of Land Management since 1975. Of those claims, 21,055 (13%) are still actively held while 138,649 (87%) are closed and are no longer held. Montana contains 147,704 sections (usually 1 section equals 1 square mile) in the Public Land Survey System, with 8,569 sections (6%) containing claim data. Of the sections with claim data, 2,192 (26%) contain actively held claims. Only 1.5% of Montana’s sections contains actively held mining claims. The four types of mining claim are lode, placer, mill, and tunnel. A mill claim may be as much as 5 acres or 1/128th (0.78125%) of a square mile. A lode claim, about 20 acres, would cover 1/32nd (3.125%) of a square mile. Mining claim data is earth science information deemed to be relevant to the assessment of historic, current, and future ecological, economic, and social systems. The digital map and data files that are available in this report are suitable for geographic information system (GIS)-based regional assessments at a scale of 1:100,000 or smaller. Campbell (1996) summarized the methodology and GIS techniques that were used to produce the mining claim density map of the Pacific Northwest. Campbell and Hyndman (1997) displayed mining claim information for the Pacific Northwest that used data acquired in 1994. Appendix A of this report lists the attribute data for the digital data files. Appendix B contains the GIS metadata.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenwalt, R J; Magnoli, D

The purpose of this study is to determine battlefield effectiveness of the self-healing minefield (''Frogs'') concept system compared to basecases of the standard AP/AT (anti-personnel/anti-tank) mixed minefield, the AT (anti-tank) pure minefield, and no minefields. This involves tactical modeling where a basecase with and without mines is compared to the concept system. However, it is first necessary to establish system characteristics and behavior of the Frog mine and minefield in order to do the tactical modeling. This initial report provides emerging insights into various minefield parameters in order to allow better program definition early in the conceptual development. In themore » following sections of this report, we investigate the self-healing minefield's ground pattern and several concepts for movement (''jump'') of a mine. Basic enemy breaching techniques are compared for the different mine movement concepts. These results are then used in the (Joint Conflict and Tactical Simulation) JCATS tactical model to evaluate minefield effects in a combat situation. The three basecases and the Frogs concept are used against a North Korean mechanized rifle battalion and outcomes are compared. Preliminary results indicate: (1) Possible breaching techniques for the self-healing minefield were proposed and compared through simulation modeling. Of these, the best breaching counter to the self-healing minefield is the ''wide-lane'' breach technique. (2) Several methods for mine movement are tested and the optimal method from this group was selected for use in the modeling. However, continued work is needed on jump criteria; a more sophisticated model may reduce the advantage of the breach counter. (3) The battle scenario used in this study is a very difficult defense for Blue. In the three baseline cases (no mines, AT mines only, and mixed AT/AP minefield), Blue loses. Only in the Frog case does Blue win, and it is a high casualty win.« less

Principles and status of neutron-based inspection technologies

NASA Astrophysics Data System (ADS)

Gozani, Tsahi

2011-06-01

Nuclear based explosive inspection techniques can detect a wide range of substances of importance for a wide range of objectives. For national and international security it is mainly the detection of nuclear materials, explosives and narcotic threats. For Customs Services it is also cargo characterization for shipment control and customs duties. For the military and other law enforcement agencies it could be the detection and/or validation of the presence of explosive mines, improvised explosive devices (IED) and unexploded ordnances (UXO). The inspection is generally based on the nuclear interactions of the neutrons (or high energy photons) with the various nuclides present and the detection of resultant characteristic emissions. These can be discrete gamma lines resulting from the thermal neutron capture process (n,γ) or inelastic neutron scattering (n,n'γ) occurring with fast neutrons. The two types of reactions are generally complementary. The capture process provides energetic and highly penetrating gamma rays in most inorganic substances and in hydrogen, while fast neutron inelastic scattering provides relatively strong gamma-ray signatures in light elements such as carbon and oxygen. In some specific important cases unique signatures are provided by the neutron capture process in light elements such as nitrogen, where unusually high-energy gamma ray is produced. This forms the basis for key explosive detection techniques. In some cases the elastically scattered source (of mono-energetic) neutrons may provide information on the atomic weight of the scattering elements. The detection of nuclear materials, both fissionable (e.g., 238U) and fissile (e.g., 235U), are generally based on the fissions induced by the probing neutrons (or photons) and detecting one or more of the unique signatures of the fission process. These include prompt and delayed neutrons and gamma rays. These signatures are not discrete in energy (typically they are continua) but temporally and energetically significantly different from the background, thus making them readily distinguishable. The penetrability of neutrons as probes and signatures as well as the gamma ray signatures make neutron interrogation applicable to the inspection of large conveyances such as cars, trucks, marine containers and also smaller objects like explosive mines concealed in the ground. The application of nuclear interrogation techniques greatly depends on operational requirements. For example explosive mines and IED detection clearly require one-sided inspection, which excludes transmission based inspection (e.g., transmission radiography) and greatly limits others. The technologies developed over the last decades are now being implemented with good results. Further advances have been made over the last several years that increase the sensitivity, applicability and robustness of these systems. The principle, applications and status of neutron-based inspection techniques will be reviewed.

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

75 FR 53345 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-31

... the Department of Labor--Mine Safety and Health Administration (MSHA), Office of Management and Budget..., mechanical, or other technological collection techniques or other forms of information technology, e.g., permitting electronic submission of responses. Agency: Mine Safety and Health Administration. Type of Review...

ASSESSING AND MANAGING MERCURY FROM HISTORIC AND CURRENT MINING ACTIVITIES

EPA Science Inventory

In order for ORD to address uncertainties resulting from past or historical mining practices a technology transfer workshop was conducted in November, 2000 in San Francisco, CA. Two primary objectives for this workshop were: 1) identify state-of-the-science practices and techniqu...

A data mining framework for time series estimation.

PubMed

Hu, Xiao; Xu, Peng; Wu, Shaozhi; Asgari, Shadnaz; Bergsneider, Marvin

2010-04-01

Time series estimation techniques are usually employed in biomedical research to derive variables less accessible from a set of related and more accessible variables. These techniques are traditionally built from systems modeling approaches including simulation, blind decovolution, and state estimation. In this work, we define target time series (TTS) and its related time series (RTS) as the output and input of a time series estimation process, respectively. We then propose a novel data mining framework for time series estimation when TTS and RTS represent different sets of observed variables from the same dynamic system. This is made possible by mining a database of instances of TTS, its simultaneously recorded RTS, and the input/output dynamic models between them. The key mining strategy is to formulate a mapping function for each TTS-RTS pair in the database that translates a feature vector extracted from RTS to the dissimilarity between true TTS and its estimate from the dynamic model associated with the same TTS-RTS pair. At run time, a feature vector is extracted from an inquiry RTS and supplied to the mapping function associated with each TTS-RTS pair to calculate a dissimilarity measure. An optimal TTS-RTS pair is then selected by analyzing these dissimilarity measures. The associated input/output model of the selected TTS-RTS pair is then used to simulate the TTS given the inquiry RTS as an input. An exemplary implementation was built to address a biomedical problem of noninvasive intracranial pressure assessment. The performance of the proposed method was superior to that of a simple training-free approach of finding the optimal TTS-RTS pair by a conventional similarity-based search on RTS features. 2009 Elsevier Inc. All rights reserved.

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

PubMed

Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Herowati, A. J.; Sugiharti, E.; Prasetiyo, B.

2018-03-01

A technique to dig valuable information buried or hidden in data collection which is so big to be found an interesting patterns that was previously unknown is called data mining. Data mining has been applied in the healthcare industry. One technique used data mining is classification. The decision tree included in the classification of data mining and algorithm developed by decision tree is C4.5 algorithm. A classifier is designed using applying pessimistic pruning in C4.5 algorithm in diagnosing chronic kidney disease. Pessimistic pruning use to identify and remove branches that are not needed, this is done to avoid overfitting the decision tree generated by the C4.5 algorithm. In this paper, the result obtained using these classifiers are presented and discussed. Using pessimistic pruning shows increase accuracy of C4.5 algorithm of 1.5% from 95% to 96.5% in diagnosing of chronic kidney disease.

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

NASA Astrophysics Data System (ADS)

Oral, L. O.; Tecim, V.

2013-05-01

Decision makers develop transportation plans and models for providing sustainable transport systems in urban areas. Mode Choice is one of the stages in transportation modelling. Data mining techniques can discover factors affecting the mode choice. These techniques can be applied with knowledge process approach. In this study a data mining process model is applied to determine the factors affecting the mode choice with decision trees techniques by considering individual trip behaviours from household survey data collected within Izmir Transportation Master Plan. From this perspective transport mode choice problem is solved on a case in district of Buca-Izmir, Turkey with CRISP-DM knowledge process model.

Blasting Rocks and Blasting Cars Applied Engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

LBNL

2008-07-02

June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated ... June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated a program at Berkeley Lab funded under the Partnership for a New Generation of Vehicles, a collaboration between the federal government and the U.S. Council for Automotive Research. Nondestructive evaluation techniques to test a car's structural integrity are being developed formore » auto assembly lines.« less

Development of a tree classifier for discrimination of surface mine activity from Landsat digital data

NASA Technical Reports Server (NTRS)

Solomon, J. L.; Miller, W. F.; Quattrochi, D. A.

1979-01-01

In a cooperative project with the Geological Survey of Alabama, the Mississippi State Remote Sensing Applications Program has developed a single purpose, decision-tree classifier using band-ratioing techniques to discriminate various stages of surface mining activity. The tree classifier has four levels and employs only two channels in classification at each level. An accurate computation of the amount of disturbed land resulting from the mining activity can be made as a product of the classification output. The utilization of Landsat data provides a cost-efficient, rapid, and accurate means of monitoring surface mining activities.

Big data mining: In-database Oracle data mining over hadoop

NASA Astrophysics Data System (ADS)

Kovacheva, Zlatinka; Naydenova, Ina; Kaloyanova, Kalinka; Markov, Krasimir

2017-07-01

Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.

Mapping of groundwater potential zones in Salem Chalk Hills, Tamil Nadu, India, using remote sensing and GIS techniques.

PubMed

Thilagavathi, N; Subramani, T; Suresh, M; Karunanidhi, D

2015-04-01

This study proposes to introduce the remote sensing and geographic information system (GIS) techniques in mapping the groundwater potential zones. Remote sensing and GIS techniques have been used to map the groundwater potential zones in Salem Chalk Hills, Tamil Nadu, India. Charnockites and fissile hornblende biotite gneiss are the major rock types in this region. Dunites and peridodites are the ultramafic rocks which cut across the foliation planes of the gneisses and are highly weathered. It comprises magnesite and chromite deposits which are excavated by five mining companies by adopting bench mining. The thickness of weathered and fracture zone varies from 2.2 to 50 m in gneissic formation and 5.8 to 55 m in charnockite. At the contacts of gneiss and charnockite, the thickness ranges from 9.0 to 90.8 m favoring good groundwater potential. The mine lease area is underlined by fractured and sheared hornblende biotite gneiss where groundwater potential is good. Water catchment tanks in this area of 5 km radius are small to moderate in size and are only seasonal. They remain dry during summer seasons. As perennial water resources are remote, the domestic and agricultural activities in this region depend mainly upon the groundwater resources. The mines are located in gently slope area, and accumulation of water is not observed except in mine pits even during the monsoon period. Therefore, it is essential to map the groundwater potential zones for proper management of the aquifer system. Satellite imageries were also used to extract lineaments, hydrogeomorphic landforms, drainage patterns, and land use, which are the major controlling factors for the occurrence of groundwater. Various thematic layers pertaining to groundwater existence such as geology, geomorphology, land use/land cover, lineament, lineament density, drainage, drainage density, slope, and soil were generated using GIS tools. By integrating all the above thematic layers based on the ranks and weightages, eventually groundwater potential zones were demarcated. The study indicates that groundwater potential is good to high in 22 villages and moderate in 13 villages. The good to high potential zone occupies an area of 128 km2 and moderate potential zone occupies an area of 77 km2. Groundwater occurrence is poor in five villages which need artificial recharge to augment groundwater.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Handling Dynamic Weights in Weighted Frequent Pattern Mining

NASA Astrophysics Data System (ADS)

Ahmed, Chowdhury Farhan; Tanbeer, Syed Khairuzzaman; Jeong, Byeong-Soo; Lee, Young-Koo

Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

Monitoring of environmental effects of coal strip mining from satellite imagery

NASA Technical Reports Server (NTRS)

Brooks, R. L.; Parra, C. G.

1976-01-01

This paper evaluates satellite imagery as a means of monitoring coal strip mines and their environmental effects. The satellite imagery employed is Skylab EREP S-190A and S-190B from SL-2, SL-3 and SL-4 missions; a large variety of camera/film/filter combinations has been reviewed. The investigation includes determining the applicability of satellite imagery for detection of disturbed acreage in areas of coal surface mining as well as the much more detailed monitoring of specific surface-mining operations, including: active mines, inactive mines, highwalls, ramp roads, pits, water impoundments and their associated acidity, graded areas and types of grading, and reclamed areas. Techniques have been developed to enable mining personnel to utilize this imagery in a practical and economic manner, requiring no previous photo-interpretation background and no purchases of expensive viewing or data-analysis equipment. To corroborate the photo-interpretation results, on-site observations were made in the very active mining area near Madisonville, Kentucky.

An Integrated Environment Monitoring System for Underground Coal Mines—Wireless Sensor Network Subsystem with Multi-Parameter Monitoring

PubMed Central

Zhang, Yu; Yang, Wei; Han, Dongsheng; Kim, Young-Il

2014-01-01

Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected. PMID:25051037

ChemBrowser: a flexible framework for mining chemical documents.

PubMed

Wu, Xian; Zhang, Li; Chen, Ying; Rhodes, James; Griffin, Thomas D; Boyer, Stephen K; Alba, Alfredo; Cai, Keke

2010-01-01

The ability to extract chemical and biological entities and relations from text documents automatically has great value to biochemical research and development activities. The growing maturity of text mining and artificial intelligence technologies shows promise in enabling such automatic chemical entity extraction capabilities (called "Chemical Annotation" in this paper). Many techniques have been reported in the literature, ranging from dictionary and rule-based techniques to machine learning approaches. In practice, we found that no single technique works well in all cases. A combinatorial approach that allows one to quickly compose different annotation techniques together for a given situation is most effective. In this paper, we describe the key challenges we face in real-world chemical annotation scenarios. We then present a solution called ChemBrowser which has a flexible framework for chemical annotation. ChemBrowser includes a suite of customizable processing units that might be utilized in a chemical annotator, a high-level language that describes the composition of various processing units that would form a chemical annotator, and an execution engine that translates the composition language to an actual annotator that can generate annotation results for a given set of documents. We demonstrate the impact of this approach by tailoring an annotator for extracting chemical names from patent documents and show how this annotator can be easily modified with simple configuration alone.

Quantifying Forest and Coastal Disturbance from Industrial Mining Using Satellite Time Series Analysis Under Very Cloudy Conditions

NASA Astrophysics Data System (ADS)

Alonzo, M.; Van Den Hoek, J.; Ahmed, N.

2015-12-01

The open-pit Grasberg mine, located in the highlands of Western Papua, Indonesia, and operated by PT Freeport Indonesia (PT-FI), is among the world's largest in terms of copper and gold production. Over the last 27 years, PT-FI has used the Ajkwa River to transport an estimated 1.3 billion tons of tailings from the mine into the so-called Ajkwa Deposition Area (ADA). The ADA is the product of aggradation and lateral expansion of the Ajkwa River into the surrounding lowland rainforest and mangroves, which include species important to the livelihoods of indigenous Papuans. Mine tailings that do not settle in the ADA disperse into the Arafura Sea where they increase levels of suspended particulate matter (SPM) and associated concentrations of dissolved copper. Despite the mine's large-scale operations, ecological impact of mine tailings deposition on the forest and estuarial ecosystems have received minimal formal study. While ground-based inquiries are nearly impossible due to access restrictions, assessment via satellite remote sensing is promising but hindered by extreme cloud cover. In this study, we characterize ridgeline-to-coast environmental impacts along the Ajkwa River, from the Grasberg mine to the Arafura Sea between 1987 and 2014. We use "all available" Landsat TM and ETM+ images collected over this time period to both track pixel-level vegetation disturbance and monitor changes in coastal SPM levels. Existing temporal segmentation algorithms are unable to assess both acute and protracted trajectories of vegetation change due to pervasive cloud cover. In response, we employ robust, piecewise linear regression on noisy vegetation index (NDVI) data in a manner that is relatively insensitive to atmospheric contamination. Using this disturbance detection technique we constructed land cover histories for every pixel, based on 199 image dates, to differentiate processes of vegetation decline, disturbance, and regrowth. Using annual reports from PT-FI, we show that the changing extent and spatial patterns of riparian vegetation disturbance directly correlate with yearly tailings production rates. While the rate of vegetation disturbance decreased after 1998, SPM levels along the Arafura coast increased, suggesting the failure of PT-FI to fully confine tailings to the ADA.

Land Ecological Security Evaluation of Underground Iron Mine Based on PSR Model

NASA Astrophysics Data System (ADS)

Xiao, Xiao; Chen, Yong; Ruan, Jinghua; Hong, Qiang; Gan, Yong

2018-01-01

Iron ore mine provides an important strategic resource to the national economy while it also causes many serious ecological problems to the environment. The study summed up the characteristics of ecological environment problems of underground iron mine. Considering the mining process of underground iron mine, we analysis connections between mining production, resource, environment and economical background. The paper proposed a land ecological security evaluation system and method of underground iron mine based on Pressure-State-Response model. Our application in Chengchao iron mine proves its efficiency and promising guide on land ecological security evaluation.

Establishment of trees and shrubs on lands disturbed by mining in the West

Treesearch

Ardell J. Bjugstad

1984-01-01

Increased research and development of cultural practices and species has assured success of establishment of trees and shrubs on lands disturbed by surface mining. Trickle irrigation and water harvesting techniques have increased survival of planted stock by 250 percent for some species.

Using Text Mining to Characterize Online Discussion Facilitation

ERIC Educational Resources Information Center

Ming, Norma; Baumer, Eric

2011-01-01

Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…

Revegetation for aesthetics

Treesearch

Bernard M. Slick

1980-01-01

Surface mining is changing the landscape character of forests in the East. Aesthetic visual aspects of the landscape are considered in the analysis, planning, and design of revegetation strategies. Application of landscape architectural design techniques in the revegetation of surface-mined lands, as well as knowledge of biological characteristics, will enhance the...

Applying Data Mining Principles to Library Data Collection.

ERIC Educational Resources Information Center

Guenther, Kim

2000-01-01

Explains how libraries can use data mining techniques for more effective data collection. Highlights include three phases: data selection and acquisition; data preparation and processing, including a discussion of the use of XML (extensible markup language); and data interpretation and integration, including database management systems. (LRW)

Big data mining analysis method based on cloud computing

NASA Astrophysics Data System (ADS)

Cai, Qing Qiu; Cui, Hong Gang; Tang, Hao

2017-08-01

Information explosion era, large data super-large, discrete and non-(semi) structured features have gone far beyond the traditional data management can carry the scope of the way. With the arrival of the cloud computing era, cloud computing provides a new technical way to analyze the massive data mining, which can effectively solve the problem that the traditional data mining method cannot adapt to massive data mining. This paper introduces the meaning and characteristics of cloud computing, analyzes the advantages of using cloud computing technology to realize data mining, designs the mining algorithm of association rules based on MapReduce parallel processing architecture, and carries out the experimental verification. The algorithm of parallel association rule mining based on cloud computing platform can greatly improve the execution speed of data mining.

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

NASA Astrophysics Data System (ADS)

Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

2016-05-01

Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

A Visual mining based framework for classification accuracy estimation

NASA Astrophysics Data System (ADS)

Arun, Pattathal Vijayakumar

2013-12-01

Classification techniques have been widely used in different remote sensing applications and correct classification of mixed pixels is a tedious task. Traditional approaches adopt various statistical parameters, however does not facilitate effective visualisation. Data mining tools are proving very helpful in the classification process. We propose a visual mining based frame work for accuracy assessment of classification techniques using open source tools such as WEKA and PREFUSE. These tools in integration can provide an efficient approach for getting information about improvements in the classification accuracy and helps in refining training data set. We have illustrated framework for investigating the effects of various resampling methods on classification accuracy and found that bilinear (BL) is best suited for preserving radiometric characteristics. We have also investigated the optimal number of folds required for effective analysis of LISS-IV images. Techniki klasyfikacji są szeroko wykorzystywane w różnych aplikacjach teledetekcyjnych, w których poprawna klasyfikacja pikseli stanowi poważne wyzwanie. Podejście tradycyjne wykorzystujące różnego rodzaju parametry statystyczne nie zapewnia efektywnej wizualizacji. Wielce obiecujące wydaje się zastosowanie do klasyfikacji narzędzi do eksploracji danych. W artykule zaproponowano podejście bazujące na wizualnej analizie eksploracyjnej, wykorzystujące takie narzędzia typu open source jak WEKA i PREFUSE. Wymienione narzędzia ułatwiają korektę pół treningowych i efektywnie wspomagają poprawę dokładności klasyfikacji. Działanie metody sprawdzono wykorzystując wpływ różnych metod resampling na zachowanie dokładności radiometrycznej i uzyskując najlepsze wyniki dla metody bilinearnej (BL).

Mining problems caused by tectonic stress in Illinois basin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, W.J.

1991-08-01

The Illinois basin coalfield is subject to a contemporary tectonic stress field in which the principal compressive stress axis ({sigma}1) is horizontal and strikes N60{degree}E to east-west. This stress is responsible for widespread development of kind zones and directional roof failures in mine headings driven perpendicular to {sigma}1. Also, small thrust faults perpendicular to {sigma}1 and joints parallel to {sigma}1 weaken the mine roof and occasionally admit water and gas to workings, depending upon geologic setting. The direction of magnitude of stress have been identified by a variety of techniques that can be applied both prior to mining and duringmore » development. Mining experience shows that the best method of minimizing stress-related problems is to drive mine headings at about 45 to {sigma}1.« less

Black Thunder Coal Mine and Los Alamos National Laboratory experimental study of seismic energy generated by large scale mine blasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, R.L.; Gross, D.; Pearson, D.C.

In an attempt to better understand the impact that large mining shots will have on verifying compliance with the international, worldwide, Comprehensive Test Ban Treaty (CTBT, no nuclear explosion tests), a series of seismic and videographic experiments has been conducted during the past two years at the Black Thunder Coal Mine. Personnel from the mine and Los Alamos National Laboratory have cooperated closely to design and perform experiments to produce results with mutual benefit to both organizations. This paper summarizes the activities, highlighting the unique results of each. Topics which were covered in these experiments include: (1) synthesis of seismic,more » videographic, acoustic, and computer modeling data to improve understanding of shot performance and phenomenology; (2) development of computer generated visualizations of observed blasting techniques; (3) documentation of azimuthal variations in radiation of seismic energy from overburden casting shots; (4) identification of, as yet unexplained, out of sequence, simultaneous detonation in some shots using seismic and videographic techniques; (5) comparison of local (0.1 to 15 kilometer range) and regional (100 to 2,000 kilometer range) seismic measurements leading to determine of the relationship between local and regional seismic amplitude to explosive yield for overburden cast, coal bulking and single fired explosions; and (6) determination of the types of mining shots triggering the prototype International Monitoring System for the CTBT.« less

Joint Force Quarterly. Issue 74, 3rd Quarter, July 2014

DTIC Science & Technology

2014-07-01

staging base USS Ponce during International Mine Countermeasures Exercise 13 (DOD/T. Scot Cregan) 16 Forum / Contexts of Future Conflict and War JFQ 74...perspective. Evolution According to David L. Woods in A History of Tactical Communication Techniques, “Since wars began, com- manders have sought...coalition personnel to meet the dynamic joint interoperability challenges within the MTN after nearly four decades. JFQ Notes 1 David L. Woods , A

Application of underground microseismic monitoring for ground failure and secure longwall coal mining operation: A case study in an Indian mine

NASA Astrophysics Data System (ADS)

Ghosh, G. K.; Sivakumar, C.

2018-03-01

Longwall mining technique has been widely used around the globe due to its safe mining process. However, mining operations are suspended when various problems arise like collapse of roof falls, cracks and fractures propagation in the roof and complexity in roof strata behaviors. To overcome these colossal problems, an underground real time microseismic monitoring technique has been implemented in the working panel-P2 in the Rajendra longwall underground coal mine at South Eastern Coalfields Limited (SECL), India. The target coal seams appears at the panel P-2 within a depth of 70 m to 76 m. In this process, 10 to 15 uniaxial geophones were placed inside a borehole at depth range of 40 m to 60 m located over the working panel-P2 with high rock quality designation value for better seismic signal. Various microseismic events were recorded with magnitude ranging from -5 to 2 in the Richter scale. The time-series processing was carried out to get various seismic parameters like activity rate, potential energy, viscosity rate, seismic moment, energy index, apparent volume and potential energy with respect to time. The used of these parameters helped tracing the events, understanding crack and fractures propagation and locating both high and low stress distribution zones prior to roof fall occurrence. In most of the cases, the events were divided into three stage processes: initial or preliminary, middle or building, and final or falling. The results of this study reveal that underground microseismic monitoring provides sufficient prior information of underground weighting events. The information gathered during the study was conveyed to the mining personnel in advance prior to roof fall event. This permits to take appropriate action for safer mining operations and risk reduction during longwall operation.

toxoMine: an integrated omics data warehouse for Toxoplasma gondii systems biology research

PubMed Central

Rhee, David B.; Croken, Matthew McKnight; Shieh, Kevin R.; Sullivan, Julie; Micklem, Gos; Kim, Kami; Golden, Aaron

2015-01-01

Toxoplasma gondii (T. gondii) is an obligate intracellular parasite that must monitor for changes in the host environment and respond accordingly; however, it is still not fully known which genetic or epigenetic factors are involved in regulating virulence traits of T. gondii. There are on-going efforts to elucidate the mechanisms regulating the stage transition process via the application of high-throughput epigenomics, genomics and proteomics techniques. Given the range of experimental conditions and the typical yield from such high-throughput techniques, a new challenge arises: how to effectively collect, organize and disseminate the generated data for subsequent data analysis. Here, we describe toxoMine, which provides a powerful interface to support sophisticated integrative exploration of high-throughput experimental data and metadata, providing researchers with a more tractable means toward understanding how genetic and/or epigenetic factors play a coordinated role in determining pathogenicity of T. gondii. As a data warehouse, toxoMine allows integration of high-throughput data sets with public T. gondii data. toxoMine is also able to execute complex queries involving multiple data sets with straightforward user interaction. Furthermore, toxoMine allows users to define their own parameters during the search process that gives users near-limitless search and query capabilities. The interoperability feature also allows users to query and examine data available in other InterMine systems, which would effectively augment the search scope beyond what is available to toxoMine. toxoMine complements the major community database ToxoDB by providing a data warehouse that enables more extensive integrative studies for T. gondii. Given all these factors, we believe it will become an indispensable resource to the greater infectious disease research community. Database URL: http://toxomine.org PMID:26130662

North American Bats and Mines Project: A cooperative approach for integrating bat conservation and mine-land reclamation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ducummon, S.L.

Inactive underground mines now provide essential habitat for more than half of North America`s 44 bat species, including some of the largest remaining populations. Thousands of abandoned mines have already been closed or are slated for safety closures, and many are destroyed during renewed mining in historic districts. The available evidence suggests that millions of bats have already been lost due to these closures. Bats are primary predators of night-flying insects that cost American farmers and foresters billions of dollars annually, therefore, threats to bat survival are cause for serious concern. Fortunately, mine closure methods exist that protect both batsmore » and humans. Bat Conservation International (BCI) and the USDI-Bureau of Land Management founded the North American Bats and Mines Project to provide national leadership and coordination to minimize the loss of mine-roosting bats. This partnership has involved federal and state mine-land and wildlife managers and the mining industry. BCI has trained hundreds of mine-land and wildlife managers nationwide in mine assessment techniques for bats and bat-compatible closure methods, published technical information on bats and mine-land management, presented papers on bats and mines at national mining and wildlife conferences, and collaborated with numerous federal, state, and private partners to protect some of the most important mine-roosting bat populations. Our new mining industry initiative, Mining for Habitat, is designed to develop bat habitat conservation and enhancement plans for active mining operations. It includes the creation of cost-effective artificial underground bat roosts using surplus mining materials such as old mine-truck tires and culverts buried beneath waste rock.« less

Chemomics-based marker compounds mining and mimetic processing for exploring chemical mechanisms in traditional processing of herbal medicines, a continuous study on Rehmanniae Radix.

PubMed

Zhou, Li; Xu, Jin-Di; Zhou, Shan-Shan; Shen, Hong; Mao, Qian; Kong, Ming; Zou, Ye-Ting; Xu, Ya-Yun; Xu, Jun; Li, Song-Lin

2017-12-29

Exploring processing chemistry, in particular the chemical transformation mechanisms involved, is a key step to elucidate the scientific basis in traditional processing of herbal medicines. Previously, taking Rehmanniae Radix (RR) as a case study, the holistic chemome (secondary metabolome and glycome) difference between raw and processed RR was revealed by integrating hyphenated chromatographic techniques-based targeted glycomics and untargeted metabolomics. Nevertheless, the complex chemical transformation mechanisms underpinning the holistic chemome variation in RR processing remain to be extensively clarified. As a continuous study, here a novel strategy by combining chemomics-based marker compounds mining and mimetic processing is proposed for further exploring the chemical mechanisms involved in herbal processing. First, the differential marker compounds between raw and processed herbs were rapidly discovered by untargeted chemomics-based mining approach through multivariate statistical analysis of the chemome data obtained by integrated metabolomics and glycomics analysis. Second, the marker compounds were mimetically processed under the simulated physicochemical conditions as in the herb processing, and the final reaction products were chemically characterized by targeted chemomics-based mining approach. Third, the main chemical transformation mechanisms involved were clarified by linking up the original marker compounds and their mimetic processing products. Using this strategy, a set of differential marker compounds including saccharides, glycosides and furfurals in raw and processed RR was rapidly found, and the major chemical mechanisms involved in RR processing were elucidated as stepwise transformations of saccharides (polysaccharides, oligosaccharides and monosaccharides) and glycosides (iridoid glycosides and phenethylalcohol glycosides) into furfurals (glycosylated/non-glycosylated hydroxymethylfurfurals) by deglycosylation and/or dehydration. The research deliverables indicated that the proposed strategy could advance the understanding of RR processing chemistry, and therefore may be considered a promising approach for delving into the scientific basis in traditional processing of herbal medicines. Copyright © 2017 Elsevier B.V. All rights reserved.

The application of high-resolution mass spectrometry-based data-mining tools in tandem to metabolite profiling of a triple drug combination in humans.

PubMed

Xing, Jie; Zang, Meitong; Zhang, Haiying; Zhu, Mingshe

2015-10-15

Patients are usually exposed to multiple drugs, and metabolite profiling of each drug in complex biological matrices is a big challenge. This study presented a new application of an improved high resolution mass spectrometry (HRMS)-based data-mining tools in tandem to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was metronidazole-pantoprazole-clarithromycin (MET-PAN-CLAR), which is widely used in clinic to treat ulcers caused by Helicobacter pylori. First, mass defect filter (MDF), as a targeted data processing tool, was able to recover all relevant metabolites of MET-PAN-CLAR in human plasma and urine from the full-scan MS dataset when appropriate MDF templates for each drug were defined. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, worked effectively except for several trace metabolites, which were buried in the remaining background signals. Third, an integrated strategy, i.e., untargeted BS followed by improved MDF, was effective for metabolite identification of MET-PAN-CLAR. Most metabolites except for trace ones were found in the first step of BS-processed datasets, and the results led to the setup of appropriate metabolite MDF template for the subsequent MDF data processing. Trace metabolites were further recovered by MDF, which used both common MDF templates and the novel metabolite-based MDF templates. As a result, a total of 44 metabolites or related components were found for MET-PAN-CLAR in human plasma and urine using the integrated strategy. New metabolic pathways such as N-glucuronidation of PAN and dehydrogenation of CLAR were found. This study demonstrated that the combination of accurate mass-based multiple data-mining techniques in tandem, i.e., untargeted background subtraction followed by targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drugs in vivo. Copyright © 2015 Elsevier B.V. All rights reserved.

Text Mining of UU-ITE Implementation in Indonesia

NASA Astrophysics Data System (ADS)

Hakim, Lukmanul; Kusumasari, Tien F.; Lubis, Muharman

2018-04-01

At present, social media and networks act as one of the main platforms for sharing information, idea, thought and opinions. Many people share their knowledge and express their views on the specific topics or current hot issues that interest them. The social media texts have rich information about the complaints, comments, recommendation and suggestion as the automatic reaction or respond to government initiative or policy in order to overcome certain issues.This study examines the sentiment from netizensas part of citizen who has vocal sound about the implementation of UU ITE as the first cyberlaw in Indonesia as a means to identify the current tendency of citizen perception. To perform text mining techniques, this study used Twitter Rest API while R programming was utilized for the purpose of classification analysis based on hierarchical cluster.

Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies

PubMed Central

López, Julio

2018-01-01

We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments. The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections. PMID:29670667

Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies.

PubMed

Bosch, Paul; Herrera, Mauricio; López, Julio; Maldonado, Sebastián

2018-01-01

We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments. The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections.

An AK-LDMeans algorithm based on image clustering

NASA Astrophysics Data System (ADS)

Chen, Huimin; Li, Xingwei; Zhang, Yongbin; Chen, Nan

2018-03-01

Clustering is an effective analytical technique for handling unmarked data for value mining. Its ultimate goal is to mark unclassified data quickly and correctly. We use the roadmap for the current image processing as the experimental background. In this paper, we propose an AK-LDMeans algorithm to automatically lock the K value by designing the Kcost fold line, and then use the long-distance high-density method to select the clustering centers to further replace the traditional initial clustering center selection method, which further improves the efficiency and accuracy of the traditional K-Means Algorithm. And the experimental results are compared with the current clustering algorithm and the results are obtained. The algorithm can provide effective reference value in the fields of image processing, machine vision and data mining.

Using natural language processing techniques to inform research on nanotechnology.

PubMed

Lewinski, Nastassja A; McInnes, Bridget T

2015-01-01

Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics.

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

PubMed Central

Castañón–Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-01-01

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi–Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information. PMID:26633417

Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles

PubMed Central

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh; Elisevich, Kost; Fotouhi, Farshad

2015-01-01

In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure. This ensemble approach has been applied to lateralization of focal epileptogenicity in temporal lobe epilepsy (TLE) and prediction of surgical outcomes. A goal of this study is to reduce extraoperative electrocorticography (eECoG) requirement which is the practice of using electrodes placed directly on the exposed surface of the brain. We have shown that such goal is achievable with application of data mining techniques. Furthermore, all TLE surgical operations do not result in complete relief from seizures and it is not always possible for human experts to identify such unsuccessful cases prior to surgery. This study demonstrates the capability of data mining techniques in prediction of undesirable outcome for a portion of such cases. PMID:26609547

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals.

PubMed

Castañón-Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-12-02

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi-Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information.

Mining Longitudinal Web Queries: Trends and Patterns.

ERIC Educational Resources Information Center

Wang, Peiling; Berry, Michael W.; Yang, Yiheng

2003-01-01

Analyzed user queries submitted to an academic Web site during a four-year period, using a relational database, to examine users' query behavior, to identify problems they encounter, and to develop techniques for optimizing query analysis and mining. Linguistic analyses focus on query structures, lexicon, and word associations using statistical…

Traffic Flow Management: Data Mining Update

NASA Technical Reports Server (NTRS)

Grabbe, Shon R.

2012-01-01

This presentation provides an update on recent data mining efforts that have been designed to (1) identify like/similar days in the national airspace system, (2) cluster/aggregate national-level rerouting data and (3) apply machine learning techniques to predict when Ground Delay Programs are required at a weather-impacted airport

Restoring tropical forests on bauxite mined lands: lessons from the Brazilian Amazon

Treesearch

John A. Parrotta; Oliver H. Knowles

2001-01-01

Restoring self-sustaining tropical forest ecosystems on surface mined sites is a formidable challenge that requires the integration of proven reclamation techniques and reforestation strategies appropriate to specific site conditions, including landscape biodiversity patterns. Restorationists working in most tropical settings are usually hampered by lack of basic...

76 FR 9376 - Proposed Extension of Existing Information Collection;

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-17

... helps to assure that requested data can be provided in the desired format, reporting burden (time and...) that are removed during mining operations or separated from mined coal and deposited on the surface... collection techniques or other forms of information technology, e.g., permitting electronic submissions of...

76 FR 65180 - Proposed Information Collection; Comment Request; Deep Seabed Mining Exploration Licenses

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-20

... Collection; Comment Request; Deep Seabed Mining Exploration Licenses AGENCY: National Oceanic and Atmospheric... documentation electronically when feasible. III. Data OMB Control Number: 0648-0145. Form Number: None. Type of... information on respondents, including through the use of automated collection techniques or other forms of...

An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest

PubMed Central

Kim, Sung-Min; Yi, Huiuk; Choi, Yosoon

2017-01-01

In this study, current geographic information system (GIS)-based methods and their application for the modeling and assessment of mining-induced hazards were reviewed. Various types of mining-induced hazard, including soil contamination, soil erosion, water pollution, and deforestation were considered in the discussion of the strength and role of GIS as a viable problem-solving tool in relation to mining-induced hazards. The various types of mining-induced hazard were classified into two or three subtopics according to the steps involved in the reclamation procedure, or elements of the hazard of interest. Because GIS is appropriated for the handling of geospatial data in relation to mining-induced hazards, the application and feasibility of exploiting GIS-based modeling and assessment of mining-induced hazards within the mining industry could be expanded further. PMID:29186922

An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest.

PubMed

Suh, Jangwon; Kim, Sung-Min; Yi, Huiuk; Choi, Yosoon

2017-11-27

In this study, current geographic information system (GIS)-based methods and their application for the modeling and assessment of mining-induced hazards were reviewed. Various types of mining-induced hazard, including soil contamination, soil erosion, water pollution, and deforestation were considered in the discussion of the strength and role of GIS as a viable problem-solving tool in relation to mining-induced hazards. The various types of mining-induced hazard were classified into two or three subtopics according to the steps involved in the reclamation procedure, or elements of the hazard of interest. Because GIS is appropriated for the handling of geospatial data in relation to mining-induced hazards, the application and feasibility of exploiting GIS-based modeling and assessment of mining-induced hazards within the mining industry could be expanded further.

78 FR 72025 - Security Zones; Naval Base Point Loma; Naval Mine Anti Submarine Warfare Command; San Diego Bay...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-02

...-AA87 Security Zones; Naval Base Point Loma; Naval Mine Anti Submarine Warfare Command; San Diego Bay... establishing a new security zone at the Naval Mine and Anti-Submarine Warfare Command to protect the relocated... Commander of Naval Base Point Loma, the Commander of the Naval Mine Anti Submarine Warfare Command, and the...

Mars Colony in situ resource utilization: An integrated architecture and economics model

NASA Astrophysics Data System (ADS)

Shishko, Robert; Fradet, René; Do, Sydney; Saydam, Serkan; Tapia-Cortez, Carlos; Dempster, Andrew G.; Coulton, Jeff

2017-09-01

This paper reports on our effort to develop an ensemble of specialized models to explore the commercial potential of mining water/ice on Mars in support of a Mars Colony. This ensemble starts with a formal systems architecting framework to describe a Mars Colony and capture its artifacts' parameters and technical attributes. The resulting database is then linked to a variety of ;downstream; analytic models. In particular, we integrated an extraction process (i.e., ;mining;) model, a simulation of the colony's environmental control and life support infrastructure known as HabNet, and a risk-based economics model. The mining model focuses on the technologies associated with in situ resource extraction, processing, storage and handling, and delivery. This model computes the production rate as a function of the systems' technical parameters and the local Mars environment. HabNet simulates the fundamental sustainability relationships associated with establishing and maintaining the colony's population. The economics model brings together market information, investment and operating costs, along with measures of market uncertainty and Monte Carlo techniques, with the objective of determining the profitability of commercial water/ice in situ mining operations. All told, over 50 market and technical parameters can be varied in order to address ;what-if; questions, including colony location.

Application of electromagnetic techniques in survey of contaminated groundwater at an abandoned mine complex in southwestern Indiana, U.S.A.

USGS Publications Warehouse

Brooks, G.A.; Olyphant, G.A.; Harper, D.

1991-01-01

In part of a large abandoned mining complex, electromagnetic geophysical surveys were used along with data derived from cores and monitoring wells to infer sources of contamination and subsurface hydrologic connections between acidic refuse deposits and adjacent undisturbed geologic materials. Electrical resistivity increases sharply along the boundary of an elevated deposit of pyritic coarse refuse, which is highly contaminated and electrically conductive, indicating poor subsurface hydrologic connections with surrounding deposits of fine refuse and undisturbed glacial material. Groundwater chemistry, as reflected in values of specific conductance, also differs markedly across the deposit's boundary, indicating that a widespread contaminant plume has not developed around the coarse refuse in more than 40 yr since the deposit was created. Most acidic drainage from the coarse refuse is by surface runoff and is concentrated around stream channels. Although most of the contaminated groundwater within the study area is concentrated within the surficial refuse deposits, transects of apparent resistivity and phase angle indicate the existence of an anomalous conductive layer at depth (>4 m) in thick alluvial sediments along the northern boundary of the mining complex. Based on knowledge of local geology, the anomaly is interpreted to represent a subsurface connection between the alluvium and a flooded abandoned underground mine. ?? 1991 Springer-Verlag New York Inc.

Application of electromagnetic techniques in survey of contaminated groundwater at an abandoned mine complex in southwestern Indiana, U.S.A.

NASA Astrophysics Data System (ADS)

Brooks, Glenn A.; Olyphant, Greg A.; Harper, Denver

1991-07-01

In part of a large abandoned mining complex, electromagnetic geophysical surveys were used along with data derived from cores and monitoring wells to infer sources of contamination and subsurface hydrologic connections between acidic refuse deposits and adjacent undisturbed geologic materials. Electrical resistivity increases sharply along the boundary of an elevated deposit of pyritic coarse refuse, which is highly contaminated and electrically conductive, indicating poor subsurface hydrologic connections with surrounding deposits of fine refuse and undisturbed glacial material. Groundwater chemistry, as reflected in values of specific conductance, also differs markedly across the deposit's boundary, indicating that a widespread contaminant plume has not developed around the coarse refuse in more than 40 yr since the deposit was created. Most acidic drainage from the coarse refuse is by surface runoff and is concentrated around stream channels. Although most of the contaminated groundwater within the study area is concentrated within the surficial refuse deposits, transects of apparent resistivity and phase angle indicate the existence of an anomalous conductive layer at depth (>4 m) in thick alluvial sediments along the northern boundary of the mining complex. Based on knowledge of local geology, the anomaly is interpreted to represent a subsurface connection between the alluvium and a flooded abandoned underground mine.

Sustainable Mineral-Intensive Growth in Odisha, India

NASA Astrophysics Data System (ADS)

Nayak, S.

2012-04-01

The focus of the work is to highlight the present environmental and social impacts of extensive mining on the health of the common people of Odisha. The mining activities have created havoc impact to the environment and social life of the state. Odisha has huge deposits of ores and minerals of chromite, nickel, bauxite, iron, coal, copper, manganese, graphite, vanadium etc. The mining activities have encouraged rapid urbanization and at the same time have altered the topography of these areas and extensively degraded the forest land. For long term sustainable development of the society, it is necessary to take a balanced and integrated approach towards environmental protection and economic advancement. Industries should aim at achieving their goals, through a system of permits based on best available techniques, which gives emphasis on integrated prevention and control of consumption of energy and water as well as pollution of water, air and soil. The rapid industrial growth has brought promising opportunities for economic development and poverty reduction in Odisha but at the same time has caused extensive environmental degradation. The best management practices to deal with environmental and social impacts on mineral-intensive growth are suggested in this work. In addition to lean technology, economic implications of the introduction of environmental technologies for mining activities are also discussed.

A distributed approach for optimizing cascaded classifier topologies in real-time stream mining systems.

PubMed

Foo, Brian; van der Schaar, Mihaela

2010-11-01

In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.

Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods

NASA Astrophysics Data System (ADS)

Eivazy, Hesameddin; Esmaieli, Kamran; Jean, Raynald

2017-12-01

An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.

A Neural-Network Clustering-Based Algorithm for Privacy Preserving Data Mining

NASA Astrophysics Data System (ADS)

Tsiafoulis, S.; Zorkadis, V. C.; Karras, D. A.

The increasing use of fast and efficient data mining algorithms in huge collections of personal data, facilitated through the exponential growth of technology, in particular in the field of electronic data storage media and processing power, has raised serious ethical, philosophical and legal issues related to privacy protection. To cope with these concerns, several privacy preserving methodologies have been proposed, classified in two categories, methodologies that aim at protecting the sensitive data and those that aim at protecting the mining results. In our work, we focus on sensitive data protection and compare existing techniques according to their anonymity degree achieved, the information loss suffered and their performance characteristics. The ℓ-diversity principle is combined with k-anonymity concepts, so that background information can not be exploited to successfully attack the privacy of data subjects data refer to. Based on Kohonen Self Organizing Feature Maps (SOMs), we firstly organize data sets in subspaces according to their information theoretical distance to each other, then create the most relevant classes paying special attention to rare sensitive attribute values, and finally generalize attribute values to the minimum extend required so that both the data disclosure probability and the information loss are possibly kept negligible. Furthermore, we propose information theoretical measures for assessing the anonymity degree achieved and empirical tests to demonstrate it.

Publications - SR 68 | Alaska Division of Geological & Geophysical Surveys

Science.gov Websites

Mining District; Base Metals; Bethel Mining District; Bismuth; Black Mining District; Bluff (Place ; Livengood Mining District; Lode; Marshall Mining District; Massive Sulfide Deposit; Massive Sulfide Occurrence; Massive Sulfide Prospect; Massive Sulfides; McGrath Mining District; Melozitna Mining District

An extended data mining method for identifying differentially expressed assay-specific signatures in functional genomic studies.

PubMed

Rollins, Derrick K; Teh, Ailing

2010-12-17

Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA) has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and compared to a current approach on the basis of false discovery rate (FDR) and statistical power (SP) which is the ability to correctly identify important genes. This work developed and evaluated two new test statistics based on PCA and compared them to a popular method that is not PCA based. Both test statistics were found to be effective as evaluated in three case studies: (i) exposing E. coli cells to two different ethanol levels; (ii) application of myostatin to two groups of mice; and (iii) a simulated data study derived from the properties of (ii). The proposed method (PM) effectively identified critical genes in these studies based on comparison with the current method (CM). The simulation study supports higher identification accuracy for PM over CM for both proposed test statistics when the gene variance is constant and for one of the test statistics when the gene variance is non-constant. PM compares quite favorably to CM in terms of lower FDR and much higher SP. Thus, PM can be quite effective in producing accurate signatures from large microarray data sets for differential expression between assays groups identified in a preliminary step of the PCA procedure and is, therefore, recommended for use in these applications.

Mining residential water and electricity demand data in Southern California to inform demand management strategies

NASA Astrophysics Data System (ADS)

Cominola, A.; Spang, E. S.; Giuliani, M.; Castelletti, A.; Loge, F. J.; Lund, J. R.

2016-12-01

Demand side management strategies are key to meet future water and energy demands in urban contexts, promote water and energy efficiency in the residential sector, provide customized services and communications to consumers, and reduce utilities' costs. Smart metering technologies allow gathering high temporal and spatial resolution water and energy consumption data and support the development of data-driven models of consumers' behavior. Modelling and predicting resource consumption behavior is essential to inform demand management. Yet, analyzing big, smart metered, databases requires proper data mining and modelling techniques, in order to extract useful information supporting decision makers to spot end uses towards which water and energy efficiency or conservation efforts should be prioritized. In this study, we consider the following research questions: (i) how is it possible to extract representative consumers' personalities out of big smart metered water and energy data? (ii) are residential water and energy consumption profiles interconnected? (iii) Can we design customized water and energy demand management strategies based on the knowledge of water- energy demand profiles and other user-specific psychographic information? To address the above research questions, we contribute a data-driven approach to identify and model routines in water and energy consumers' behavior. We propose a novel customer segmentation procedure based on data-mining techniques. Our procedure consists of three steps: (i) extraction of typical water-energy consumption profiles for each household, (ii) profiles clustering based on their similarity, and (iii) evaluation of the influence of candidate explanatory variables on the identified clusters. The approach is tested onto a dataset of smart metered water and energy consumption data from over 1000 households in South California. Our methodology allows identifying heterogeneous groups of consumers from the studied sample, as well as characterizing them with respect to consumption profiles features and socio- demographic information. Results show how such better understanding of the considered users' community allows spotting potentially interesting areas for water and energy demand management interventions.

Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data

PubMed Central

Batal, Iyad; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes. PMID:25937993