Machine listening intelligence

NASA Astrophysics Data System (ADS)

Cella, C. E.

2017-05-01

This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.

A computer architecture for intelligent machines

NASA Technical Reports Server (NTRS)

Lefebvre, D. R.; Saridis, G. N.

1992-01-01

The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

A computer architecture for intelligent machines

NASA Technical Reports Server (NTRS)

Lefebvre, D. R.; Saridis, G. N.

1991-01-01

The Theory of Intelligent Machines proposes a hierarchical organization for the functions of an autonomous robot based on the Principle of Increasing Precision With Decreasing Intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed in recent years. A computer architecture that implements the lower two levels of the intelligent machine is presented. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Details of Execution Level controllers for motion and vision systems are addressed, as well as the Petri net transducer software used to implement Coordination Level functions. Extensions to UNIX and VxWorks operating systems which enable the development of a heterogeneous, distributed application are described. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Complementary Machine Intelligence and Human Intelligence in Virtual Teaching Assistant for Tutoring Program Tracing

ERIC Educational Resources Information Center

Chou, Chih-Yueh; Huang, Bau-Hung; Lin, Chi-Jen

2011-01-01

This study proposes a virtual teaching assistant (VTA) to share teacher tutoring tasks in helping students practice program tracing and proposes two mechanisms of complementing machine intelligence and human intelligence to develop the VTA. The first mechanism applies machine intelligence to extend human intelligence (teacher answers) to evaluate…

Human evolution in the age of the intelligent machine

NASA Technical Reports Server (NTRS)

Mclaughlin, W. I.

1983-01-01

A systems analysis of the future evolution of man can be conducted by analyzing the biological material of the galaxy into three subsystems: man, intelligent machines, and intelligent extraterrestrial organisms. A binomial interpretation is applied to this system wherein each of the subsystems is assigned a designation of success or failure. For man the two alternatives are, respectively, 'decline' or 'flourish', for machine they are 'become intelligent' or 'stay dumb', while for extraterrestrial intelligence the dichotomy is that of 'existence' or 'nonexistence'. The choices for each of three subsystems yield a total of eight possible states for the system. The relative lack of integration between brain components makes man a weak evolutionary contestant compared to machines. It is judged that machines should become dominant on earth within 100 years, probably by means of continuing development of existing man-machine systems. Advanced forms of extraterrestrial intelligence may exist but are too difficult to observe. The prospects for communication with extraterrestrial intelligence are reviewed.

Human evolution in the age of the intelligent machine

NASA Astrophysics Data System (ADS)

McLaughlin, W. I.

A systems analysis of the future evolution of man can be conducted by analyzing the biological material of the galaxy into three subsystems: man, intelligent machines, and intelligent extraterrestrial organisms. A binomial interpretation is applied to this system wherein each of the subsystems is assigned a designation of success or failure. For man the two alternatives are, respectively, 'decline' or 'flourish', for machine they are 'become intelligent' or 'stay dumb', while for extraterrestrial intelligence the dichotomy is that of 'existence' or 'nonexistence'. The choices for each of three subsystems yield a total of eight possible states for the system. The relative lack of integration between brain components makes man a weak evolutionary contestant compared to machines. It is judged that machines should become dominant on earth within 100 years, probably by means of continuing development of existing man-machine systems. Advanced forms of extraterrestrial intelligence may exist but are too difficult to observe. The prospects for communication with extraterrestrial intelligence are reviewed.

Analysis in Motion Initiative – Human Machine Intelligence

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

Blaha, Leslie

As computers and machines become more pervasive in our everyday lives, we are looking for ways for humans and machines to work more intelligently together. How can we help machines understand their users so the team can do smarter things together? The Analysis in Motion Initiative is advancing the science of human machine intelligence — creating human-machine teams that work better together to make correct, useful, and timely interpretations of data.

Intelligent Systems and Its Applications in Robotics

NASA Astrophysics Data System (ADS)

Kaynak, Okyay

The last decade of the last millennium is characterized by what might be called the intelligent systems revolution, as a result of which, it is now possible to have man made systems that exhibit ability to reason, learn from experience and make rational decisions without human intervention. Prof. Zadeh has coined the word MIQ (machine intelligence quotient) to describe a measure of intelligence of man-made systems. In this perspective, an intelligent system can be defined as a system that has a high MIQ.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

CESAR research in intelligent machines

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

Weisbin, C.R.

1986-01-01

The Center for Engineering Systems Advanced Research (CESAR) was established in 1983 as a national center for multidisciplinary, long-range research and development in machine intelligence and advanced control theory for energy-related applications. Intelligent machines of interest here are artificially created operational systems that are capable of autonomous decision making and action. The initial emphasis for research is remote operations, with specific application to dexterous manipulation in unstructured dangerous environments where explosives, toxic chemicals, or radioactivity may be present, or in other environments with significant risk such as coal mining or oceanographic missions. Potential benefits include reduced risk to man inmore » hazardous situations, machine replication of scarce expertise, minimization of human error due to fear or fatigue, and enhanced capability using high resolution sensors and powerful computers. A CESAR goal is to explore the interface between the advanced teleoperation capability of today, and the autonomous machines of the future.« less

Toward a Brief Multidimensional Assessment of Emotional Intelligence: Psychometric Properties of the Emotional Quotient Inventory-Short Form

ERIC Educational Resources Information Center

Parker, James D. A.; Keefer, Kateryna V.; Wood, Laura M.

2011-01-01

Although several brief instruments are available for the emotional intelligence (EI) construct, their conceptual coverage tends to be quite limited. One notable exception is the short form of the Emotional Quotient Inventory (EQ-i:S), which measures multiple EI dimensions in addition to a global EI index. Despite the unique advantage offered by…

Tool path strategy and cutting process monitoring in intelligent machining

NASA Astrophysics Data System (ADS)

Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

2018-06-01

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

Machine intelligence and robotics: Report of the NASA study group

NASA Technical Reports Server (NTRS)

1980-01-01

Opportunities for the application of machine intelligence and robotics in NASA missions and systems were identified. The benefits of successful adoption of machine intelligence and robotics techniques were estimated and forecasts were prepared to show their growth potential. Program options for research, advanced development, and implementation of machine intelligence and robot technology for use in program planning are presented.

Inverse association between 18-carbon trans fatty acids and intelligence quotients in smoking schizophrenia patients.

PubMed

Lohner, Szimonetta; Vágási, Judit; Marosvölgyi, Tamás; Tényi, Tamás; Decsi, Tamás

2014-01-30

This study aimed to investigate polyunsaturated (PUFA) and trans isomeric fatty acid status in schizophrenia patients. Fatty acid composition of plasma phospholipids (PL) and triacylglycerols (TG) was analyzed by gas chromatography in 29 schizophrenia patients and 15 healthy controls. We found no difference in PL n-3 fatty acid status between the two groups, while the values of 22:5n-6 were significantly higher in patients with schizophrenia than in controls. In TG, values of docosatrienoic acid (20:3n-3) and docosapentaenoic acid (20:5n-3) were significantly higher in schizophrenia patients than in controls. We found no difference in the trans fatty acid status between patients and controls. In smoking schizophrenia patients significant negative correlations were detected between Wechsler adult full-scale intelligence quotients and values of total trans fatty acids in PL lipids, whereas no such correlation was seen either in non-smoking schizophrenia patients, or in healthy controls. While data obtained in the present study fail to furnish evidence for n-3 PUFA supplementation to the diet of patients with schizophrenia, they indicate that in smoking schizophrenia patients high dietary exposure to trans fatty acids is associated with lower intelligence quotients. © 2013 Published by Elsevier Ireland Ltd.

[Children's intelligence quotient following general anesthesia for dental care: a clinical observation by Chinese Wechsler young children scale of intelligence].

PubMed

Xia, B; Wang, J H; Xiao, Y M; Liu, K Y; Yang, X D; Ge, L H

2016-04-18

It has been demonstrated that anesthetics exposure may lead to neurocognitive impairment in developing brain of animal models. However, for the limitation that the animal models cannot fully mimic the dose and duration in clinical settings especially for dental general anesthesia, the clinical significance of anesthetics exposure on developing central nervous system remains undetermined. Therefore, we conducted the current study in order to observe the fluctuation of intelligence quotient (IQ) after the administration of dental general anesthesia comparing to that before surgery. We conducted the current study in order to observe the fluctuation of intelligence quotient (IQ) after the administration of dental general anesthesia compared with that before surgery. Thirty two patients, ASA I, who were exposed to dental general anesthesia in Department of Pediatric Dentistry Peking University School and Hospital of Stomatology, aged 4 to 6.5 years, were enrolled in this prospective study. Patients with severe learning difficulties or communication disorders were excluded. Written and informed consent was obtained from each patients' family which was fully explained of the purpose and method of study. Their intelligence quotients were evaluated with the Chinese Wechsler young children scale of intelligence (Urban version) before and 2 weeks after dental anesthesia. They were treated by experienced pediatric dentists and the sevoflurane, propofol and nitrous oxide were used for general anesthesia by anesthetist. Articaine hydrochloride and epinephrine tartrate injections were used for their pulp treatment or extraction. The examiners and scorers for IQ had technical training in the test administration. All the patients were tested by the same examiner and with standardized guide language. Each subtest was scored according to the tool review. Verbal IQ and performance IQ consisted of relevant 5 subtests and full scale IQ. Statistical analyses were performed by SPSS 18

Integrated human-machine intelligence in space systems

NASA Technical Reports Server (NTRS)

Boy, Guy A.

1992-01-01

The integration of human and machine intelligence in space systems is outlined with respect to the contributions of artificial intelligence. The current state-of-the-art in intelligent assistant systems (IASs) is reviewed, and the requirements of some real-world applications of the technologies are discussed. A concept of integrated human-machine intelligence is examined in the contexts of: (1) interactive systems that tolerate human errors; (2) systems for the relief of workloads; and (3) interactive systems for solving problems in abnormal situations. Key issues in the development of IASs include the compatibility of the systems with astronauts in terms of inputs/outputs, processing, real-time AI, and knowledge-based system validation. Real-world applications are suggested such as the diagnosis, planning, and control of enginnered systems.

Research on intelligent machine self-perception method based on LSTM

NASA Astrophysics Data System (ADS)

Wang, Qiang; Cheng, Tao

2018-05-01

In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

A cross-sectional study to assess the intelligence quotient (IQ) of school going children aged 10-12 years in villages of Mysore district, India with different fluoride levels.

PubMed

Sebastian, Shibu Thomas; Sunitha, S

2015-01-01

Besides dental and skeletal fluorosis, excessive fluoride intake can also affect the central nervous system without first causing the physical deformities associated with skeletal fluorosis. With the existence of widespread endemic fluorosis in India, the possible adverse effect of elevated fluoride in drinking water on the Intelligence Quotient (IQ) level of children is a potentially serious public health problem. This study assessed the Intelligence Quotient (IQ) of school going children aged 10-12 years in villages of Mysore district with different fluoride levels. In this cross-sectional study, 405 school children aged 10-12 years were selected from three villages in Mysore district with normal fluoride (1.20 mg F/l), low fluoride (0.40 mg F/l) and high fluoride (2.20 mg F/l) in their water supplies. A pre designed questionnaire was used to collect the required data for the survey which included socio demographic details, oral hygiene practices, diet history, body mass index and dental fluorosis. Intelligence Quotient was assessed using Raven's colored Progressive Matrices Test. In bivariate analysis, significant relationships were found between water fluoride levels and Intelligence Quotient of school children (P < 0.05). In the high fluoride village, the proportion of children with IQ below 90, i.e. below average IQ was larger compared to normal and low fluoride village. Age, gender, parent education level and family income had no significant association with IQ. School children residing in area with higher than normal water fluoride level demonstrated more impaired development of intelligence when compared to school children residing in areas with normal and low water fluoride levels. Thus, children's intelligence can be affected by high water fluoride levels.

Inter-relationship of intelligence-quotient and self-concept with dental caries amongst socially handicapped orphan children.

PubMed

Virk, Pks; Jain, R L; Pathak, A; Sharma, U; Rajput, J S

2012-01-01

India has been the focus of many health surveys among normal, physically, and mentally handicapped children. However, the data, concerning oral health conditions of socially handicapped children living in orphanages, are scanty. To study the effect of parental inadequacy, environmental deprivation, and emotional disturbances on dental caries through intelligence quotient (IQ) and self-concept in orphan children and also to co-relate dental caries with different levels of IQ and self-concept. The study was carried out amongst socially handicapped children living in orphanages. 100 children in the age group of 10-14 years from orphanages were selected. Malin's Intelligence Scale for Indian Children (MISIC) was used to assess the intelligence quotient; self-concept questionnaire to assess self-concept of the child and recording of dental caries status of children was done as per WHO Index (1997). To assess the relationship of dental caries with IQ, student's unpaired t-test was used and; to find the relationship between self-concept and dental caries, Karl-Pearson's coefficient of co-relation was applied. the children in orphanages had a lower IQ and high caries experience but had an above average self-concept. There was also no co-relation between dental caries and self-concept. Orphan children, being socially handicapped, are at an increased risk for dental caries due to a lower IQ level, parental deprivation, and institutionalization. Moreover, lack of co-relation between dental caries and self-concept could be explained by the fact that dental caries is a lifelong process whereas different dimensions of self-concept are in a state of constant flux.

Issues Related to Obtaining Intelligence Quotient-Matched Controls in Autism Research

PubMed Central

Rao, Vanitha S.; Raman, Vijaya; Mysore, Ashok V.

2015-01-01

Background: Intelligence Quotient (IQ) is considered to be an index of global cognitive functioning and has traditionally been used as a fulcral measure in case-control studies in neuro-developmental disorders such as autism. Aim: The aim is to highlight the issues of “matching for IQ” with controls in autism research. Materials and Methods: Percentile scores on the Coloured Progressive Matrices of 20 children with autism in the age range of 5 to 12 years have been graphically compared with 21 age matched typically developing children. Results and Conclusions: The percentile scores of the so-called high functioning children with autism from special schools were well below that of typically developing children. There are many challenges when using IQ in case-control studies of autism. Alternative approaches need to be considered. PMID:25969598

Intelligence Quotient (IQ) in Congenital Strabismus.

PubMed

Bagheri, Abbas; Fallahi, Mohammad Reza; Tamannaifard, Shima; Vajebmonfared, Sara; Zonozian, Saideh

2013-04-01

To evaluate intelligence quotient (IQ) in patients with congenital strabismus. All patients with congenital strabismus scheduled for surgery were enrolled consecutively over a one year period in a cross-sectional study and were evaluated for verbal, performance and total IQ scores, and compared to the mean normal IQ of 100±15. During the study period, 109 patients with mean age of 18.4±10.5 (range, 4-63) years were included. Educational status in most patients (80%) was less than high-school. Most patients (80%) lived in urban areas and 46 patients (42.2%) had some degrees of unilateral or bilateral amblyopia. Mean verbal IQ was 87.2±19.6 (range, 45-127), performance IQ was 81±15.5 (range, 44-111) and total IQ was 83.5±18.3 (range, 40-120). Total IQ was significantly lower in comparison to the normal population (P<0.01) and significantly higher in urban as compared to rural residents (85.1±19.5 versus 77.3±10.8 respectively, P=0.02). Patients with coexisting amblyopia and alternate deviation had lower IQ levels. Verbal IQ was insignificantly higher in myopes than emmetropes and hyperopes. IQ was better with vertical deviations and was higher in esotropes than exotropes; however, these differences were not statistically significant (P>0.05 for all comparisons). Patients with congenital strabismus in this study had lower mean IQ scores than the normal population which may be due to genetic background or acquired causes secondary to strabismus.

The machine intelligence Hex project

NASA Astrophysics Data System (ADS)

Chalup, Stephan K.; Mellor, Drew; Rosamond, Fran

2005-12-01

Hex is a challenging strategy board game for two players. To enhance students’ progress in acquiring understanding and practical experience with complex machine intelligence and programming concepts we developed the Machine Intelligence Hex (MIHex) project. The associated undergraduate student assignment is about designing and implementing Hex players and evaluating them in an automated tournament of all programs developed by the class. This article surveys educational aspects of the MIHex project. Additionally, fundamental techniques for game programming as well as specific concepts for Hex board evaluation are reviewed. The MIHex game server and possibilities of tournament organisation are described. We summarise and discuss our experiences from running the MIHex project assignment over four consecutive years. The impact on student motivation and learning benefits are evaluated using questionnaires and interviews.

Intelligence quotient discrepancy indicates levels of motor competence in preschool children at risk for developmental delays.

PubMed

Yu, Tzu-Ying; Chen, Kuan-Lin; Chou, Willy; Yang, Shu-Han; Kung, Sheng-Chun; Lee, Ya-Chen; Tung, Li-Chen

2016-01-01

This study aimed to establish 1) whether a group difference exists in the motor competence of preschool children at risk for developmental delays with intelligence quotient discrepancy (IQD; refers to difference between verbal intelligence quotient [VIQ] and performance intelligence quotient [PIQ]) and 2) whether an association exists between IQD and motor competence. Children's motor competence and IQD were determined with the motor subtests of the Comprehensive Developmental Inventory for Infants and Toddlers and Wechsler Preschool and Primary Scale of Intelligence™ - Fourth Edition. A total of 291 children were included in three groups: NON-IQD (n=213; IQD within 1 standard deviation [SD]), VIQ>PIQ (n=39; VIQ>PIQ greater than 1 SD), and PIQ>VIQ (n=39; PIQ>VIQ greater than 1 SD). The results of one-way analysis of variance indicated significant differences among the subgroups for the "Gross and fine motor" subdomains of the Comprehensive Developmental Inventory for Infants and Toddlers, especially on the subtests of "body-movement coordination" (F=3.87, P<0.05) and "visual-motor coordination" (F=6.90, P<0.05). Motor competence was significantly worse in the VIQ>PIQ group than in the NON and PIQ>VIQ groups. Significant negative correlations between IQD and most of the motor subtests (r=0.31-0.46, P<0.01) were found only in the VIQ>PIQ group. This study demonstrates that 1) IQD indicates the level of motor competence in preschoolers at risk for developmental delays and 2) IQD is negatively associated with motor competence in preschoolers with significant VIQ>PIQ discrepancy. The first finding was that preschoolers with VIQ>PIQ discrepancy greater than 1 SD performed significantly worse on motor competence than did preschoolers without significant IQD and preschoolers with PIQ>VIQ discrepancy greater than 1 SD. However, preschoolers with significant PIQ>VIQ discrepancy performed better on motor competence than did preschoolers without significant IQD, though the

Language skills and intelligence quotient of protein energy malnutrition survivors.

PubMed

Nassar, May F; Shaaban, Sanaa Y; Nassar, Jilan F; Younis, Neveen T; Abdel-Mobdy, Ahmad E

2012-06-01

The study was conducted on 33 children aged 3-6 years who suffered from protein energy malnutrition (PEM) during infancy in comparison to 30 matching children to assess the long-term deficits in cognition and language skills. The patients' files were revised to record their admission and follow-up data and history, clinical examination, intelligence quotient and language assessment were done. The study revealed that 2-5 years from the acute attack the PEM patients were still shorter than the controls and their cognitive abilities were poorer. Their mental ages and language skills were mostly determined by their height and the duration of follow-up during their acute illness. Additionally their diet after the 3-5 years is still defective and does not meet their recommended daily allowance. These observations urge us to continue following these patients for longer durations to make sure no permanent damage occurs due to the PEM insult to the growing brain.

Integrated human-machine intelligence in space systems.

PubMed

Boy, G A

1992-07-01

This paper presents an artificial intelligence approach to integrated human-machine intelligence in space systems. It discusses the motivations for Intelligent Assistant Systems in both nominal and abnormal situations. The problem of constructing procedures is shown to be a very critical issue. In particular, keeping procedural experience in both design and operation is critical. We suggest what artificial intelligence can offer in this direction. Some crucial problems induced by this approach are discussed in detail. Finally, we analyze the various roles that would be shared by both astronauts, ground operators, and the intelligent assistant system.

Socioeconomic status and intelligence quotient as predictors of psychiatric disorders in children and adolescents with high-functioning autism spectrum disorder and in their siblings.

PubMed

Rosa, Mireia; Puig, Olga; Lázaro, Luisa; Calvo, Rosa

2016-11-01

Previous studies have shown high rates of comorbid disorders in children and adolescents with autism spectrum disorder, but failed to compare them with general population and few of them have identified predictors of comorbidity. This study compared the rates of psychiatric disorders in 50 children and adolescents with autism spectrum disorder, 24 of their siblings, 32 controls from general population and 22 of their siblings. Children and adolescent with autism spectrum disorder and their siblings had higher rates of attention deficit and hyperactivity disorder compared to controls. Lower socioeconomic status and intelligence quotient were the main risk factors. The contribution of socioeconomic status and intelligence quotient to increase the risk of developing comorbidity in autism spectrum disorder and psychopathology in their siblings deserves further study. © The Author(s) 2016.

Intelligence quotient discrepancy indicates levels of motor competence in preschool children at risk for developmental delays

PubMed Central

Yu, Tzu-Ying; Chen, Kuan-Lin; Chou, Willy; Yang, Shu-Han; Kung, Sheng-Chun; Lee, Ya-Chen; Tung, Li-Chen

2016-01-01

Purpose This study aimed to establish 1) whether a group difference exists in the motor competence of preschool children at risk for developmental delays with intelligence quotient discrepancy (IQD; refers to difference between verbal intelligence quotient [VIQ] and performance intelligence quotient [PIQ]) and 2) whether an association exists between IQD and motor competence. Methods Children’s motor competence and IQD were determined with the motor subtests of the Comprehensive Developmental Inventory for Infants and Toddlers and Wechsler Preschool and Primary Scale of Intelligence™ – Fourth Edition. A total of 291 children were included in three groups: NON-IQD (n=213; IQD within 1 standard deviation [SD]), VIQ>PIQ (n=39; VIQ>PIQ greater than 1 SD), and PIQ>VIQ (n=39; PIQ>VIQ greater than 1 SD). Results The results of one-way analysis of variance indicated significant differences among the subgroups for the “Gross and fine motor” subdomains of the Comprehensive Developmental Inventory for Infants and Toddlers, especially on the subtests of “body-movement coordination” (F=3.87, P<0.05) and “visual-motor coordination” (F=6.90, P<0.05). Motor competence was significantly worse in the VIQ>PIQ group than in the NON and PIQ>VIQ groups. Significant negative correlations between IQD and most of the motor subtests (r=0.31–0.46, P<0.01) were found only in the VIQ>PIQ group. Conclusion This study demonstrates that 1) IQD indicates the level of motor competence in preschoolers at risk for developmental delays and 2) IQD is negatively associated with motor competence in preschoolers with significant VIQ>PIQ discrepancy. The first finding was that preschoolers with VIQ>PIQ discrepancy greater than 1 SD performed significantly worse on motor competence than did preschoolers without significant IQD and preschoolers with PIQ>VIQ discrepancy greater than 1 SD. However, preschoolers with significant PIQ>VIQ discrepancy performed better on motor competence than

Comparison of intelligence quotients of first- and second-generation deaf children with cochlear implants.

PubMed

Amraei, K; Amirsalari, S; Ajalloueyan, M

2017-01-01

Hearing impairment is a common type of sensory loss in children. Studies indicate that children with hearing impairment are deficient in social, cognitive and communication skills. This study compared the intelligence quotients of first- and second-generation deaf children with cochlear implants. This research is causal-comparative. All 15 deaf children investigated had deaf parents and were selected from Baqiyatallah Cochlear Implant Center. The 15 children with cochlear implants were paired with similar children with hearing parents using purposive sampling. The findings show that the Hotelling trace of multivariate analysis of variance (F = 6.78, p < 0.01, η P 2  = 0.73) was significant. The tests of between-subjects effects for second-generation children was significantly higher than for first-generation children for all intelligence scales except knowledge. It can be assumed that second-generation children joined their family in the use of sign language as the primary experience before a cochlear implant. The use of sign language before cochlear implants is recommended. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Probabilistic machine learning and artificial intelligence.

PubMed

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

NASA Astrophysics Data System (ADS)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Comparing Intelligence Quotient (IQ)among 3 to 7-year-old strabismic and nonstrabismic children in an Iranian population.

PubMed

Ghaderpanah, Mahboubeh; Farrahi, Feraidoon; Khataminia, Gholamreza; Jahanbakhshi, Ahmad; Rezaei, Leila; Tashakori, Ashraf; Mahboubi, Mohammad

2015-06-25

This study was designed to compare the Intelligence Quotient (IQ) among 3 to 7-year-old strabismic and nonstrabismic children in an Iranian population. In this cross-sectional study, 108 preschool children with equal numbers of strabismic/non-strabismic disorder (age 3-7 years) were randomly selected from exceptional strabismus clinics of Ahvaz and were evaluated with the preschool and primary scale of intelligence versions of Wechsler (WPPSI). In the current study, 108 children were evaluated. In strabismic patients the mean performance, verbal and total IQ were 89.46±19.79, 89.57±21.57 and 91.54±22.08 respectively.These mean scores in normal children  were 91.89±47.53 , 87.56±15.6 and 89.96±17.62 consecuently. The results showed that these three different IQ subscales were not significantly different among 3 to 7 years old strabismic and nonstrabismic children ((P>0.05 for all comparisons). There was no significant difference in IQ between two sexes (P>0.05) while Persian tribe children had greater IQ score compared to other tribes (P<0.05). Also, higher paternal educational status of children related to higher IQ score. IQ score was better in combined deviations and was higher in exotropes than esotropes; however, these differences were not statistically significant.(p>0.05) In this evaluation, we did not found a significant negative interference of strabismus on IQ score of preschool children. It can be concluded that paternal educational level and tribe have a significant effect on intelligent quotient, while this is not the case on sex and ocular deviation.

Comparing Intelligence Quotient (IQ) Among 3 to 7-Year-Old Strabismic and Nonstrabismic Children in an Iranian Population

PubMed Central

Ghaderpanah, Mahboubeh; Farrahi, Feraidoon; Khataminia, Gholamreza; Jahanbakhshi, Ahmad; Rezaei, Leila; Tashakori, Ashraf; Mahboubi, Mohammad

2016-01-01

This study was designed to compare the Intelligence Quotient (IQ) among 3 to 7-year-old strabismic and nonstrabismic children in an Iranian population. In this cross-sectional study, 108 preschool children with equal numbers of strabismic/non-strabismic disorder (age 3–7 years) were randomly selected from exceptional strabismus clinics of Ahvaz and were evaluated with the preschool and primary scale of intelligence versions of Wechsler (WPPSI). In the current study, 108 children were evaluated. In strabismic patients the mean performance, verbal and total IQ were 89.46±19.79, 89.57±21.57 and 91.54±22.08 respectively. These mean scores in normal children were 91.89±47.53, 87.56±15.6 and 89.96±17.62consecuently. The results showed that these three different IQ subscales were not significantly different among 3 to 7 years old strabismic and nonstrabismic children ((P>0.05 for all comparisons). There was no significant difference in IQ between two sexes (P>0.05) while Persian tribe children had greater IQ score compared to other tribes (P<0.05). Also, higher paternal educational status of children related to higher IQ score. IQ score was better in combined deviations and was higher in exotropes than esotropes; however, these differences were not statistically significant (P>0.05). In this evaluation, we did not found a significant negative interference of strabismus on IQ score of preschool children. It can be concluded that paternal educational level and tribe have a significant effect on intelligent quotient, while this is not the case on sex and ocular deviation. PMID:26493422

Intelligent image processing for machine safety

NASA Astrophysics Data System (ADS)

Harvey, Dennis N.

1994-10-01

This paper describes the use of intelligent image processing as a machine guarding technology. One or more color, linear array cameras are positioned to view the critical region(s) around a machine tool or other piece of manufacturing equipment. The image data is processed to provide indicators of conditions dangerous to the equipment via color content, shape content, and motion content. The data from these analyses is then sent to a threat evaluator. The purpose of the evaluator is to determine if a potentially machine-damaging condition exists based on the analyses of color, shape, and motion, and on `knowledge' of the specific environment of the machine. The threat evaluator employs fuzzy logic as a means of dealing with uncertainty in the vision data.

Automated planning for intelligent machines in energy-related applications

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

Weisbin, C.R.; de Saussure, G.; Barhen, J.

1984-01-01

This paper discusses the current activities of the Center for Engineering Systems Advanced Research (CESAR) program related to plan generation and execution by an intelligent machine. The system architecture for the CESAR mobile robot (named HERMIES-1) is described. The minimal cut-set approach is developed to reduce the tree search time of conventional backward chaining planning techniques. Finally, a real-time concept of an Intelligent Machine Operating System is presented in which planning and reasoning is embedded in a system for resource allocation and process management.

An intelligent CNC machine control system architecture

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

Miller, D.J.; Loucks, C.S.

1996-10-01

Intelligent, agile manufacturing relies on automated programming of digitally controlled processes. Currently, processes such as Computer Numerically Controlled (CNC) machining are difficult to automate because of highly restrictive controllers and poor software environments. It is also difficult to utilize sensors and process models for adaptive control, or to integrate machining processes with other tasks within a factory floor setting. As part of a Laboratory Directed Research and Development (LDRD) program, a CNC machine control system architecture based on object-oriented design and graphical programming has been developed to address some of these problems and to demonstrate automated agile machining applications usingmore » platform-independent software.« less

[Algorithms, machine intelligence, big data : general considerations].

PubMed

Radermacher, F J

2015-08-01

We are experiencing astonishing developments in the areas of big data and artificial intelligence. They follow a pattern that we have now been observing for decades: according to Moore's Law,the performance and efficiency in the area of elementary arithmetic operations increases a thousand-fold every 20 years. Although we have not achieved the status where in the singular sense machines have become as "intelligent" as people, machines are becoming increasingly better. The Internet of Things has again helped to massively increase the efficiency of machines. Big data and suitable analytics do the same. If we let these processes simply continue, our civilization may be endangerd in many instances. If the "containment" of these processes succeeds in the context of a reasonable political global governance, a worldwide eco-social market economy, andan economy of green and inclusive markets, many desirable developments that are advantageous for our future may result. Then, at some point in time, the constant need for more and faster innovation may even stop. However, this is anything but certain. We are facing huge challenges.

Rethinking Intelligence Quotient Exclusion Criteria Practices in the Study of Attention Deficit Hyperactivity Disorder

PubMed Central

Mackenzie, Genevieve B.; Wonders, Elif

2016-01-01

Attention deficit hyperactivity disorder (ADHD) is associated with lower than average intelligence quotient (IQ) scores. However, research done on this disorder often excludes participants based on lower than average IQ’s (i.e., between 70 and 85). The purpose of this paper is to alert researchers to the consequences of excluding participants based on IQ’s within this range and to highlight the importance of providing a clear rationale when choosing to exclude participants based on IQ. Next, we offer recommendations for researching ADHD and their relative benefits and drawbacks of these approaches. Overall this paper emphasizes that including participants who have lower than average IQ in research on ADHD may promote a more realistic understanding of the condition and in turn improve our ability to treat it. PMID:27303350

On Intelligent Design and Planning Method of Process Route Based on Gun Breech Machining Process

NASA Astrophysics Data System (ADS)

Hongzhi, Zhao; Jian, Zhang

2018-03-01

The paper states an approach of intelligent design and planning of process route based on gun breech machining process, against several problems, such as complex machining process of gun breech, tedious route design and long period of its traditional unmanageable process route. Based on gun breech machining process, intelligent design and planning system of process route are developed by virtue of DEST and VC++. The system includes two functional modules--process route intelligent design and its planning. The process route intelligent design module, through the analysis of gun breech machining process, summarizes breech process knowledge so as to complete the design of knowledge base and inference engine. And then gun breech process route intelligently output. On the basis of intelligent route design module, the final process route is made, edited and managed in the process route planning module.

Meta-Analysis of Intelligence Quotient (IQ) in Obsessive-Compulsive Disorder.

PubMed

Abramovitch, Amitai; Anholt, Gideon; Raveh-Gottfried, Sagi; Hamo, Naama; Abramowitz, Jonathan S

2018-03-01

Obsessive compulsive disorder (OCD) is associated with a moderate degree of underperformance on cognitive tests, including deficient processing speed. However, despite little research focusing on Intelligence Quotient (IQ) in OCD, it has long been speculated that the disorder is associated with elevated intellectual capacity. The present meta-analytic study was, therefore, conducted to quantitatively summarize the literature on IQ in OCD systematically. We identified 98 studies containing IQ data among individuals with OCD and non-psychiatric comparison groups, and computed 108 effect sizes for Verbal IQ (VIQ, n = 55), Performance IQ (PIQ, n = 13), and Full Scale IQ (FSIQ, n = 40). Across studies, small effect sizes were found for FSIQ and VIQ, and a moderate effect size for PIQ, exemplifying reduced IQ in OCD. However, mean IQ scores across OCD samples were in the normative range. Moderator analyses revealed no significant moderating effect across clinical and demographic indices. We conclude that, although lower than controls, OCD is associated with normative FSIQ and VIQ, and relatively lowered PIQ. These results are discussed in light of neuropsychological research in OCD, and particularly the putative impact of reduced processing speed in this population. Recommendations for utilization of IQ tests in OCD, and directions for future studies are offered.

Intelligence quotient profile in myotonic dystrophy, intergenerational deficit, and correlation with CTG amplification.

PubMed Central

Turnpenny, P; Clark, C; Kelly, K

1994-01-01

An abbreviated Wechsler Adult Intelligence Scale Revised (WAIS-R) was used to assess verbal and arithmetical cognitive performance in 55 subjects with myotonic dystrophy (DM), covering all grades of disease severity, and 31 controls at 50% risk of inheriting DM. Scaled scores from the assessment were converted into an intelligence quotient (IQ) estimation on each person. Significant IQ differences were found between: (1) all 55 DM subjects (mean 90.2, SD 16.1) and 31 controls (102.6, SD 9.4), with no sex differences in either group; (2) 15 affected parents (99.3, SD 12.2) and their affected children (88.1, SD 17.2), where significance was dependent on parental sex being female; and (3) 15 pairs of affected sibs (89.6, SD 13.2) and their normal sibs (100.2, SD 7.6). IQ steadily declined as (1) the age of onset of signs and symptoms decreased, and (2) the CTG expansion size increased. The correlation appeared to be more linear with age of onset. The correlation of IQ difference and CTG expansion difference in both the DM parent-child pairs and normal sib-affected sib pairs was poor, indicating that CTG expansion is not a reliable predictor of IQ either in individual persons or families. Further analysis of cognitive function in DM is required to clarify specific deficits characteristic of this patient group. PMID:8071955

Intelligence quotient and iodine intake: a cross-sectional study in children.

PubMed

Santiago-Fernandez, Piedad; Torres-Barahona, Rosario; Muela-Martínez, J Antonio; Rojo-Martínez, Gemma; García-Fuentes, Eduardo; Garriga, M José; León, Ana García; Soriguer, Federico

2004-08-01

The association between iodine deficiency and poor mental and psychomotor development is known. However, most studies were undertaken in areas of very low iodine intake. We investigated whether a similar association is found in schoolchildren from southern Europe with a median urinary iodine output of 90 microg/liter. Urinary iodine levels were measured in 1221 children who also completed a questionnaire about their usual dietary habits. Intelligence quotient (IQ) was measured by Cattell's g factor test. IQ was significantly higher in children with urinary iodine levels above 100 microg/liter. The risk of having an IQ below the 25th percentile was significantly related to the intake of noniodized salt and drinking milk less than once a day. As expected, the risk of having an IQ below 70 was greater in children with urinary iodine levels less than 100 microg/liter. In conclusion, this study demonstrates that the IQ of schoolchildren in a developed country can be influenced by iodine intake. The results support the possibility of improving the IQ of many children from areas with mild iodine deficiency by ensuring an iodine intake sufficient to achieve a urinary iodine concentration greater than 100 microg/liter.

Machine intelligence and autonomy for aerospace systems

NASA Technical Reports Server (NTRS)

Heer, Ewald (Editor); Lum, Henry (Editor)

1988-01-01

The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.

Intelligence or Misorientation? Eurocentrism in the WISC-III.

ERIC Educational Resources Information Center

Kwate, Naa Oyo A.

2001-01-01

Examines the Eurocentric basis of the Wechsler Intelligence Scale for Children--Third Edition (WISC-III) and reveals its antagonistic and incompatible relationship to an Africentric conception of intellectual and mental health. Suggests that the WISC-III provides a measure of misorientation quotient rather than intelligence quotient, and notes…

Long-term effect of increased lead absorption on intelligence of children.

PubMed

Soong, W T; Chao, K Y; Jang, C S; Wang, J D

1999-01-01

The authors examined the reversibility of cognitive impairment caused by a mild increase in lead absorption among children. The results of our initial study revealed that air and soil outside a lead-recycling plant in Taiwan were seriously contaminated by lead, which was associated with lowered intelligence quotients of 32 children who attended a nearby kindergarten (i.e., kindergarten A). Thirty-five children-who were comparable with respect to age, sex, birth order, sibling number, and parental education level-from another kindergarten (i.e., kindergarten B) located 5 km from the plant were enrolled as the reference group. Following the initial study, kindergarten A school children moved 2 km from the lead-recycling plant. Twenty-eight children in each group were followed successfully 2.5 y later. Blood lead, intelligence quotient, and intelligence quotient-related factors were reassessed. The results showed that the average blood lead level of the exposed pupils dropped 6.9 microg/dl (standard deviation [SD] = 3.9 microg/dl) (p < .001), and the average intelligence quotient increased 11.7 points (SD = 13.2) (p < .01), compared with the results of the initial study. The average blood lead level of the reference group decreased by 1.7 microg/dl (SD = .1.3) (p < .001), whereas the average intelligence quotient increased by 4.2 points (SD = 13.8) (p = .115). There was a significant difference in intelligence quotients between the two groups during the initial study, but the difference subsequently disappeared during the follow up. The authors concluded that intelligence quotient impairment, caused by a mild subclinical elevation of blood lead (i.e., likely no more than 30 microg/dl) for a period of 1-3 y in 3- to 5-y-olds, is at least partially reversible.

Intelligent man/machine interfaces on the space station

NASA Technical Reports Server (NTRS)

Daughtrey, Rodney S.

1987-01-01

Some important topics in the development of good, intelligent, usable man/machine interfaces for the Space Station are discussed. These computer interfaces should adhere strictly to three concepts or doctrines: generality, simplicity, and elegance. The motivation for natural language interfaces and their use and value on the Space Station, both now and in the future, are discussed.

Solar prediction and intelligent machines

NASA Technical Reports Server (NTRS)

Johnson, Gordon G.

1987-01-01

The solar prediction program is aimed at reducing or eliminating the need to throughly understand the process previously developed and to still be able to produce a prediction. Substantial progress was made in identifying the procedures to be coded as well as testing some of the presently coded work. Another project involves work on developing ideas and software that should result in a machine capable of learning as well as carrying on an intelligent conversation over a wide range of topics. The underlying idea is to use primitive ideas and construct higher order ideas from these, which can then be easily related one to another.

Syndrome Diagnosis: Human Intuition or Machine Intelligence?

PubMed Central

Braaten, Øivind; Friestad, Johannes

2008-01-01

The aim of this study was to investigate whether artificial intelligence methods can represent objective methods that are essential in syndrome diagnosis. Most syndromes have no external criterion standard of diagnosis. The predictive value of a clinical sign used in diagnosis is dependent on the prior probability of the syndrome diagnosis. Clinicians often misjudge the probabilities involved. Syndromology needs objective methods to ensure diagnostic consistency, and take prior probabilities into account. We applied two basic artificial intelligence methods to a database of machine-generated patients - a ‘vector method’ and a set method. As reference methods we ran an ID3 algorithm, a cluster analysis and a naive Bayes’ calculation on the same patient series. The overall diagnostic error rate for the the vector algorithm was 0.93%, and for the ID3 0.97%. For the clinical signs found by the set method, the predictive values varied between 0.71 and 1.0. The artificial intelligence methods that we used, proved simple, robust and powerful, and represent objective diagnostic methods. PMID:19415142

Syndrome diagnosis: human intuition or machine intelligence?

PubMed

Braaten, Oivind; Friestad, Johannes

2008-01-01

The aim of this study was to investigate whether artificial intelligence methods can represent objective methods that are essential in syndrome diagnosis. Most syndromes have no external criterion standard of diagnosis. The predictive value of a clinical sign used in diagnosis is dependent on the prior probability of the syndrome diagnosis. Clinicians often misjudge the probabilities involved. Syndromology needs objective methods to ensure diagnostic consistency, and take prior probabilities into account. We applied two basic artificial intelligence methods to a database of machine-generated patients - a 'vector method' and a set method. As reference methods we ran an ID3 algorithm, a cluster analysis and a naive Bayes' calculation on the same patient series. The overall diagnostic error rate for the the vector algorithm was 0.93%, and for the ID3 0.97%. For the clinical signs found by the set method, the predictive values varied between 0.71 and 1.0. The artificial intelligence methods that we used, proved simple, robust and powerful, and represent objective diagnostic methods.

Games and Machine Learning: A Powerful Combination in an Artificial Intelligence Course

ERIC Educational Resources Information Center

Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

2010-01-01

Project MLeXAI [Machine Learning eXperiences in Artificial Intelligence (AI)] seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense--a simple real-time strategy game…

Machine intelligence and robotics: Report of the NASA study group. Executive summary

NASA Technical Reports Server (NTRS)

1979-01-01

A brief overview of applications of machine intelligence and robotics in the space program is given. These space exploration robots, global service robots to collect data for public service use on soil conditions, sea states, global crop conditions, weather, geology, disasters, etc., from Earth orbit, space industrialization and processing technologies, and construction of large structures in space. Program options for research, advanced development, and implementation of machine intelligence and robot technology for use in program planning are discussed. A vigorous and long-range program to incorporate and keep pace with state of the art developments in computer technology, both in spaceborne and ground-based computer systems is recommended.

Machine Learning-based Intelligent Formal Reasoning and Proving System

NASA Astrophysics Data System (ADS)

Chen, Shengqing; Huang, Xiaojian; Fang, Jiaze; Liang, Jia

2018-03-01

The reasoning system can be used in many fields. How to improve reasoning efficiency is the core of the design of system. Through the formal description of formal proof and the regular matching algorithm, after introducing the machine learning algorithm, the system of intelligent formal reasoning and verification has high efficiency. The experimental results show that the system can verify the correctness of propositional logic reasoning and reuse the propositional logical reasoning results, so as to obtain the implicit knowledge in the knowledge base and provide the basic reasoning model for the construction of intelligent system.

Design of intelligent proximity detection zones to prevent striking and pinning fatalities around continuous mining machines.

PubMed

Bissert, P T; Carr, J L; DuCarme, J P; Smith, A K

2016-01-01

The continuous mining machine is a key piece of equipment used in underground coal mining operations. Over the past several decades these machines have been involved in a number of mine worker fatalities. Proximity detection systems have been developed to avert hazards associated with operating continuous mining machines. Incorporating intelligent design into proximity detection systems allows workers greater freedom to position themselves to see visual cues or avoid other hazards such as haulage equipment or unsupported roof or ribs. However, intelligent systems must be as safe as conventional proximity detection systems. An evaluation of the 39 fatal accidents for which the Mine Safety and Health Administration has published fatality investigation reports was conducted to determine whether the accident may have been prevented by conventional or intelligent proximity. Multiple zone configurations for the intelligent systems were studied to determine how system performance might be affected by the zone configuration. Researchers found that 32 of the 39 fatalities, or 82 percent, may have been prevented by both conventional and intelligent proximity systems. These results indicate that, by properly configuring the zones of an intelligent proximity detection system, equivalent protection to a conventional system is possible.

Performance Intelligence, Sexual Offending and Psychopathy

ERIC Educational Resources Information Center

Nijman, Henk; Merckelbach, Harald; Cima, Maaike

2009-01-01

Previous studies have suggested that offenders have lowered verbal intelligence compared to their performance intelligence. This phenomenon has been linked traditionally to childhood risk factors (e.g. deficient education, abuse and neglect). Substantial discrepancies between performance intelligence quotients (PIQ) and verbal intelligence…

Impact of breast milk on intelligence quotient, brain size, and white matter development.

PubMed

Isaacs, Elizabeth B; Fischl, Bruce R; Quinn, Brian T; Chong, Wui K; Gadian, David G; Lucas, Alan

2010-04-01

Although observational findings linking breast milk to higher scores on cognitive tests may be confounded by factors associated with mothers' choice to breastfeed, it has been suggested that one or more constituents of breast milk facilitate cognitive development, particularly in preterms. Because cognitive scores are related to head size, we hypothesized that breast milk mediates cognitive effects by affecting brain growth. We used detailed data from a randomized feeding trial to calculate percentage of expressed maternal breast milk (%EBM) in the infant diet of 50 adolescents. MRI scans were obtained (mean age=15 y 9 mo), allowing volumes of total brain (TBV) and white and gray matter (WMV, GMV) to be calculated. In the total group, %EBM correlated significantly with verbal intelligence quotient (VIQ); in boys, with all IQ scores, TBV and WMV. VIQ was, in turn, correlated with WMV and, in boys only, additionally with TBV. No significant relationships were seen in girls or with gray matter. These data support the hypothesis that breast milk promotes brain development, particularly white matter growth. The selective effect in males accords with animal and human evidence regarding gender effects of early diet. Our data have important neurobiological and public health implications and identify areas for future mechanistic study.

Intelligence quotient is associated with epilepsy in children with intellectual disability in India

PubMed Central

Lakhan, Ram

2013-01-01

Background: Epilepsy is a disorder that is commonly found in people with intellectual disability (ID). The prevalence of epilepsy increases with the severity of ID. The objective of this study was to determine if there is an association between intelligence quotient (IQ) and epilepsy in children with ID. Materials and Methods: A total of 262 children, aged 3-18 years, with ID were identified as part of a community-based rehabilitation project. These children were examined for epilepsy and diagnosed by a psychiatrist and physicians based on results of electroencephalogram tests. A Spearman's correlation (ρ) was used to determine if there was an association between IQ scores and the occurrence of epilepsy. X2 statistics used to examine the relationship of epilepsy with gender, socioeconomic status, population type, severity of ID, family history of mental illness, mental retardation, epilepsy, and coexisting disorder. Results: Spearman's rho –0.605 demonstrates inverse association of IQ with epilepsy. X2 demonstrates statistically significant association (P < 0.05) with gender, severity of ID, cerebral palsy, behavior problems, and family history of mental illness, mental retardation, and epilepsy. Conclusions: Lower IQ score in children with ID has association with occurrence of epilepsy. Epilepsy is also found highly associated with male gender and lower age. PMID:24347947

Effect of environmental factors on intelligence quotient of children

PubMed Central

Makharia, Archita; Nagarajan, Abhishek; Mishra, Aakanksha; Peddisetty, Sandeep; Chahal, Deepak; Singh, Yashpal

2016-01-01

Introduction: A child's intelligence quotient (IQ) is determined by both genetic and environmental factors that start from the prenatal period itself. There is a lack of data on the factors which influence IQ in Indian children; therefore, we conducted a multicenter questionnaire-based study to determine the environmental factors which influence IQ in Indian children. Participants and Methods: In this cross-sectional observational study, we recruited 1065 schoolchildren between the age of 12 and 16 years from 2 government and 13 private schools in 5 towns, 6 cities, and 2 villages across India. All the children were administered a questionnaire consisting of various environmental factors such as parents' education, occupation, income, and the physical activity of the students. IQ scores were assessed using Ravens Standard Progressive Matrices. An approximate IQ score was calculated using the score on the Ravens test. IQ scores were divided into three groups: below normal IQ (0–79), normal IQ (80–119), and high IQ (above 120). The data were analyzed using SPSS software. Results: In this study, it was observed that the environmental factors such as place of residence, physical activity, family income, parental education, and occupation of the father had an impact on the IQ of the children. Children living in cities (P = 0.001), children having physical activity more than 5 h/weeks (P = 0.001), children with parents having a postgraduate or graduate level of education (P = 0.001), children whose father having a professional job (P = 0.001), and those with a higher family income (P = 0.001) were more likely to have high IQ. Conclusions: In the present study, we found that various environmental factors such as place of residence, physical exercise, family income, parents' occupation and education influence the IQ of a child to a great extent. Hence, a child must be provided with an optimal environment to be able to develop to his/her full genetic potential. PMID

Home environment, not duration of breast-feeding, predicts intelligence quotient of children at four years.

PubMed

Zhou, Shao J; Baghurst, Peter; Gibson, Robert A; Makrides, Maria

2007-03-01

We investigated the relation between duration of breast-feeding in infancy and the intelligence quotient (IQ) of children at 4 y of age in a well-nourished population of an industrialized country. Data on duration of breast-feeding were collected prospectively from a cohort of 302 children born between 1998 and 1999 in Adelaide, Australia. The IQ of the children was assessed at 4 y of age using the Stanford-Binet Intelligence Scale. Information on important predictors of childhood IQ including the quality of the home environment was also collected prospectively. Regression analyses were conducted to examine the effect of duration of breast-feeding on IQ with adjustment for potential confounders. There was no association between the duration of breast-feeding and IQ of the children. The expected IQ of a child at 4 y of age who was breast-fed for 6 mo was only 0.2 point (95% confidence interval -0.8 to 1.2) higher than that of a child who had never been breast-fed after adjustments for the quality of the home environment and socioeconomic characteristics of families using multivariable regression analysis. The quality of the home environment, as assessed by the Home Screening Questionnaire, was the strongest predictor of IQ at 4 y. There was no association between duration of breast-feeding and childhood IQ in this relatively well-nourished cohort from an industrialized society. In such settings, the apparent benefit of breast-feeding on cognitive function is most likely attributable to sociodemographic factors.

Dental ethics and emotional intelligence.

PubMed

Rosenblum, Alvin B; Wolf, Steve

2014-01-01

Dental ethics is often taught, viewed, and conducted as an intell enterprise, uninformed by other noncognitive factors. Emotional intelligence (EQ) is defined distinguished from the cognitive intelligence measured by Intelligence Quotient (IQ). This essay recommends more inclusion of emotional, noncognitive input to the ethical decision process in dental education and dental practice.

Quantity quotient reporting. A proposal for a standardized presentation of laboratory results.

PubMed

Haeckel, Rainer; Wosniok, Werner

2009-01-01

Laboratory results are reported in different units (despite international recommendations for SI units) together with different reference limits, of which several exist for many quantities. It is proposed to adopt the concept of the intelligence quotient and to report quantitative results as a quantity quotient (QQ) in laboratory medicine. This quotient is essentially the difference (measured result minus mean or mode value of the reference interval) divided by the observed biological variation CV(o). Thus, all quantities are reported in the same unit system with the same reference limits (for convenience shifted to e.g., 80-120). The critical difference can also be included in this standardization concept. In this way the information of reference intervals and the original result are integrated into one combined value, which has the same format for all quantities suited for quotient reporting (QR). The proposal of QR does not interfere with neither the current concepts of traceability, SI units or method standardization. This proposal represents a further step towards harmonization of reporting. It provides simple values which can be interpreted easily by physicians and their patients.

Association between resting-state coactivation in the parieto-frontal network and intelligence during late childhood and adolescence.

PubMed

Li, C; Tian, L

2014-06-01

A number of studies have associated the adult intelligence quotient with the structure and function of the bilateral parieto-frontal networks, whereas the relationship between intelligence quotient and parieto-frontal network function has been found to be relatively weak in early childhood. Because both human intelligence and brain function undergo protracted development into adulthood, the purpose of the present study was to provide a better understanding of the development of the parieto-frontal network-intelligence quotient relationship. We performed independent component analysis of resting-state fMRI data of 84 children and 50 adolescents separately and then correlated full-scale intelligence quotient with the spatial maps of the bilateral parieto-frontal networks of each group. In children, significant positive spatial-map versus intelligence quotient correlations were detected in the right angular gyrus and inferior frontal gyrus in the right parieto-frontal network, and no significant correlation was observed in the left parieto-frontal network. In adolescents, significant positive correlation was detected in the left inferior frontal gyrus in the left parieto-frontal network, and the correlations in the frontal pole in the 2 parieto-frontal networks were only marginally significant. The present findings not only support the critical role of the parieto-frontal networks for intelligence but indicate that the relationship between intelligence quotient and the parieto-frontal network in the right hemisphere has been well established in late childhood, and that the relationship in the left hemisphere was also established in adolescence. © 2014 by American Journal of Neuroradiology.

[Longitudinal study of intelligence quotient of a group of Dominican children who had experienced third degree malnutrition in their first two years of life].

PubMed

Castillo Ariza, M; Gonzalez Sanchez, M; Reyes Baez, J F; Ariza Castillo, M

1988-01-01

Intelligence quotients (IQs) were measured in 15 children hospitalized in their 1st 2 years with 3rd degree malnutrition and in their siblings of closest age who had no history of hospitalization for malnutrition. Clinical records were reviewed of 459 malnourished infants admitted to the Dr. Robert Reid Cabral Hospital in Santo Domingo between January 1976-January 1977. 230 of the children had died, and 15 of the 57 survivors who returned to the hospital for a preliminary interview were selected as subjects. Their closely aged siblings served as controls. Both groups were given Bender's visual-motor test and Weschler's intelligence scale for children. The ages of the subjects were 7-0 years and of siblings 6-13 years. Children who had spent a greater number of days in the hospital appeared to have a greater degree of mental impairment. 2 of the children with marasmus presented moderate mental retardation, 1 slight retardation, and another borderline retardation. The controls for these 5 cases included 1 moderately retarded, 2 slightly retarded, and 1 borderline case. 1 child with marasmus who was removed from his home showed a normal intelligence while his control who had remained in the home had a slight retardation. There was no consistent relationship between the cephalic perimeter and the IQ score, although 40% of the malnourished children and 30% of controls had perimeters below the normal range. 20% of cases and no controls required more than 18 months to learn to walk. Cases were also slower than controls to begin speaking and to be toilet trained. The tests showed that the control group members had somewhat higher intelligence quotients than the malnourished group. 53.2% of the malnourished children had IQs far below normal at 60-69, compared to 39.9% of controls. 93% of the study group and 87% of controls had IQs below normal values of 80-89. But there was no statistically significant difference in average IQs: 72.2 + or - 17.5 in the study group and 75

Intelligent quotient estimation of mental retarded people from different psychometric instruments using artificial neural networks.

PubMed

Di Nuovo, Alessandro G; Di Nuovo, Santo; Buono, Serafino

2012-02-01

The estimation of a person's intelligence quotient (IQ) by means of psychometric tests is indispensable in the application of psychological assessment to several fields. When complex tests as the Wechsler scales, which are the most commonly used and universally recognized parameter for the diagnosis of degrees of retardation, are not applicable, it is necessary to use other psycho-diagnostic tools more suited for the subject's specific condition. But to ensure a homogeneous diagnosis it is necessary to reach a common metric, thus, the aim of our work is to build models able to estimate accurately and reliably the Wechsler IQ, starting from different psycho-diagnostic tools. Four different psychometric tests (Leiter international performance scale; coloured progressive matrices test; the mental development scale; psycho educational profile), along with the Wechsler scale, were administered to a group of 40 mentally retarded subjects, with various pathologies, and control persons. The obtained database is used to evaluate Wechsler IQ estimation models starting from the scores obtained in the other tests. Five modelling methods, two statistical and three from machine learning, that belong to the family of artificial neural networks (ANNs) are employed to build the estimator. Several error metrics for estimated IQ and for retardation level classification are defined to compare the performance of the various models with univariate and multivariate analyses. Eight empirical studies show that, after ten-fold cross-validation, best average estimation error is of 3.37 IQ points and mental retardation level classification error of 7.5%. Furthermore our experiments prove the superior performance of ANN methods over statistical regression ones, because in all cases considered ANN models show the lowest estimation error (from 0.12 to 0.9 IQ points) and the lowest classification error (from 2.5% to 10%). Since the estimation performance is better than the confidence interval of

The DISC Quotient

NASA Astrophysics Data System (ADS)

Elliott, John R.; Baxter, Stephen

2012-09-01

D.I.S.C: Decipherment Impact of a Signal's Content. The authors present a numerical method to characterise the significance of the receipt of a complex and potentially decipherable signal from extraterrestrial intelligence (ETI). The purpose of the scale is to facilitate the public communication of work on any such claimed signal, as such work proceeds, and to assist in its discussion and interpretation. Building on a "position" paper rationale, this paper looks at the DISC quotient proposed and develops the algorithmic steps and comprising measures that form this post detection strategy for information dissemination, based on prior work on message detection, decipherment. As argued, we require a robust and incremental strategy, to disseminate timely, accurate and meaningful information, to the scientific community and the general public, in the event we receive an "alien" signal that displays decipherable information. This post-detection strategy is to serve as a stepwise algorithm for a logical approach to information extraction and a vehicle for sequential information dissemination, to manage societal impact. The "DISC Quotient", which is based on signal analysis processing stages, includes factors based on the signal's data quantity, structure, affinity to known human languages, and likely decipherment times. Comparisons with human and other phenomena are included as a guide to assessing likely societal impact. It is submitted that the development, refinement and implementation of DISC as an integral strategy, during the complex processes involved in post detection and decipherment, is essential if we wish to minimize disruption and optimize dissemination.

A coordination theory for intelligent machines

NASA Technical Reports Server (NTRS)

Wang, Fei-Yue; Saridis, George N.

1990-01-01

A formal model for the coordination level of intelligent machines is established. The framework of the coordination level investigated consists of one dispatcher and a number of coordinators. The model called coordination structure has been used to describe analytically the information structure and information flow for the coordination activities in the coordination level. Specifically, the coordination structure offers a formalism to (1) describe the task translation of the dispatcher and coordinators; (2) represent the individual process within the dispatcher and coordinators; (3) specify the cooperation and connection among the dispatcher and coordinators; (4) perform the process analysis and evaluation; and (5) provide a control and communication mechanism for the real-time monitor or simulation of the coordination process. A simple procedure for the task scheduling in the coordination structure is presented. The task translation is achieved by a stochastic learning algorithm. The learning process is measured with entropy and its convergence is guaranteed. Finally, a case study of the coordination structure with three coordinators and one dispatcher for a simple intelligent manipulator system illustrates the proposed model and the simulation of the task processes performed on the model verifies the soundness of the theory.

Study on intelligent processing system of man-machine interactive garment frame model

NASA Astrophysics Data System (ADS)

Chen, Shuwang; Yin, Xiaowei; Chang, Ruijiang; Pan, Peiyun; Wang, Xuedi; Shi, Shuze; Wei, Zhongqian

2018-05-01

A man-machine interactive garment frame model intelligent processing system is studied in this paper. The system consists of several sensor device, voice processing module, mechanical parts and data centralized acquisition devices. The sensor device is used to collect information on the environment changes brought by the body near the clothes frame model, the data collection device is used to collect the information of the environment change induced by the sensor device, voice processing module is used for speech recognition of nonspecific person to achieve human-machine interaction, mechanical moving parts are used to make corresponding mechanical responses to the information processed by data collection device.it is connected with data acquisition device by a means of one-way connection. There is a one-way connection between sensor device and data collection device, two-way connection between data acquisition device and voice processing module. The data collection device is one-way connection with mechanical movement parts. The intelligent processing system can judge whether it needs to interact with the customer, realize the man-machine interaction instead of the current rigid frame model.

The negative impact of living environment on intelligence quotient of primary school children in Baghdad City, Iraq: a cross-sectional study.

PubMed

Ghazi, Hasanain Faisal; Isa, Zaleha Md; Aljunid, Syed; Shah, Shamsul Azhar; Tamil, Azmi Mohd; Abdalqader, Mohammed A

2012-07-27

Environmental factors play a very important role in the child development process, especially in a situation like that of Iraq. Thirteen years of economic sanctions followed by the 2003 war and 8 years of unstable security have affected the daily life of Iraqi families and children. The objective of this study was to assess the associations between living environment domains and child intelligence quotient (IQ) score. A cross-sectional survey was conducted among 529 children aged 7-8 years from five primary schools in Baghdad during September-October, 2011. The five schools represent people living a range of conditions, and include of both high and low socio-economic groups. Living environment was assessed by 13 questionnaire items, consists of three domains: physical safety , mental stress and public services. While IQ was assessed by Raven Colored progressive matrices. Among the participants, 22% were of low intelligence versus 77% of high intelligence and 19% lived in a poor environment. There were significant associations between the mental stress and service living environment domains and child IQ (p = 0.009 and p = 0.001, respectively). In Iraq, child IQ was found to be associated with the mental stress and service domains of the living environment. This study findings will help authorities in their efforts to improve living environment.

Social Intelligence in a Human-Machine Collaboration System

NASA Astrophysics Data System (ADS)

Nakajima, Hiroshi; Morishima, Yasunori; Yamada, Ryota; Brave, Scott; Maldonado, Heidy; Nass, Clifford; Kawaji, Shigeyasu

In this information society of today, it is often argued that it is necessary to create a new way of human-machine interaction. In this paper, an agent with social response capabilities has been developed to achieve this goal. There are two kinds of information that is exchanged by two entities: objective and functional information (e.g., facts, requests, states of matters, etc.) and subjective information (e.g., feelings, sense of relationship, etc.). Traditional interactive systems have been designed to handle the former kind of information. In contrast, in this study social agents handling the latter type of information are presented. The current study focuses on sociality of the agent from the view point of Media Equation theory. This article discusses the definition, importance, and benefits of social intelligence as agent technology and argues that social intelligence has a potential to enhance the user's perception of the system, which in turn can lead to improvements of the system's performance. In order to implement social intelligence in the agent, a mind model has been developed to render affective expressions and personality of the agent. The mind model has been implemented in a human-machine collaborative learning system. One differentiating feature of the collaborative learning system is that it has an agent that performs as a co-learner with which the user interacts during the learning session. The mind model controls the social behaviors of the agent, thus making it possible for the user to have more social interactions with the agent. The experiment with the system suggested that a greater degree of learning was achieved when the students worked with the co-learner agent and that the co-learner agent with the mind model that expressed emotions resulted in a more positive attitude toward the system.

Relationship between Intelligence Quotient and Musical Ability in Children with Cochlear Implantation.

PubMed

Soleimanifar, Simin; Jafari, Zahra; Motasaddi Zarandy, Masoud; Asadi, Houman; Haghani, Hamid

2016-09-01

Children with cochlear implants (CIs) may experience few opportunities for positive musical experiences, and musical perception is therefore often not sufficiently developed. This paper investigates and discusses the relationship between intelligence quotient (IQ) and musical ability in children with CIs compared with children with normal hearing. This was a comparative analytical study conducted in 48 children with unilateral CI and 48 normal-hearing children, 6-8 years of age, with 'normal' IQ and no formal music training. The average IQ score in the experimental and control groups were 105.41 and 106.31, respectively. No statistically significant differences were detected between Raven's IQ scores in both groups. Data were collected by administering Raven's Colored Progressive Matrices IQ Tests and the Montreal Battery of Evaluation of Musical Abilities (MBEMA) Test, consisting of scale, contour, interval, rhythm, and memory sections. Mean total MBEMA score in the experimental and control groups was 58.93 and 72.16 (out of 100), respectively. Significant differences were evident between scores of children with CIs in comparison with their normal-hearing peers (P≤0.001). A remarkable direct correlation between IQ and musical scores in both the control (r≥0.38) and experimental (r≥0.37) groups was observed. IQ has a noticeable effect on music processing and facilitates the perception of various musical elements. With regard to the mutual relationship between IQ and musical skills, this study illustrates the advantage of determining music perception scores and highlights the importance of appropriate musical intervention in order to enhance auditory neural plasticity, especially in children with cochlear implantation.

Parental education predicts change in intelligence quotient after childhood epilepsy surgery.

PubMed

Meekes, Joost; van Schooneveld, Monique M J; Braams, Olga B; Jennekens-Schinkel, Aag; van Rijen, Peter C; Hendriks, Marc P H; Braun, Kees P J; van Nieuwenhuizen, Onno

2015-04-01

To know whether change in the intelligence quotient (IQ) of children who undergo epilepsy surgery is associated with the educational level of their parents. Retrospective analysis of data obtained from a cohort of children who underwent epilepsy surgery between January 1996 and September 2010. We performed simple and multiple regression analyses to identify predictors associated with IQ change after surgery. In addition to parental education, six variables previously demonstrated to be associated with IQ change after surgery were included as predictors: age at surgery, duration of epilepsy, etiology, presurgical IQ, reduction of antiepileptic drugs, and seizure freedom. We used delta IQ (IQ 2 years after surgery minus IQ shortly before surgery) as the primary outcome variable, but also performed analyses with pre- and postsurgical IQ as outcome variables to support our findings. To validate the results we performed simple regression analysis with parental education as the predictor in specific subgroups. The sample for regression analysis included 118 children (60 male; median age at surgery 9.73 years). Parental education was significantly associated with delta IQ in simple regression analysis (p = 0.004), and also contributed significantly to postsurgical IQ in multiple regression analysis (p = 0.008). Additional analyses demonstrated that parental education made a unique contribution to prediction of delta IQ, that is, it could not be replaced by the illness-related variables. Subgroup analyses confirmed the association of parental education with IQ change after surgery for most groups. Children whose parents had higher education demonstrate on average a greater increase in IQ after surgery and a higher postsurgical--but not presurgical--IQ than children whose parents completed at most lower secondary education. Parental education--and perhaps other environmental variables--should be considered in the prognosis of cognitive function after childhood epilepsy

Critical Combinations of Radiation Dose and Volume Predict Intelligence Quotient and Academic Achievement Scores After Craniospinal Irradiation in Children With Medulloblastoma

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

Merchant, Thomas E., E-mail: thomas.merchant@stjude.org; Schreiber, Jane E.; Wu, Shengjie

Purpose: To prospectively follow children treated with craniospinal irradiation to determine critical combinations of radiation dose and volume that would predict for cognitive effects. Methods and Materials: Between 1996 and 2003, 58 patients (median age 8.14 years, range 3.99-20.11 years) with medulloblastoma received risk-adapted craniospinal irradiation followed by dose-intense chemotherapy and were followed longitudinally with multiple cognitive evaluations (through 5 years after treatment) that included intelligence quotient (estimated intelligence quotient, full-scale, verbal, and performance) and academic achievement (math, reading, spelling) tests. Craniospinal irradiation consisted of 23.4 Gy for average-risk patients (nonmetastatic) and 36-39.6 Gy for high-risk patients (metastatic or residual disease >1.5 cm{sup 2}). The primary sitemore » was treated using conformal or intensity modulated radiation therapy using a 2-cm clinical target volume margin. The effect of clinical variables and radiation dose to different brain volumes were modeled to estimate cognitive scores after treatment. Results: A decline with time for all test scores was observed for the entire cohort. Sex, race, and cerebrospinal fluid shunt status had a significant impact on baseline scores. Age and mean radiation dose to specific brain volumes, including the temporal lobes and hippocampi, had a significant impact on longitudinal scores. Dichotomized dose distributions at 25 Gy, 35 Gy, 45 Gy, and 55 Gy were modeled to show the impact of the high-dose volume on longitudinal test scores. The 50% risk of a below-normal cognitive test score was calculated according to mean dose and dose intervals between 25 Gy and 55 Gy at 10-Gy increments according to brain volume and age. Conclusions: The ability to predict cognitive outcomes in children with medulloblastoma using dose-effects models for different brain subvolumes will improve treatment planning, guide intervention, and

Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

PubMed

Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Sadeghfam, Sina; Moghaddam, Asghar Asghari

2017-01-01

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. Copyright © 2016 Elsevier B.V. All rights reserved.

Maternal pre-pregnancy BMI and intelligence quotient (IQ) in 5-year-old children: a cohort based study.

PubMed

Bliddal, Mette; Olsen, Jørn; Støvring, Henrik; Eriksen, Hanne-Lise F; Kesmodel, Ulrik S; Sørensen, Thorkild I A; Nøhr, Ellen A

2014-01-01

An association between maternal pre-pregnancy BMI and childhood intelligence quotient (IQ) has repeatedly been found but it is unknown if this association is causal or due to confounding caused by genetic or social factors. We used a cohort of 1,783 mothers and their 5-year-old children sampled from the Danish National Birth Cohort. The children participated between 2003 and 2008 in a neuropsychological assessment of cognitive ability including IQ tests taken by both the mother and the child. Linear regression analyses were used to estimate the associations between parental BMI and child IQ adjusted for a comprehensive set of potential confounders. Child IQ was assessed with the Wechsler Primary and Preschool Scales of Intelligence--Revised (WPPSI-R). The crude association between maternal BMI and child IQ showed that BMI was adversely associated with child IQ with a reduction in IQ of -0.40 point for each one unit increase in BMI. This association was attenuated after adjustment for social factors and maternal IQ to a value of -0.27 (-0.50 to -0.03). After mutual adjustment for the father's BMI and all other factors except maternal IQ, the association between paternal BMI and child IQ yielded a regression coefficient of -0.26 (-0.59 to 0.07), which was comparable to that seen for maternal BMI (-0.20 (-0.44 to 0.04)). Although maternal pre-pregnancy BMI was inversely associated with the IQ of her child, the similar association with paternal BMI suggests that it is not a specific pregnancy related adiposity effect.

Relationship between Intelligence Quotient and Musical Ability in Children with Cochlear Implantation

PubMed Central

Soleimanifar, Simin; Jafari, Zahra; Motasaddi Zarandy, Masoud; Asadi, Houman; Haghani, Hamid

2016-01-01

Introduction: Children with cochlear implants (CIs) may experience few opportunities for positive musical experiences, and musical perception is therefore often not sufficiently developed. This paper investigates and discusses the relationship between intelligence quotient (IQ) and musical ability in children with CIs compared with children with normal hearing. Materials and Methods: This was a comparative analytical study conducted in 48 children with unilateral CI and 48 normal-hearing children, 6–8 years of age, with ‘normal’ IQ and no formal music training. The average IQ score in the experimental and control groups were 105.41 and 106.31, respectively. No statistically significant differences were detected between Raven’s IQ scores in both groups. Data were collected by administering Raven's Colored Progressive Matrices IQ Tests and the Montreal Battery of Evaluation of Musical Abilities (MBEMA) Test, consisting of scale, contour, interval, rhythm, and memory sections. Results: Mean total MBEMA score in the experimental and control groups was 58.93 and 72.16 (out of 100), respectively. Significant differences were evident between scores of children with CIs in comparison with their normal-hearing peers (P≤0.001). A remarkable direct correlation between IQ and musical scores in both the control (r≥0.38) and experimental (r≥0.37) groups was observed. Conclusion: IQ has a noticeable effect on music processing and facilitates the perception of various musical elements. With regard to the mutual relationship between IQ and musical skills, this study illustrates the advantage of determining music perception scores and highlights the importance of appropriate musical intervention in order to enhance auditory neural plasticity, especially in children with cochlear implantation. PMID:27738611

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Intelligent Machine Learning Approaches for Aerospace Applications

NASA Astrophysics Data System (ADS)

Sathyan, Anoop

Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire

A Brief Assessment of Intelligence Decline in Schizophrenia As Represented by the Difference between Current and Premorbid Intellectual Quotient

PubMed Central

Ohi, Kazutaka; Sumiyoshi, Chika; Fujino, Haruo; Yasuda, Yuka; Yamamori, Hidenaga; Fujimoto, Michiko; Sumiyoshi, Tomiki; Hashimoto, Ryota

2017-01-01

Patients with schizophrenia elicit several clinical features, such as psychotic symptoms, cognitive impairment, and subtle decline of intelligence. The latter two features become evident around the onset of the illness, although they may exist even before the disease onset in a substantial proportion of cases. Here, we review the literature concerning intelligence decline (ID) during the progression of schizophrenia. ID can be estimated by comparing premorbid and current intellectual quotient (IQ) by means of the Adult Reading Test and Wechsler Adult Intelligence Scale (WAIS), respectively. For the purpose of brief assessment, we have recently developed the WAIS-Short Form, which consists of Similarities and Symbol Search and well reflects functional outcomes. According to the degree of ID, patients were classified into three distinct subgroups; deteriorated, preserved, and compromised groups. Patients who show deteriorated IQ (deteriorated group) elicit ID from a premorbid level (≥10-point difference between current and premorbid IQ), while patients who show preserved or compromised IQ do not show such decline (<10-point difference). Furthermore, the latter patients were divided into patients with preserved and compromised IQ based on an estimated premorbid IQ score >90 or below 90, respectively. We have recently shown the distribution of ID in a large cohort of schizophrenia patients. Consistent with previous studies, approximately 30% of schizophrenia patients had a decline of less than 10 points, i.e., normal intellectual performance. In contrast, approximately 70% of patients showed deterioration of IQ. These results indicate that there is a subgroup of schizophrenia patients who have mild or minimal intellectual deficits, following the onset of the disorder. Therefore, a careful assessment of ID is important in identifying appropriate interventions, including medications, cognitive remediation, and social/community services. PMID:29312019

A proposal of an architecture for the coordination level of intelligent machines

NASA Technical Reports Server (NTRS)

Beard, Randall; Farah, Jeff; Lima, Pedro

1993-01-01

The issue of obtaining a practical, structured, and detailed description of an architecture for the Coordination Level of Center for Intelligent Robotic Systems for Sapce Exploration (CIRSSE) Testbed Intelligent Controller is addressed. Previous theoretical and implementation works were the departure point for the discussion. The document is organized as follows: after this introductory section, section 2 summarizes the overall view of the Intelligent Machine (IM) as a control system, proposing a performance measure on which to base its design. Section 3 addresses with some detail implementation issues. An hierarchic petri-net with feedback-based learning capabilities is proposed. Finally, section 4 is an attempt to address the feedback problem. Feedback is used for two functions: error recovery and reinforcement learning of the correct translations for the petri-net transitions.

Routine human-competitive machine intelligence by means of genetic programming

NASA Astrophysics Data System (ADS)

Koza, John R.; Streeter, Matthew J.; Keane, Martin

2004-01-01

Genetic programming is a systematic method for getting computers to automatically solve a problem. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem. The paper demonstrates that genetic programming (1) now routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterized topology; and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time. Recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits and controllers demonstrate these points.

The relationship between happiness and intelligent quotient: the contribution of socio-economic and clinical factors.

PubMed

Ali, A; Ambler, G; Strydom, A; Rai, D; Cooper, C; McManus, S; Weich, S; Meltzer, H; Dein, S; Hassiotis, A

2013-06-01

Happiness and higher intelligent quotient (IQ) are independently related to positive health outcomes. However, there are inconsistent reports about the relationship between IQ and happiness. The aim was to examine the association between IQ and happiness and whether it is mediated by social and clinical factors. Method The authors analysed data from the 2007 Adult Psychiatric Morbidity Survey in England. The participants were adults aged 16 years or over, living in private households in 2007. Data from 6870 participants were included in the study. Happiness was measured using a validated question on a three-point scale. Verbal IQ was estimated using the National Adult Reading Test and both categorical and continuous IQ was analysed. Happiness is significantly associated with IQ. Those in the lowest IQ range (70-99) reported the lowest levels of happiness compared with the highest IQ group (120-129). Mediation analysis using the continuous IQ variable found dependency in activities of daily living, income, health and neurotic symptoms were strong mediators of the relationship, as they reduced the association between happiness and IQ by 50%. Those with lower IQ are less happy than those with higher IQ. Interventions that target modifiable variables such as income (e.g. through enhancing education and employment opportunities) and neurotic symptoms (e.g. through better detection of mental health problems) may improve levels of happiness in the lower IQ groups.

Psychometric Characteristics of the Emotional Quotient Inventory, Youth Version, Short Form, in Hungarian High School Students

ERIC Educational Resources Information Center

Kun, Bernadette; Urban, Robert; Paksi, Borbala; Csobor, Lujza Vargane; Olah, Attila; Demetrovics, Zsolt

2012-01-01

Research on the psychometric characteristics, including factor structure, of measures assessing emotional intelligence improve our understanding of the manifest and latent dimensions of the construct. The factor structure of the Bar-On Emotional Quotient Inventory (Bar-On, 1997), despite the popularity of the measure, has been the subject of only…

THE COMPARISON OF INTELLIGENCE QUOTIENTS OF ATOPIC AND NONATOPIC CHILDREN IN IBADAN, NIGERIA

PubMed Central

Daramola, O O M; Ayoola, O O; Ogunbiyi, A O

2010-01-01

Background: Atopy-related illnesses such as atopic dermatitis and asthma are chronic illnesses, and children suffering from such illnesses are subjected to frequent absenteeism from school. Studies have shown that the performance of children with asthma was comparable to their healthy counterparts despite their absenteeism at school, in contrast to findings in other chronic illnesses like epilepsy. Aim: In the present study, we investigated the association between atopy and intelligence quotient (IQ) scores in a group of Nigerian children in Ibadan, a city in southwestern Nigeria. Materials and Methods: This is a cross-sectional study of children in an urban elementary school. Questionnaires to ascertain the presence of atopy-associated conditions such as hay fever, atopic dermatitis, asthma, allergic rhinitis, and allergic conjunctivitis were administered to the parents of 128 pupils in the 3rd to 6th grades of elementary school. Based on the responses to the questionnaire, pupils were categorized as being atopic and nonatopic. All the pupils underwent the Standard Progressive Matrices IQ test. The IQ scores were then compared among these two groups of children. Results: Out of the children studied, 26.6% were found to have atopy and after adjusting for factors such as age and sex, the IQ scores in this atopic group were not found to be statistically different from the scores in the nonatopic group (r = 2.122872, P = 0.009). Conclusion: IQ scores were not statistically significantly different for children with and without atopy. Thus, the presence of atopy does not appear to be associated with low IQ scores and hence, may not be related to poor school performance. PMID:21063510

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

PubMed

Jeong, Doo Seok; Hwang, Cheol Seong

2018-04-18

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Towards General Evaluation of Intelligent Systems: Lessons Learned from Reproducing AIQ Test Results

NASA Astrophysics Data System (ADS)

Vadinský, Ondřej

2018-03-01

This paper attempts to replicate the results of evaluating several artificial agents using the Algorithmic Intelligence Quotient test originally reported by Legg and Veness. Three experiments were conducted: One using default settings, one in which the action space was varied and one in which the observation space was varied. While the performance of freq, Q0, Qλ, and HLQλ corresponded well with the original results, the resulting values differed, when using MC-AIXI. Varying the observation space seems to have no qualitative impact on the results as reported, while (contrary to the original results) varying the action space seems to have some impact. An analysis of the impact of modifying parameters of MC-AIXI on its performance in the default settings was carried out with the help of data mining techniques used to identifying highly performing configurations. Overall, the Algorithmic Intelligence Quotient test seems to be reliable, however as a general artificial intelligence evaluation method it has several limits. The test is dependent on the chosen reference machine and also sensitive to changes to its settings. It brings out some differences among agents, however, since they are limited in size, the test setting may not yet be sufficiently complex. A demanding parameter sweep is needed to thoroughly evaluate configurable agents that, together with the test format, further highlights computational requirements of an agent. These and other issues are discussed in the paper along with proposals suggesting how to alleviate them. An implementation of some of the proposals is also demonstrated.

11th Annual Intelligent Ground Vehicle Competition: team approaches to intelligent driving and machine vision

NASA Astrophysics Data System (ADS)

Theisen, Bernard L.; Lane, Gerald R.

2003-10-01

The Intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990's. The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics, and mobile platform fundamentals to design and build an unmanned system. Both the U.S. and international teams focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligtent driving capabilities. Over the past 11 years, the competition has challenged both undergraduates and graduates, including Ph.D. students with real world applications in intelligent transportation systems, the military, and manufacturing automation. To date, teams from over 40 universities and colleges have participated. In this paper, we describe some of the applications of the technologies required by this competition, and discuss the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the three-day competition are highlighted. Finally, an assessment of the competition based on participant feedback is presented.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

PubMed

Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

PubMed Central

Salehi, Mojtaba

2010-01-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020

Emotional Intelligence of Malaysian Academia towards Work Performance

ERIC Educational Resources Information Center

Ngah, Rohana; Jusoff, Kamaruzaman; Rahman, Zanariah Abdul

2009-01-01

This paper describes the research conducted in relating to emotional intelligence of university staff to work attitude. The Emotional Intelligence (EI) Scale devised by Schutte et al. (1998) is used in this study, which is more suitable compared to BarOn Emotional Quotient Inventory. Beside their experiences, knowledge and skills, emotion play an…

Randomised trial of early diet in preterm babies and later intelligence quotient

PubMed Central

Lucas, A; Morley, R; Cole, T J

1998-01-01

Objectives To determine whether perinatal nutrition influences cognitive function at 7½-8 years in children born preterm. Design Randomised, blinded nutritional intervention trial. Blinded follow up at 7½-8 years. Setting Intervention phase in two neonatal units; follow up in a clinic or school setting. Subjects 424 preterm infants who weighed under 1850 g at birth; 360 of those who survived were tested at 7½-8 years. Interventions Standard infant formula versus nutrient enriched preterm formula randomly assigned as sole diet (trial A) or supplements to maternal milk (trial B) fed for a mean of 1 month. Main outcome measures Intelligence quotient (IQ) at 7½-8 years with abbreviated Weschler intelligence scale for children (revised). Results There was a major sex difference in the impact of diet. At 7½-8 years boys previously fed standard versus preterm formula as sole diet had a 12.2 point disadvantage (95% confidence interval 3.7 to 20.6; P<0.01) in verbal IQ. In those with highest intakes of trial diets corresponding figures were 9.5 point disadvantage and 14.4 point disadvantage in overall IQ (1.2 to 17.7; P<0.05) and verbal IQ (5.7 to 23.2; P<0.01). Consequently, more infants fed term formula had low verbal IQ (<85): 31% versus 14% for both sexes (P=0.02) and 47% versus 13% in boys P=0.009). There was a higher incidence of cerebral palsy in those fed term formula; exclusion of such children did not alter the findings. Conclusions Preterm infants are vulnerable to suboptimal early nutrition in terms of their cognitive performance—notably, language based skills—at 7½-8 years, when cognitive scores are highly predictive of adult ones. Our data on cerebral palsy generate a new hypothesis that suboptimal nutritional management during a critical or plastic early period of rapid brain growth could impair functional compensation in those sustaining an earlier brain insult. Cognitive function, notably in males, may be permanently impaired by suboptimal

Poor postdischarge head growth is related to a 10% lower intelligence quotient in very preterm infants at the chronological age of five years.

PubMed

Neubauer, Vera; Fuchs, Teresa; Griesmaier, Elke; Kager, Katrin; Pupp-Peglow, Ulrike; Kiechl-Kohlendorfer, Ursula

2016-05-01

This study examined the relationship between head growth and cognitive outcome at the age of five years in preterm infants born at less than 32 weeks of gestation from 2003 to 2009, as previous research has mostly focused on outcomes in toddlers. The head circumference of 273 very preterm infants born in Tyrol, Austria, was measured at birth, discharge, the corrected ages of three, 12 and 24 months and the chronological age of five years. Suboptimal head size was defined as a head circumference of more than one standard deviation below the mean. Full-scale intelligence quotient (IQ) at five years was determined using Wechsler Preschool and Primary Scales of Intelligence, third edition. Infants with a suboptimal head size at the age of three months had a significantly lower median IQ than those with a normal head size (90 [20-122] versus 98 [20-138], p = 0.001) and from three months onwards they were more likely to exhibit cognitive delay. A suboptimal head size from the age of three months was consistently related to a 10% lower IQ, and this study adds further evidence that head growth failure, especially during the early postdischarge period, is related to impaired cognitive abilities. ©2016 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

NASA Astrophysics Data System (ADS)

Hu, Yao; Quinn, Christopher J.; Cai, Ximing; Garfinkle, Noah W.

2017-11-01

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm- corn price, underlying groundwater level, monthly mean temperature and precipitation- have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems.

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

PubMed

Knuth, Kevin H

2003-12-15

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have

Intelligent machines in the twenty-first century: foundations of inference and inquiry

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.

2003-01-01

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have

Artificial Intelligence.

ERIC Educational Resources Information Center

Wash, Darrel Patrick

1989-01-01

Making a machine seem intelligent is not easy. As a consequence, demand has been rising for computer professionals skilled in artificial intelligence and is likely to continue to go up. These workers develop expert systems and solve the mysteries of machine vision, natural language processing, and neural networks. (Editor)

A proposed method to estimate premorbid full scale intelligence quotient (FSIQ) for the Canadian Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) using demographic and combined estimation procedures.

PubMed

Schoenberg, Mike R; Lange, Rael T; Saklofske, Donald H

2007-11-01

Establishing a comparison standard in neuropsychological assessment is crucial to determining change in function. There is no available method to estimate premorbid intellectual functioning for the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV). The WISC-IV provided normative data for both American and Canadian children aged 6 to 16 years old. This study developed regression algorithms as a proposed method to estimate full-scale intelligence quotient (FSIQ) for the Canadian WISC-IV. Participants were the Canadian WISC-IV standardization sample (n = 1,100). The sample was randomly divided into two groups (development and validation groups). The development group was used to generate regression algorithms; 1 algorithm only included demographics, and 11 combined demographic variables with WISC-IV subtest raw scores. The algorithms accounted for 18% to 70% of the variance in FSIQ (standard error of estimate, SEE = 8.6 to 14.2). Estimated FSIQ significantly correlated with actual FSIQ (r = .30 to .80), and the majority of individual FSIQ estimates were within +/-10 points of actual FSIQ. The demographic-only algorithm was less accurate than algorithms combining demographic variables with subtest raw scores. The current algorithms yielded accurate estimates of current FSIQ for Canadian individuals aged 6-16 years old. The potential application of the algorithms to estimate premorbid FSIQ is reviewed. While promising, clinical validation of the algorithms in a sample of children and/or adolescents with known neurological dysfunction is needed to establish these algorithms as a premorbid estimation procedure.

Children with unilateral hearing loss may have lower intelligence quotient scores: A meta-analysis.

PubMed

Purcell, Patricia L; Shinn, Justin R; Davis, Greg E; Sie, Kathleen C Y

2016-03-01

In this meta-analysis, we reviewed observational studies investigating differences in intelligence quotient (IQ) scores of children with unilateral hearing loss compared to children with normal hearing. PubMed Medline, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO. A query identified all English-language studies related to pediatric unilateral hearing loss published between January 1980 and December 2014. Titles, abstracts, and articles were reviewed to identify observational studies reporting IQ scores. There were 261 unique titles, with 29 articles undergoing full review. Four articles were identified, which included 173 children with unilateral hearing loss and 202 children with normal hearing. Ages ranged from 6 to 18 years. Three studies were conducted in the United States and one in Mexico. All were of high quality. All studies reported full-scale IQ results; three reported verbal IQ results; and two reported performance IQ results. Children with unilateral hearing loss scored 6.3 points lower on full-scale IQ, 95% confidence interval (CI) [-9.1, -3.5], P value < 0.001; and 3.8 points lower on performance IQ, 95% CI [-7.3, -0.2], P value 0.04. When investigating verbal IQ, we detected substantial heterogeneity among studies; exclusion of the outlying study resulted in significant difference in verbal IQ of 4 points, 95% CI [-7.5, -0.4], P value 0.028. This meta-analysis suggests children with unilateral hearing loss have lower full-scale and performance IQ scores than children with normal hearing. There also may be disparity in verbal IQ scores. Laryngoscope, 126:746-754, 2016. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

Games and machine learning: a powerful combination in an artificial intelligence course

NASA Astrophysics Data System (ADS)

Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

2010-03-01

Project MLeXAI (Machine Learning eXperiences in Artificial Intelligence (AI)) seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense - a simple real-time strategy game and Checkers - a classic turn-based board game. From the instructors' prospective, we examine aspects of design and implementation as well as the challenges and rewards of using the curricula. We explore students' responses to the projects via the results of a common survey. Finally, we compare the student perceptions from the game-based projects to non-game based projects from the first phase of Project MLeXAI.

Intelligent Machines in the 21st Century: Automating the Processes of Inference and Inquiry

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.

2003-01-01

The last century saw the application of Boolean algebra toward the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines. in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. However, modern intelligent machines work by inferring knowledge using only their pre-programmed prior knowledge and the data provided. They lack the ability to ask questions, or request data that would aid their inferences. Recent advances in understanding the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we identified the algebra of questions as the free distributive algebra, which now allows us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper we describe this logic of inference and inquiry using the mathematics of partially ordered sets and the scaffolding of lattice theory, discuss the far-reaching implications of the methodology, and demonstrate its application with current examples in machine learning. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them to not only make inferences from data, but also decide which question to ask, experiment to perform, or measurement to take given what they have learned and what they are designed to understand.

Determinants of Body Mass Index and Intelligence Quotient of Elementary School Children in Mountain Area of Nepal: An Explorative Study.

PubMed

Ranabhat, Chhabi; Kim, Chun-Bae; Park, Myung Bae; Kim, Chang Soo; Freidoony, Leila

2016-02-03

The physical growth and cognitive development of elementary school children are very crucial and this group is large in number but has little research dedicated to it. The physical growth and cognitive development of children occur simultaneously and can be measured by body mass index (BMI) and intelligence quotient (IQ). Previous studies could not sufficiently focus on both aspects. The aim of this study was to identify determinants of BMI and IQ of students in two elementary schools in the Humla district of Nepal. Two randomly selected elementary schools and all children available there (n = 173) participated in the study. BMI was calculated with the objective of proper measurement of height and weight of the children. Likewise, the updated universal nonverbal intelligence test (UNIT) was applied for IQ. Descriptive statistics, t-test, analysis of variance and multiple linear regressions were used when appropriate. Study findings showed that one-tenth of the children had grade 2 thinness (-2SD) and about one-third had poor IQ (<85). The age of the children (p < 0.05) and household economic status (p < 0.001) were significant for the BMI. Likewise, frequencies of illness in the previous year, mother's education (p < 0.05) and father's education (p < 0.001) were significant factors for the IQ score. More commonly, BMI and IQ scores were significantly lower in the ultra-poor group. Economic status and parent education are still major determinants of IQ and BMI in these students. Special programs and strategies should be launched to improve the poor ranking of IQ and BMI.

Does Implementing an Emotional Intelligence Program Guarantee Student Achievement?

ERIC Educational Resources Information Center

Wilkens, Coral L.; Wilmore, Elaine

2015-01-01

Being a 21st century learner may require a shift in the education paradigm. To be successful students may need to possess a different type of intelligence. Cherniss (2001), Goleman (1995), and O'Neil (1996), suggest that the key to positive life outcomes might consider emotional intelligence as more important than intellectual quotient (IQ).…

[Intelligence level and intelligence structure of children with primary nocturnal enuresis].

PubMed

Dai, Xiao-Mei; Ma, Hong-Wei; Pan, Xue-Xia

2007-10-01

Some research has shown that there may be memory/caution (M/C) defects in children with primary nocturnal enuresis (PNE). This study aimed to investigate whether the defects affect the intelligence level and the intelligence structure in PNE children. Intelligence tests were performed by means of Wechsler Young Children Scales of Intelligence (C-WISC) in 40 children with PNE and 40 age-matched normal children. The full intelligence quotient (FIQ), verbal IQ (VIQ) and performances IQ (PIQ) in the PNE group were in a normal range and did not different from the control group. There were significant differences in the scores for digit extent, decipher, knowledge and arithmetics between the PNE and the control groups (P < 0.05). M/C factor in the PNE group was statistically lower than in the control group (93.44 +/-11.27 vs 100.03 +/-11.79; P < 0.05). The total intelligence level of children with PNE was normal, but the M/C factor in the intelligence structure had some defects, suggesting that PNE may be related to the abnormity of executive function in the frontal lobe.

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 1: Executive Summary

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions are explored. The specific tasks which will be required by future space projects are identified. ARAMIS options which are candidates for those space project tasks and the relative merits of these options are defined and evaluated. Promising applications of ARAMIS and specific areas for further research are identified. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Machine intelligence-based decision-making (MIND) for automatic anomaly detection

NASA Astrophysics Data System (ADS)

Prasad, Nadipuram R.; King, Jason C.; Lu, Thomas

2007-04-01

Any event deemed as being out-of-the-ordinary may be called an anomaly. Anomalies by virtue of their definition are events that occur spontaneously with no prior indication of their existence or appearance. Effects of anomalies are typically unknown until they actually occur, and their effects aggregate in time to show noticeable change from the original behavior. An evolved behavior would in general be very difficult to correct unless the anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly. Substantial time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to abnormal behavior. There is a critical need therefore to automatically detect anomalous behavior as and when they may occur, and to do so with the operator in the loop. Human-machine interaction results in better machine learning and a better decision-support mechanism. This is the fundamental concept of intelligent control where machine learning is enhanced by interaction with human operators, and vice versa. The paper discusses a revolutionary framework for the characterization, detection, identification, learning, and modeling of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems.

Intelligence-Augmented Rat Cyborgs in Maze Solving.

PubMed

Yu, Yipeng; Pan, Gang; Gong, Yongyue; Xu, Kedi; Zheng, Nenggan; Hua, Weidong; Zheng, Xiaoxiang; Wu, Zhaohui

2016-01-01

Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains.

Intelligence-Augmented Rat Cyborgs in Maze Solving

PubMed Central

Yu, Yipeng; Pan, Gang; Gong, Yongyue; Xu, Kedi; Zheng, Nenggan; Hua, Weidong; Zheng, Xiaoxiang; Wu, Zhaohui

2016-01-01

Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains. PMID:26859299

Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

NASA Astrophysics Data System (ADS)

Luo, Zhonghui; Peng, Bin; Xiao, Qijun; Bai, Lu

2018-03-01

Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

NASA Astrophysics Data System (ADS)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Biomimetics in Intelligent Sensor and Actuator Automation Systems

NASA Astrophysics Data System (ADS)

Bruckner, Dietmar; Dietrich, Dietmar; Zucker, Gerhard; Müller, Brit

Intelligent machines are really an old mankind's dream. With increasing technological development, the requirements for intelligent devices also increased. However, up to know, artificial intelligence (AI) lacks solutions to the demands of truly intelligent machines that have no problems to integrate themselves into daily human environments. Current hardware with a processing power of billions of operations per second (but without any model of human-like intelligence) could not substantially contribute to the intelligence of machines when compared with that of the early AI times. There are great results, of course. Machines are able to find the shortest path between far apart cities on the map; algorithms let you find information described only by few key words. But no machine is able to get us a cup of coffee from the kitchen yet.

Is obesity associated with a decline in intelligence quotient during the first half of the life course?

PubMed

Belsky, Daniel W; Caspi, Avshalom; Goldman-Mellor, Sidra; Meier, Madeline H; Ramrakha, Sandhya; Poulton, Richie; Moffitt, Terrie E

2013-11-01

Cross-sectional studies have found that obesity is associated with low intellectual ability and neuroimaging abnormalities in adolescence and adulthood. Some have interpreted these associations to suggest that obesity causes intellectual decline in the first half of the life course. We analyzed data from a prospective longitudinal study to test whether becoming obese was associated with intellectual decline from childhood to midlife. We used data from the ongoing Dunedin Multidisciplinary Health and Development Study, a population-representative birth cohort study of 1,037 children in New Zealand who were followed prospectively from birth (1972-1973) through their fourth decade of life with a 95% retention rate. Intelligence quotient (IQ) was measured in childhood and adulthood. Anthropometric measurements were taken at birth and at 12 subsequent in-person assessments. As expected, cohort members who became obese had lower adulthood IQ scores. However, obese cohort members exhibited no excess decline in IQ. Instead, these cohort members had lower IQ scores since childhood. This pattern remained consistent when we accounted for children's birth weights and growth during the first years of life, as well as for childhood-onset obesity. Lower IQ scores among children who later developed obesity were present as early as 3 years of age. We observed no evidence that obesity contributed to a decline in IQ, even among obese individuals who displayed evidence of the metabolic syndrome and/or elevated systemic inflammation.

Is Obesity Associated With a Decline in Intelligence Quotient During the First Half of the Life Course?

PubMed Central

Belsky, Daniel W.; Caspi, Avshalom; Goldman-Mellor, Sidra; Meier, Madeline H.; Ramrakha, Sandhya; Poulton, Richie; Moffitt, Terrie E.

2013-01-01

Cross-sectional studies have found that obesity is associated with low intellectual ability and neuroimaging abnormalities in adolescence and adulthood. Some have interpreted these associations to suggest that obesity causes intellectual decline in the first half of the life course. We analyzed data from a prospective longitudinal study to test whether becoming obese was associated with intellectual decline from childhood to midlife. We used data from the ongoing Dunedin Multidisciplinary Health and Development Study, a population-representative birth cohort study of 1,037 children in New Zealand who were followed prospectively from birth (1972–1973) through their fourth decade of life with a 95% retention rate. Intelligence quotient (IQ) was measured in childhood and adulthood. Anthropometric measurements were taken at birth and at 12 subsequent in-person assessments. As expected, cohort members who became obese had lower adulthood IQ scores. However, obese cohort members exhibited no excess decline in IQ. Instead, these cohort members had lower IQ scores since childhood. This pattern remained consistent when we accounted for children's birth weights and growth during the first years of life, as well as for childhood-onset obesity. Lower IQ scores among children who later developed obesity were present as early as 3 years of age. We observed no evidence that obesity contributed to a decline in IQ, even among obese individuals who displayed evidence of the metabolic syndrome and/or elevated systemic inflammation. PMID:24029684

Artificial Intelligence.

ERIC Educational Resources Information Center

Thornburg, David D.

1986-01-01

Overview of the artificial intelligence (AI) field provides a definition; discusses past research and areas of future research; describes the design, functions, and capabilities of expert systems and the "Turing Test" for machine intelligence; and lists additional sources for information on artificial intelligence. Languages of AI are…

The Flynn Effect: A Quantitative Commentary on Modernity and Human Intelligence

ERIC Educational Resources Information Center

Clark, Cameron M.; Lawlor-Savage, Linette; Goghari, Vina M.

2016-01-01

Average intelligence quotient (IQ) scores have been rising throughout the 20th century and likely before--a pattern now known as the Flynn effect. The central thesis of this paper is that the Flynn effect does not represent genuine increases in general intelligence but rather an increasing aptitude for the types of modern thinking that modern life…

Effects of lead, cadmium, arsenic, and mercury co-exposure on children's intelligence quotient in an industrialized area of southern China.

PubMed

Pan, Shangxia; Lin, Lifeng; Zeng, Fan; Zhang, Jianpeng; Dong, Guanghui; Yang, Boyi; Jing, You; Chen, Shejun; Zhang, Gan; Yu, Zhiqiang; Sheng, Guoying; Ma, Huimin

2018-04-01

Exposure to metal(loid)s can lead to adverse effects on nervous system in children. However, little is known about the possible interaction effects of simultaneous exposure to multiple metal(loid)s on children's intelligence. In addition, relationship between blood lead concentrations (<100 μg/L) and the intelligence of children over 5 years needs further epidemiological evidence. We recruited 530 children aged 9-11 years, including 266 living in a town near an industrialized area and 264 from another town in the same city in South China as a reference. The levels of lead (Pb), cadmium (Cd), arsenic (As) and mercury (Hg) in blood (BPb, BCd, BAs, BHg) and urine (UPb, UCd, UAs, UHg) were assessed, as well as children's intelligence quotient (IQ). A significant decrease in IQ scores was identified in children from the industrialized town (p < .05), who had statistically higher geometric mean concentrations of BPb, BCd, UPb, UCd and UHg (65.89, 1.93, 4.04, 1.43 and 0.37 μg/L, respectively) compared with children from the reference town (37.21, 1.07, 2.14, 1.02 and 0.30 μg/L, respectively, p < .05). After adjusting confounders, only BPb had a significant negative association with IQ (B = -0.10, 95% confidence interval: -0.15 to -0.05, p < .001), which indicated that IQ decreased 0.10 points when BPb increased 1 μg/L. Significant negative interactions between BAs and BHg, positive interaction between UPb and UCd on IQ were observed (p < .10), and BPb <100 μg/L still negatively affected IQ (p < .05). Our findings suggest that although only BPb causes a decline in children's IQ when simultaneously exposed to these four metal(loid)s at relatively low levels, interactions between metal(loid)s on children's IQ should be paid special attention, and the reference standard in China of 100 μg/L BPb for children above 5 years old should be revised. Copyright © 2017 Elsevier Ltd. All rights reserved.

Big Data Analytics and Machine Intelligence Capability Development at NASA Langley Research Center: Strategy, Roadmap, and Progress

NASA Technical Reports Server (NTRS)

Ambur, Manjula Y.; Yagle, Jeremy J.; Reith, William; McLarney, Edward

2016-01-01

In 2014, a team of researchers, engineers and information technology specialists at NASA Langley Research Center developed a Big Data Analytics and Machine Intelligence Strategy and Roadmap as part of Langley's Comprehensive Digital Transformation Initiative, with the goal of identifying the goals, objectives, initiatives, and recommendations need to develop near-, mid- and long-term capabilities for data analytics and machine intelligence in aerospace domains. Since that time, significant progress has been made in developing pilots and projects in several research, engineering, and scientific domains by following the original strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This report summarizes the work to date in Data Intensive Scientific Discovery, Deep Content Analytics, and Deep Q&A projects, as well as the progress made in collaboration, outreach, and education. Recommendations for continuing this success into future phases of the initiative are also made.

Different aspects of emotional intelligence of borderline personality disorder.

PubMed

Peter, Mathell; Arntz, Arnoud R; Klimstra, Theo; Vingerhoets, Ad J J M

2018-01-01

The present study investigated deficiencies in different components of emotional intelligence in borderline personality disorder (BPD). The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) and the Emotional Quotient Inventory (EQ-i) were used to assess EI dimensions. BPD patients (N = 85; 69 women; M = 33.6 years) were compared with Cluster C personality disorder (PD) patients (N = 39; 23 women; M = 36.6 years) and nonpatients (N = 69; 44 women; M = 35.6 years). Compared to the Cluster C PD patients and the nonpatient group, BPD patients displayed only deficits in their ability to understand emotions as measured with the Mayer-Salovey-Caruso Emotional Intelligence Test. The Emotional Quotient Inventory only revealed deficits in stress management in BPD patients compared to Cluster C PD patients. Our findings suggest that BPD patients have the ability to regulate emotions effectively, but they subjectively experience deficits in emotion regulation and therefore may not use this ability when they need it. Copyright © 2017 John Wiley & Sons, Ltd.

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

The Product and Quotient Rules Revisited

ERIC Educational Resources Information Center

Eggleton, Roger; Kustov, Vladimir

2011-01-01

Mathematical elegance is illustrated by strikingly parallel versions of the product and quotient rules of basic calculus, with some applications. Corresponding rules for second derivatives are given: the product rule is familiar, but the quotient rule is less so.

Measures of Emotional Intelligence and Social Acceptability in Children: A Concurrent Validity Study

ERIC Educational Resources Information Center

Windingstad, Sunny; McCallum, R. Steve; Bell, Sherry Mee; Dunn, Patrick

2011-01-01

The concurrent validity of two measures of Emotional Intelligence (EI), one considered a trait measure, the other an ability measure, was examined by administering the Emotional Quotient Inventory: Youth Version (EQi:YV; Bar-On & Parker, 2000), the Mayer-Salovey-Caruso Emotional Intelligence Test: Youth Version (MSCEIT:YV; Mayer, Salovey, &…

The effect of long chain polyunsaturated fatty acid supplementation on intelligence in low birth weight infant during lactation: A meta-analysis

PubMed Central

Song, Yuan; Liu, Ya; Pan, Yun; Yuan, Xiaofeng; Chang, Pengyu; Tian, Yuan; Cui, Weiwei

2018-01-01

Background Low birth weight infant (LBWIs) are prone to mental and behavioural problems. As an important constituent of the brain and retina, long chain polyunsaturated fatty acids are essential for foetal infant mental and visual development. The effect of lactation supplemented with long chain polyunsaturated fatty acids (LCPUFA) on the improvement of intelligence in low birth weight children requires further validation. Methods In this study, a comprehensive search of multiple databases was performed to identify studies focused the association between intelligence and long chain polyunsaturated fatty acid supplementation in LBWIs. Studies that compared the Bayley Scales of Infant Development (BSID) or the Wechsler Abbreviated Scale of Intelligence for Children (WISC) scores between LBWIs who were supplemented and controls that were not supplemented with LCPUFA during lactation were selected for inclusion in the meta-analysis. Results The main outcome was the mean difference in the mental development index (MDI) and psychomotor development index (PDI) of the BSID and the full scale intelligence quotient (FSIQ), verbal intelligence quotient (VIQ) and performance intelligence quotient (PIQ) of the WISC between LBWIs and controls. Our findings indicated that the mean BSID or WISC scores in LBWIs did not differ between the supplemented groups and controls. Conclusion This meta-analysis does not reveal that LCPUFA supplementation has a significant impact on the level of intelligence in LBWIs. PMID:29634752

Research on Intelligent Interface in Double-front Work Machines

NASA Astrophysics Data System (ADS)

Kamezaki, Mitsuhiro; Iwata, Hiroyasu; Sugano, Shigeki

This paper proposes a work state identification method with full independent of work environmental conditions and operator skill levels for construction machinery. Advanced operated-work machines, which have been designed for complicated tasks, require intelligent systems that can provide the quantitative work analysis needed to determine effective work procedures and that can provide operational and cognitive support for operators. Construction work environments are extremely complicated, however, and this makes state identification, which is a key technology for an intelligent system, difficult. We therefore defined primitive static states (PSS) that are determined using on-off information for the lever inputs and manipulator loads for each part of the grapple and front and that are completely independent of the various environmental conditions and variation in operator skill level that can cause an incorrect work state identification. To confirm the usefulness of PSS, we performed experiments with a demolition task by using our virtual reality simulator. We confirmed that PSS could robustly and accurately identify the work states and that untrained skills could be easily inferred from the results of PSS-based work analysis. We also confirmed in skill-training experiments that advice information based on PSS-based skill analysis greatly improved operator's work performance. We thus confirmed that PSS can adequately identify work states and are useful for work analysis and skill improvement.

The Relationship between Emotional Intelligence and Middle School Students with Learning Disabilities

ERIC Educational Resources Information Center

Petersen, Vanessa C.

2010-01-01

The purpose of the present study was to investigate the relationship between emotional intelligence and academic success in middle school students with learning disabilities. Emotional Intelligence (EI) was measured using the BarOn Emotional Quotient Inventory: Youth Version (BarOn EQ-i: YV). The results of the BarOn EQ-i: YV was then compared to…

A Sequential Mixed Methods Study: An Exploration of the Use of Emotional Intelligence by Senior Student Affairs Officers in Managing Critical Incidents

ERIC Educational Resources Information Center

Johnson, Brian

2013-01-01

Emotional intelligence is a relatively new academic discipline that began forming in the early 1990s. Currently, emotional intelligence is used in academia and in business as a new intelligence quotient. This research study investigates how Senior Student Affairs Officers' use their emotional intelligence ability during critical incidents. The…

Calabi-Yau metrics for quotients and complete intersections

DOE PAGES

Braun, Volker; Brelidze, Tamaz; Douglas, Michael R.; ...

2008-05-22

We extend previous computations of Calabi-Yau metrics on projective hypersurfaces to free quotients, complete intersections, and free quotients of complete intersections. In particular, we construct these metrics on generic quintics, four-generation quotients of the quintic, Schoen Calabi-Yau complete intersections and the quotient of a Schoen manifold with Z₃ x Z₃ fundamental group that was previously used to construct a heterotic standard model. Various numerical investigations into the dependence of Donaldson's algorithm on the integration scheme, as well as on the Kähler and complex structure moduli, are also performed.

Dyadic Short Forms of the Wechsler Adult Intelligence Scale-IV.

PubMed

Denney, David A; Ringe, Wendy K; Lacritz, Laura H

2015-08-01

Full Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) administration can be time-consuming and may not be necessary when intelligence quotient estimates will suffice. Estimated Full Scale Intelligence Quotient (FSIQ) and General Ability Index (GAI) scores were derived from nine dyadic short forms using individual regression equations based on data from a clinical sample (n = 113) that was then cross validated in a separate clinical sample (n = 50). Derived scores accounted for 70%-83% of the variance in FSIQ and 77%-88% of the variance in GAI. Predicted FSIQs were strongly associated with actual FSIQ (rs = .73-.88), as were predicted and actual GAIs (rs = .80-.93). Each of the nine dyadic short forms of the WAIS-IV was a good predictor of FSIQ and GAI in the validation sample. These data support the validity of WAIS-IV short forms when time is limited or lengthier batteries cannot be tolerated by patients. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Metrics in Keplerian orbits quotient spaces

NASA Astrophysics Data System (ADS)

Milanov, Danila V.

2018-03-01

Quotient spaces of Keplerian orbits are important instruments for the modelling of orbit samples of celestial bodies on a large time span. We suppose that variations of the orbital eccentricities, inclinations and semi-major axes remain sufficiently small, while arbitrary perturbations are allowed for the arguments of pericentres or longitudes of the nodes, or both. The distance between orbits or their images in quotient spaces serves as a numerical criterion for such problems of Celestial Mechanics as search for common origin of meteoroid streams, comets, and asteroids, asteroid families identification, and others. In this paper, we consider quotient sets of the non-rectilinear Keplerian orbits space H. Their elements are identified irrespective of the values of pericentre arguments or node longitudes. We prove that distance functions on the quotient sets, introduced in Kholshevnikov et al. (Mon Not R Astron Soc 462:2275-2283, 2016), satisfy metric space axioms and discuss theoretical and practical importance of this result. Isometric embeddings of the quotient spaces into R^n, and a space of compact subsets of H with Hausdorff metric are constructed. The Euclidean representations of the orbits spaces find its applications in a problem of orbit averaging and computational algorithms specific to Euclidean space. We also explore completions of H and its quotient spaces with respect to corresponding metrics and establish a relation between elements of the extended spaces and rectilinear trajectories. Distance between an orbit and subsets of elliptic and hyperbolic orbits is calculated. This quantity provides an upper bound for the metric value in a problem of close orbits identification. Finally the invariance of the equivalence relations in H under coordinates change is discussed.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 2: Space projects overview

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and their related ground support functions are studied so that informed decisions can be made on which aspects of ARAMIS to develop. The space project breakdowns, which are used to identify tasks ('functional elements'), are described. The study method concentrates on the production of a matrix relating space project tasks to pieces of ARAMIS.

A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data

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

Bramer, Lisa M.; Chatterjee, Samrat; Holmes, Aimee E.

Business intelligence problems are particularly challenging due to the use of large volume and high velocity data in attempts to model and explain complex underlying phenomena. Incremental machine learning based approaches for summarizing trends and identifying anomalous behavior are often desirable in such conditions to assist domain experts in characterizing their data. The overall goal of this research is to develop a machine learning algorithm that enables predictive analysis on streaming data, detects changes and anomalies in the data, and can evolve based on the dynamic behavior of the data. Commercial shipping transaction data for the U.S. is used tomore » develop and test a Naïve Bayes model that classifies several companies into lines of businesses and demonstrates an ability to predict when the behavior of these companies changes by venturing into other lines of businesses.« less

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

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

Jha, Sumit Kumar; Pullum, Laura L; Ramanathan, Arvind

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studyingmore » the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.« less

STATISTICS AND INTELLIGENCE IN DEVELOPING COUNTRIES: A NOTE.

PubMed

Kodila-Tedika, Oasis; Asongu, Simplice A; Azia-Dimbu, Florentin

2017-05-01

The purpose of this study is to assess the relationship between intelligence (or human capital) and the statistical capacity of developing countries. The line of inquiry is motivated essentially by the scarce literature on poor statistics in developing countries and an evolving stream of literature on the knowledge economy. A positive association is established between intelligence quotient (IQ) and statistical capacity. The relationship is robust to alternative specifications with varying conditioning information sets and control for outliers. Policy implications are discussed.

Emotional intelligence and criminal behavior.

PubMed

Megreya, Ahmed M

2015-01-01

A large body of research links criminality to cognitive intelligence and personality traits. This study examined the link between emotional intelligence (EI) and criminal behavior. One hundred Egyptian adult male offenders who have been sentenced for theft, drug dealing or murder and 100 nonoffenders were administered the Bar-On Emotional Quotient Inventory (EQ-i). The offenders had lower levels of EI than the nonoffenders. In addition, EI varied as a function of the types of offenses. Namely, it decreased in magnitude with crime severity (lowest for murder, higher for drug dealing, and highest for theft). These results converged with the direct/ indirect aggression theory suggesting that indirect aggression requires more social intelligence than physical aggression. Forensic intervention programs should therefore include EI training, especially when violence is involved. © 2014 American Academy of Forensic Sciences.

Understanding the gap between cognitive abilities and daily living skills in adolescents with autism spectrum disorders with average intelligence.

PubMed

Duncan, Amie W; Bishop, Somer L

2015-01-01

Daily living skills standard scores on the Vineland Adaptive Behavior Scales-2nd edition were examined in 417 adolescents from the Simons Simplex Collection. All participants had at least average intelligence and a diagnosis of autism spectrum disorder. Descriptive statistics and binary logistic regressions were used to examine the prevalence and predictors of a "daily living skills deficit," defined as below average daily living skills in the context of average intelligence quotient. Approximately half of the adolescents were identified as having a daily living skills deficit. Autism symptomatology, intelligence quotient, maternal education, age, and sex accounted for only 10% of the variance in predicting a daily living skills deficit. Identifying factors associated with better or worse daily living skills may help shed light on the variability in adult outcome in individuals with autism spectrum disorder with average intelligence. © The Author(s) 2013.

Early growth patterns are associated with intelligence quotient scores in children born small-for-gestational age.

PubMed

Varella, Marcia H; Moss, William J

2015-08-01

To assess whether patterns of growth trajectory during infancy are associated with intelligence quotient (IQ) scores at 4 years of age in children born small-for-gestational age (SGA). Children in the Collaborative Perinatal Project born SGA were eligible for analysis. The primary outcome was the Stanford-Binet IQ score at 4 years of age. Growth patterns were defined based on changes in weight-for-age z-scores from birth to 4 months and 4 to 12 months of age and consisted of steady, early catch-up, late catch-up, constant catch-up, early catch-down, late catch-down, constant catch-down, early catch-up & late catch-down, and early catch-down & late catch-up. Multivariate linear regression was used to assess associations between patterns of growth and IQ. We evaluated patterns of growth and IQ in 5640 children. Compared with children with steady growth, IQ scores were 2.9 [standard deviation (SD)=0.54], 1.5 (SD=0.63), and 2.2 (SD=0.9) higher in children with early catch-up, early catch-up and later catch-down, and constant catch-up growth patterns, respectively, and 4.4 (SD=1.4) and 3.9 (SD=1.5) lower in children with early catch-down & late catch-up, and early catch-down growth patterns, respectively. Patterns in weight gain before 4 months of age were associated with differences in IQ scores at 4 years of age, with children with early catch-up having slightly higher IQ scores than children with steady growth and children with early catch-down having slightly lower IQ scores. These findings have implications for early infant nutrition in children born SGA. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Maternal fatty acids in pregnancy, FADS polymorphisms, and child intelligence quotient at 8 y of age.

PubMed

Steer, Colin D; Lattka, Eva; Koletzko, Berthold; Golding, Jean; Hibbeln, Joseph R

2013-12-01

Brain tissue is selectively enriched with highly unsaturated fatty acids (FAs). Altering the maternal FA status in pregnancy may improve fetal neural development with lasting consequences for child development. We explored whether maternal FAs in erythrocytes, either measured directly or indirectly by maternal FADS genetic variants, are associated with child intelligence quotient (IQ). Linear regression analyses, adjusted for 18 confounders, were used to investigate the associations in 2839 mother-child pairs from the population-based Avon Longitudinal Study of Parents and Children cohort. Low levels of arachidonic acid (20:4n-6) were associated with lower performance IQ (-2.0 points; 95% CI: -3.5, -0.6 points; P = 0.007, increased R² = 0.27%), high levels of osbond acid (22:5n-6) were associated with verbal IQ (-1.8 points; 95% CI: -3.2, -0.4 points; P = 0.014, R² = 0.20%), and high levels of adrenic acid (22:4n-6) were associated with verbal IQ (-1.7 points; 95% CI:-3.1, -0.3 points; P = 0.016, R² = 0.19%). There was some evidence to support a negative association of low docosahexaenoic acid (DHA; 22:6n-3) with full-scale IQ (R² = 0.15%). Novel weak associations were also observed for low levels of osbond acid (R² ≤ 0.29%) and FADS variants with opposite effects for intron variants and variants in the promoter region such as rs3834458 (R² ≤ 0.38%). These results support the positive role of maternal arachidonic acid and DHA on fetal neural development, although the effects on child IQ by 8 y of age were small (0.1 SD), with other factors contributing more substantially. The endogenous synthesis of these FAs by FADS genes, especially FADS2, may also be important. The replication of these results is recommended.

White matter integrity in dyskinetic cerebral palsy: Relationship with intelligence quotient and executive function.

PubMed

Laporta-Hoyos, Olga; Pannek, Kerstin; Ballester-Plané, Júlia; Reid, Lee B; Vázquez, Élida; Delgado, Ignacio; Zubiaurre-Elorza, Leire; Macaya, Alfons; Póo, Pilar; Meléndez-Plumed, Mar; Junqué, Carme; Boyd, Roslyn; Pueyo, Roser

2017-01-01

Dyskinetic cerebral palsy (CP) is one of the most disabling motor types of CP and has been classically associated with injury to the basal ganglia and thalamus. Although cognitive dysfunction is common in CP, there is a paucity of published quantitative analyses investigating the relationship between white matter (WM) microstructure and cognition in this CP type. This study aims (1) to compare brain WM microstructure between people with dyskinetic CP and healthy controls, (2) to identify brain regions where WM microstructure is related to intelligence and (3) to identify brain regions where WM microstructure is related to executive function in people with dyskinetic CP and (4) to identify brain regions where the correlations are different between controls and people with CP in IQ and executive functions. Thirty-three participants with dyskinetic CP (mean ± SD age: 24.42 ± 12.61, 15 female) were age and sex matched with 33 controls. Participants underwent a comprehensive neuropsychological battery to assess intelligence quotient (IQ) and four executive function domains (attentional control, cognitive flexibility, goal setting and information processing). Diffusion weighted MRI scans were acquired at 3T. Voxel-based whole brain groupwise analyses were used to compare fractional anisotropy (FA) and of the CP group to the matched controls using a general lineal model. Further general linear models were used to identify regions where white matter FA correlated with IQ and each of the executive function domains. White matter FA was significantly reduced in the CP group in all cerebral lobes, predominantly in regions connected with the parietal and to a lesser extent the temporal lobes. There was no significant correlation between IQ or any of the four executive function domains and WM microstructure in the control group. In participants with CP, lower IQ was associated with lower FA in all cerebral lobes, predominantly in locations that also showed reduced FA

Hodge numbers for all CICY quotients

NASA Astrophysics Data System (ADS)

Constantin, Andrei; Gray, James; Lukas, Andre

2017-01-01

We present a general method for computing Hodge numbers for Calabi-Yau manifolds realised as discrete quotients of complete intersections in products of projective spaces. The method relies on the computation of equivariant cohomologies and is illustrated for several explicit examples. In this way, we compute the Hodge numbers for all discrete quotients obtained in Braun's classification [1].

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Finding minimum-quotient cuts in planar graphs

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

Park, J.K.; Phillips, C.A.

Given a graph G = (V, E) where each vertex v {element_of} V is assigned a weight w(v) and each edge e {element_of} E is assigned a cost c(e), the quotient of a cut partitioning the vertices of V into sets S and {bar S} is c(S, {bar S})/min{l_brace}w(S), w(S){r_brace}, where c(S, {bar S}) is the sum of the costs of the edges crossing the cut and w(S) and w({bar S}) are the sum of the weights of the vertices in S and {bar S}, respectively. The problem of finding a cut whose quotient is minimum for a graph hasmore » in recent years attracted considerable attention, due in large part to the work of Rao and Leighton and Rao. They have shown that an algorithm (exact or approximation) for the minimum-quotient-cut problem can be used to obtain an approximation algorithm for the more famous minimumb-balanced-cut problem, which requires finding a cut (S,{bar S}) minimizing c(S,{bar S}) subject to the constraint bW {le} w(S) {le} (1 {minus} b)W, where W is the total vertex weight and b is some fixed balance in the range 0 < b {le} {1/2}. Unfortunately, the minimum-quotient-cut problem is strongly NP-hard for general graphs, and the best polynomial-time approximation algorithm known for the general problem guarantees only a cut whose quotient is at mostO(lg n) times optimal, where n is the size of the graph. However, for planar graphs, the minimum-quotient-cut problem appears more tractable, as Rao has developed several efficient approximation algorithms for the planar version of the problem capable of finding a cut whose quotient is at most some constant times optimal. In this paper, we improve Rao`s algorithms, both in terms of accuracy and speed. As our first result, we present two pseudopolynomial-time exact algorithms for the planar minimum-quotient-cut problem. As Rao`s most accurate approximation algorithm for the problem -- also a pseudopolynomial-time algorithm -- guarantees only a 1.5-times-optimal cut, our algorithms represent a significant advance.« less

Finding minimum-quotient cuts in planar graphs

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

Park, J.K.; Phillips, C.A.

Given a graph G = (V, E) where each vertex v [element of] V is assigned a weight w(v) and each edge e [element of] E is assigned a cost c(e), the quotient of a cut partitioning the vertices of V into sets S and [bar S] is c(S, [bar S])/min[l brace]w(S), w(S)[r brace], where c(S, [bar S]) is the sum of the costs of the edges crossing the cut and w(S) and w([bar S]) are the sum of the weights of the vertices in S and [bar S], respectively. The problem of finding a cut whose quotient is minimummore » for a graph has in recent years attracted considerable attention, due in large part to the work of Rao and Leighton and Rao. They have shown that an algorithm (exact or approximation) for the minimum-quotient-cut problem can be used to obtain an approximation algorithm for the more famous minimumb-balanced-cut problem, which requires finding a cut (S,[bar S]) minimizing c(S,[bar S]) subject to the constraint bW [le] w(S) [le] (1 [minus] b)W, where W is the total vertex weight and b is some fixed balance in the range 0 < b [le] [1/2]. Unfortunately, the minimum-quotient-cut problem is strongly NP-hard for general graphs, and the best polynomial-time approximation algorithm known for the general problem guarantees only a cut whose quotient is at mostO(lg n) times optimal, where n is the size of the graph. However, for planar graphs, the minimum-quotient-cut problem appears more tractable, as Rao has developed several efficient approximation algorithms for the planar version of the problem capable of finding a cut whose quotient is at most some constant times optimal. In this paper, we improve Rao's algorithms, both in terms of accuracy and speed. As our first result, we present two pseudopolynomial-time exact algorithms for the planar minimum-quotient-cut problem. As Rao's most accurate approximation algorithm for the problem -- also a pseudopolynomial-time algorithm -- guarantees only a 1.5-times-optimal cut, our algorithms represent a significant advance.« less

Intelligence quotient (IQ) in adolescence and later risk of alcohol-related hospital admissions and deaths--37-year follow-up of Swedish conscripts.

PubMed

Sjölund, Sara; Allebeck, Peter; Hemmingsson, Tomas

2012-01-01

To investigate the relationship between intelligence measured at ages 18-19 and later alcohol-related hospital admission and mortality among men, while controlling for possible confounders. Cohort study. A total of 49,321 Swedish men who were conscripted for military training in 1969-70 and followed until 2007. Intelligence quotient (IQ) measured at conscription is the exposure, while alcohol-related hospital admission and death are the two outcomes. Adjustments for following variables were made: early life circumstances [childhood socio-economic position (SEP), father's drinking], mental health, social adjustment and behavioural factors measured at age 18 (psychiatric diagnosis, contact with police and child care, low emotional control, daily smoking, risky use of alcohol) and adult social position (attained education, SEP and income at age 40). IQ had an inverse and graded association with later alcohol-related problems. For alcohol-related hospital admissions the crude hazard ratio (HR) was 1.29 (95% CI = 1.26-1.31) and for alcohol-related mortality it was 1.21 (95% CI = 1.17-1.24) for every one point decrease on the nine-point IQ scale. Adjustment for risk factors measured at age 18 attenuated the association somewhat for both outcomes. After adjustment for social position as adult, the HR was considerably lower resulting in a HR of 1.06 (95% CI = 1.02-1.10) for alcohol-related hospital admissions and 1.01 (95% CI = 0.95-1.08) for alcohol-related mortality. In Swedish men there is an association between IQ in early adulthood and later alcohol-related hospital admission and death. Social position as adult could be an important contributory factor. © 2011 The Authors, Addiction © 2011 Society for the Study of Addiction.

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Emotional Intelligence Abilities and Traits in Different Career Paths

ERIC Educational Resources Information Center

Kafetsios, Konstantinos; Maridaki-Kassotaki, Aikaterini; Zammuner, Vanda L.; Zampetakis, Leonidas A.; Vouzas, Fotios

2009-01-01

Two studies tested hypotheses about differences in emotional intelligence (EI) abilities and traits between followers of different career paths. Compared to their social science peers, science students had higher scores in adaptability and general mood traits measured with the Emotion Quotient Inventory, but lower scores in strategic EI abilities…

Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems

DTIC Science & Technology

2016-06-01

research is being done to incorporate the field of machine learning into intrusion detection. Machine learning is a branch of artificial intelligence (AI...adversarial drift." Proceedings of the 2013 ACM workshop on Artificial intelligence and security. ACM. (2013) Kantarcioglu, M., Xi, B., and Clifton, C. "A...34 Proceedings of the 4th ACM workshop on Security and artificial intelligence . ACM. (2011) Dua, S., and Du, X. Data Mining and Machine Learning in

Emotional Intelligence, Personality Traits and Career Decision Difficulties

ERIC Educational Resources Information Center

Di Fabio, Annamaria; Palazzeschi, Letizia

2009-01-01

This study aims to take an in-depth look at the role of emotional intelligence and personality traits in relation to career decision difficulties. The Italian version of the Career Decision Difficulties Questionnaire (CDDQ), the Bar-On Emotional Quotient Inventory: Short (Bar-On EQ-i: S), and the Big Five Questionnaire (BFQ) were administered to…

Emotional intelligence, risk perception in abstinent cocaine dependent individuals.

PubMed

Romero-Ayuso, Dulce; Mayoral-Gontán, Yolanda; Triviño-Juárez, José-Matías

2016-01-01

Cocaine is now responsible for the second-highest number of cessation intervention requests. In this study we analyze the different skills of emotional intelligence in cocaine- dependent patients maintaining abstinence. The Mayer- Salovey-Caruso Emotional Intelligence Test (MSCEIT) and the Balloon Analogue Risk Task (BART) were administered to 50 subjects (25 individuals with no history of drug use and 25 individuals in treatment at the Addictive Behaviors Unit in a state of withdrawal at the time of evaluation). The results showed differences between these groups in overall emotional intelligence quotient, strategic emotional intelligence, understanding emotions and emotional management. Cocaine-addicted participants showed difficulties in analyzing complex emotions and regulating their emotional response, aspects that can interfere with interactions in daily life.

What Is Artificial Intelligence Anyway?

ERIC Educational Resources Information Center

Kurzweil, Raymond

1985-01-01

Examines the past, present, and future status of Artificial Intelligence (AI). Acknowledges the limitations of AI but proposes possible areas of application and further development. Urges a concentration on the unique strengths of machine intelligence rather than a copying of human intelligence. (ML)

Comparing Chalk With Cheese-The EGG Contact Quotient Is Only a Limited Surrogate of the Closed Quotient.

PubMed

Herbst, Christian T; Schutte, Harm K; Bowling, Daniel L; Svec, Jan G

2017-07-01

The electroglottographic (EGG) contact quotient (CQegg), an estimate of the relative duration of vocal fold contact per vibratory cycle, is the most commonly used quantitative analysis parameter in EGG. The purpose of this study is to quantify the CQegg's relation to the closed quotient, a measure more directly related to glottal width changes during vocal fold vibration and the respective sound generation events. Thirteen singers (six females) phonated in four extreme phonation types while independently varying the degree of breathiness and vocal register. EGG recordings were complemented by simultaneous videokymographic (VKG) endoscopy, which allows for calculation of the VKG closed quotient (CQvkg). The CQegg was computed with five different algorithms, all used in previous research. All CQegg algorithms produced CQegg values that clearly differed from the respective CQvkg, with standard deviations around 20% of cycle duration. The difference between CQvkg and CQegg was generally greater for phonations with lower CQvkg. The largest differences were found for low-quality EGG signals with a signal-to-noise ratio below 10 dB, typically stemming from phonations with incomplete glottal closure. Disregarding those low-quality signals, we found the best match between CQegg and CQvkg for a CQegg algorithm operating on the first derivative of the EGG signal. These results show that the terms "closed quotient" and "contact quotient" should not be used interchangeably. They relate to different physiological phenomena. Phonations with incomplete glottal closure having an EGG signal-to-noise ratio below 10 dB are not suited for CQegg analysis. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Correlation among body height, intelligence, and brain gray matter volume in healthy children.

PubMed

Taki, Yasuyuki; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Asano, Michiko; Asano, Kohei; Kotozaki, Yuka; Nouchi, Rui; Wu, Kai; Fukuda, Hiroshi; Kawashima, Ryuta

2012-01-16

A significant positive correlation between height and intelligence has been demonstrated in children. Additionally, intelligence has been associated with the volume of gray matter in the brains of children. Based on these correlations, we analyzed the correlation among height, full-scale intelligence quotient (IQ) and gray matter volume applying voxel-based morphometry using data from the brain magnetic resonance images of 160 healthy children aged 5-18 years of age. As a result, body height was significantly positively correlated with brain gray matter volume. Additionally, the regional gray matter volume of several regions such as the bilateral prefrontal cortices, temporoparietal region, and cerebellum was significantly positively correlated with body height and that the gray matter volume of several of these regions was also significantly positively correlated with full-scale intelligence quotient (IQ) scores after adjusting for age, sex, and socioeconomic status. Our results demonstrate that gray and white matter volume may mediate the correlation between body height and intelligence in healthy children. Additionally, the correlations among gray and white matter volume, height, and intelligence may be at least partially explained by the effect of insulin-like growth factor-1 and growth hormones. Given the importance of the effect of environmental factors, especially nutrition, on height, IQ, and gray matter volume, the present results stress the importance of nutrition during childhood for the healthy maturation of body and brain. Copyright © 2011 Elsevier Inc. All rights reserved.

Computational Foundations of Natural Intelligence

PubMed Central

van Gerven, Marcel

2017-01-01

New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence. PMID:29375355

Science, Intelligence, and Educational Policy: The Mismeasure of Frankenstein (with Apologies to Mary Shelley and Stephen Jay Gould).

ERIC Educational Resources Information Center

Zappardino, Pamela

Stephen Jay Gould points out in "The Mismeasure of Man" (1981), "Science, since people must do it, is a socially embedded activity. It progresses by hunch, vision, and intuition." The legacy of the traditional construct of intelligence and its measurement through intelligence quotient (IQ) tests has not been educational improvement. Its legacy in…

Gender differences in self-rated and partner-rated multiple intelligences: a Portuguese replication.

PubMed

Neto, Félix; Furnham, Adrian

2006-11-01

The authors examined gender differences and the influence of intelligence quotient (IQ) test experience in the self and partner estimation of H. Gardner's (1999) 10 multiple intelligences. Portuguese students (N = 190) completed a brief questionnaire developed on the basis of an instrument used in previous research (A. Furnham, 2001). Three of the 10 self-estimates yielded significant gender differences. Men believed they were more intelligent than were women on mathematical (logical), spatial, and naturalistic intelligence. Those who had previously completed an IQ test gave higher self-estimates on 2 of the 10 estimates. Factor analysis of the 10 and then 8 self-estimated scores did not confirm Gardner's 3-factor classification of multiple intelligences in this sample.

Personality and emotional intelligence in teacher burnout.

PubMed

Pishghadam, Reza; Sahebjam, Samaneh

2012-03-01

This paper aims to investigate the relationship between teacher's personality types, emotional intelligence and burnout and to predict the burnout levels of 147 teachers in the city of Mashhad (Iran). To this end, we have used three inventories: Maslach Burnout Inventory (MBI), NEO Five Factor Inventory (NEO-FFI), and Emotional Quotient Inventory (EQ-I). We used Homogeneity Analysis and Multiple Linear Regression to analyze the data. The results exhibited a significant relationship between personality types and emotional intelligence and the three dimensions of burnout. It was indicated that the best predictors for emotional exhaustion were neuroticism and extroversion, for depersonalization were intrapersonal scale of emotional intelligence and agreeableness, and for personal accomplishment were interpersonal scale and conscientiousness. Finally, the results were discussed in the context of teacher burnout.

Electronic collaboration: Some effects of telecommunication media and machine intelligence on team performance

NASA Technical Reports Server (NTRS)

Wellens, A. Rodney

1991-01-01

Both NASA and DoD have had a long standing interest in teamwork, distributed decision making, and automation. While research on these topics has been pursued independently, it is becoming increasingly clear that the integration of social, cognitive, and human factors engineering principles will be necessary to meet the challenges of highly sophisticated scientific and military programs of the future. Images of human/intelligent-machine electronic collaboration were drawn from NASA and Air Force reports as well as from other sources. Here, areas of common concern are highlighted. A description of the author's research program testing a 'psychological distancing' model of electronic media effects and human/expert system collaboration is given.

[Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine].

PubMed

Wang, Yue; Yu, Lei; Fu, Jianming; Fang, Qiang

2014-04-01

In order to realize an individualized and specialized rehabilitation assessment of remoteness and intelligence, we set up a remote intelligent assessment system of upper limb movement function of post-stroke patients during rehabilitation. By using the remote rehabilitation training sensors and client data sampling software, we collected and uploaded the gesture data from a patient's forearm and upper arm during rehabilitation training to database of the server. Then a remote intelligent assessment system, which had been developed based on the extreme learning machine (ELM) algorithm and Brunnstrom stage assessment standard, was used to evaluate the gesture data. To evaluate the reliability of the proposed method, a group of 23 stroke patients, whose upper limb movement functions were in different recovery stages, and 4 healthy people, whose upper limb movement functions were normal, were recruited to finish the same training task. The results showed that, compared to that of the experienced rehabilitation expert who used the Brunnstrom stage standard table, the accuracy of the proposed remote Brunnstrom intelligent assessment system can reach a higher level, as 92.1%. The practical effects of surgery have proved that the proposed system could realize the intelligent assessment of upper limb movement function of post-stroke patients remotely, and it could also make the rehabilitation of the post-stroke patients at home or in a community care center possible.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Application of ARAMIS capabilities to space project functional elements

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities and their related ground support functions are studied, so that informed decisions can be made on which aspects of ARAMIS to develop. The specific tasks which will be required by future space project tasks are identified and the relative merits of these options are evaluated. The ARAMIS options defined and researched span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Decomposing self-estimates of intelligence: structure and sex differences across 12 nations.

PubMed

von Stumm, Sophie; Chamorro-Premuzic, Tomas; Furnham, Adrian

2009-05-01

This study examines the structure of self-estimates of intelligence (SEI) across 12 nations (Australia, Austria, Brazil, France, Iran, Israel, Malaysia, South Africa, Spain, Turkey, UK and US). Participants rated themselves on general and specific abilities from three popular models of intelligence: Gardner's multiple intelligences, Sternberg's triarchic theory of intelligence, and Goleman's emotional intelligence. The results showed that (a) laypeople across nations have similar and invariant concepts of intelligence, (b) concepts of intelligence are cross-culturally closely related to academic notions of intellectual ability and (c) sex differences in general and specific SEI favouring men are consistent across countries. Male hubris and female humility in SEI seem independent of sex differences in actual cognitive ability and national levels of masculinity-femininity. Furthermore, international mean differences in general SEI could not be attributed to discrepancies in national intelligence quotient (IQ) levels or to cultural variations.

The Exchangeability of Brief Intelligence Tests for Children with Intellectual Giftedness: Illuminating Error Variance Components' Influence on IQs

ERIC Educational Resources Information Center

Irby, Sarah M.; Floyd, Randy G.

2017-01-01

This study examined the exchangeability of total scores (i.e., intelligent quotients [IQs]) from three brief intelligence tests. Tests were administered to 36 children with intellectual giftedness, scored live by one set of primary examiners and later scored by a secondary examiner. For each student, six IQs were calculated, and all 216 values…

Whether the Autism Spectrum Quotient Consists of Two Different Subgroups? Cluster Analysis of the Autism Spectrum Quotient in General Population

ERIC Educational Resources Information Center

Kitazoe, Noriko; Fujita, Naofumi; Izumoto, Yuji; Terada, Shin-ichi; Hatakenaka, Yuhei

2017-01-01

The purpose of this study was to investigate whether the individuals in the general population with high scores on the Autism Spectrum Quotient constituted a single homogeneous group or not. A cohort of university students (n = 4901) was investigated by cluster analysis based on the original five subscales of the Autism Spectrum Quotient. Based on…

Needed: A Clinton Crusade for Quality and Equality.

ERIC Educational Resources Information Center

Clinchy, Evans

1993-01-01

Our present factory-model educational system, now desperately undergoing "restructuring," was never intended as a social equalizer but as a great American academic and social sorting machine. The archaic, restrictive goals of America 2000 are doomed, along with narrowly defined intelligence quotients. A more revolutionary, restructuring…

Interrogative Suggestibility among Adolescent Boys and Its Relationship with Intelligence, Memory, and Cognitive Set.

ERIC Educational Resources Information Center

Singh, Krishna K.; Gudjonsson, Gisli H.

1992-01-01

Investigated hypotheses generated by Gudjonsson and Clark model of interrogative suggestibility. Adolescent boys (n=40) completed Gudjonsson Suggestibility Scale and measures of intellectual skills, memory, field-dependence, hostility, and attitudes toward persons in authority. Suggestibility correlated negatively with intelligence quotient and…

Exploring the Emotional Intelligence of Student Leaders in the SI Context

ERIC Educational Resources Information Center

James, Cindy; Templeman, Elizabeth

2015-01-01

An exploratory study of the emotional intelligence (EI) of student leaders participating in a Supplemental Instruction (SI) program was conducted to determine whether a significant relationship exists between leadership effectiveness and EI as measured by the Bar-On Emotional Quotient Inventory (EQ-i) and to assess the impact of the leadership…

Profinite Completions of Burnside-Type Quotients of Surface Groups

NASA Astrophysics Data System (ADS)

Funar, Louis; Lochak, Pierre

2018-06-01

Using quantum representations of mapping class groups, we prove that profinite completions of Burnside-type surface group quotients are not virtually prosolvable, in general. Further, we construct infinitely many finite simple characteristic quotients of surface groups.

Composite Reliability and Standard Errors of Measurement for a Seven-Subtest Short Form of the Wechsler Adult Intelligence Scale-Revised.

ERIC Educational Resources Information Center

Schretlen, David; And Others

1994-01-01

Composite reliability and standard errors of measurement were computed for prorated Verbal, Performance, and Full-Scale intelligence quotient (IQ) scores from a seven-subtest short form of the Wechsler Adult Intelligence Scale-Revised. Results with 1,880 adults (standardization sample) indicate that this form is as reliable as the complete test.…

Brain Development Parameters and Intelligence in Chilean High School Graduates

ERIC Educational Resources Information Center

Ivanovic, Daniza M.; Leiva, Boris P.; Castro, Carmen G.; Olivares, Manuel G.; Jansana, Joan Manuel M.; Castro, Veronica G.; Almagia, Atilio Aldo F.; Toro, Triana D.; Urrutia, Maria Soledad C.; Miller, Patricio T.; Bosch, Enrique O.; Larrain, Cristian G.; Perez, Hernan T.

2004-01-01

The hypothesis that independently of sex, brain volume (BV) and head circumference (HC) are positively and significantly associated with intellectual quotient (IQ) was examined in a sample of 96 high school graduates of high [Wechsler Intelligence Scale for Adults--Revised (WAIS-R) is greater than 120] and low IQ (WAIS-R is less than 100) (1:1),…

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

PubMed

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

A cloud platform for remote diagnosis of breast cancer in mammography by fusion of machine and human intelligence

NASA Astrophysics Data System (ADS)

Jiang, Guodong; Fan, Ming; Li, Lihua

2016-03-01

Mammography is the gold standard for breast cancer screening, reducing mortality by about 30%. The application of a computer-aided detection (CAD) system to assist a single radiologist is important to further improve mammographic sensitivity for breast cancer detection. In this study, a design and realization of the prototype for remote diagnosis system in mammography based on cloud platform were proposed. To build this system, technologies were utilized including medical image information construction, cloud infrastructure and human-machine diagnosis model. Specifically, on one hand, web platform for remote diagnosis was established by J2EE web technology. Moreover, background design was realized through Hadoop open-source framework. On the other hand, storage system was built up with Hadoop distributed file system (HDFS) technology which enables users to easily develop and run on massive data application, and give full play to the advantages of cloud computing which is characterized by high efficiency, scalability and low cost. In addition, the CAD system was realized through MapReduce frame. The diagnosis module in this system implemented the algorithms of fusion of machine and human intelligence. Specifically, we combined results of diagnoses from doctors' experience and traditional CAD by using the man-machine intelligent fusion model based on Alpha-Integration and multi-agent algorithm. Finally, the applications on different levels of this system in the platform were also discussed. This diagnosis system will have great importance for the balanced health resource, lower medical expense and improvement of accuracy of diagnosis in basic medical institutes.

Borderline personality disorder and emotional intelligence.

PubMed

Peter, Mathell; Schuurmans, Hanneke; Vingerhoets, Ad J J M; Smeets, Guus; Verkoeijen, Peter; Arntz, Arnoud

2013-02-01

The present study investigated emotional intelligence (EI) in borderline personality disorder (BPD). It was hypothesized that patients with BPD (n = 61) compared with patients with other personality disorders (PDs; n = 69) and nonpatients (n = 248) would show higher scores on the ability to perceive emotions and impairments in the ability to regulate emotions. EI was assessed with the Mayer-Salovey-Caruso Emotional Intelligence Test (Mayer, Salovey, and Caruso [New York: MHS, 2002]). As compared with the PD group and the nonpatient group, the patients with BPD displayed the anticipated deficits in their ability to understand, whereas no differences emerged with respect to their ability to perceive, use, and regulate emotions. In addition, a negative relationship was found between the severity of BPD and total EI score. However, this relationship disappeared when intelligence quotient was partialled out. These results suggest that BPD is associated with emotion understanding deficits, whereas temporary severity of BPD is associated with emotion regulation deficits.

Emotional intelligence of mental health nurses.

PubMed

van Dusseldorp, Loes R L C; van Meijel, Berno K G; Derksen, Jan J L

2011-02-01

The aim of this study is to gain insight into the level of emotional intelligence of mental health nurses in the Netherlands. The focus in research on emotional intelligence to date has been on a variety of professionals. However, little is known about emotional intelligence in mental health nurses. The emotional intelligence of 98 Dutch nurses caring for psychiatric patients is reported. Data were collected with the Bar-On Emotional Quotient Inventory within a cross-sectional research design. The mean level of emotional intelligence of this sample of professionals is statistically significant higher than the emotional intelligence of the general population. Female nurses score significantly higher than men on the subscales Empathy, Social Responsibility, Interpersonal Relationship, Emotional Self-awareness, Self-Actualisation and Assertiveness. No correlations are found between years of experience and age on the one hand and emotional intelligence on the other hand. The results of this study show that nurses in psychiatric care indeed score above average in the emotional intelligence required to cope with the amount of emotional labour involved in daily mental health practice. The ascertained large range in emotional intelligence scores among the mental health nurses challenges us to investigate possible implications which higher or lower emotional intelligence levels may have on the quality of care. For instance, a possible relation between the level of emotional intelligence and the quality of the therapeutic nurse-patient relationship or the relation between the level of emotional intelligence and the manner of coping with situations characterised by a great amount of emotional labour (such as caring for patients who self-harm or are suicidal). © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

Medium wave exposure characterisation using exposure quotients.

PubMed

Paniagua, Jesús M; Rufo, Montaña; Jiménez, Antonio; Antolín, Alicia; Pinar, Iván

2010-06-01

One of the aspects considered in the International Commission on Non-Ionizing Radiation Protection guidelines is that, in situations of simultaneous exposure to fields of different frequencies, exposure quotients for thermal and electrical stimulation effects should be examined. The aim of the present work was to analyse the electromagnetic radiation levels and exposure quotients for exposure to multiple-frequency sources in the vicinity of medium wave radio broadcasting antennas. The measurements were made with a spectrum analyser and a monopole antenna. Kriging interpolation was used to prepare contour maps and to estimate the levels in the towns and villages of the zone. The results showed that the exposure quotient criterion based on electrical stimulation effects to be more stringent than those based on thermal effects or power density levels. Improvement of dosimetry evaluations requires the spectral components of the radiation to be quantified, followed by application of the criteria for exposure to multiple-frequency sources.

Emotional Intelligence and Teacher Efficacy: A Study of Turkish EFL Pre-Service Teachers

ERIC Educational Resources Information Center

Kocoglu, Zeynep

2011-01-01

This study investigated the relationship between emotional intelligence and teacher efficacy among 90 English language pre-service teachers from a university in Turkey. Data sources included Tschannen-Moran and Woolfolk-Hoy's Teachers' Sense of Efficacy Scale and Reuven Bar-On's Emotional Quotient Inventory. The findings indicated that Turkish EFL…

Social cognition in bipolar disorder: Focus on emotional intelligence.

PubMed

Varo, C; Jimenez, E; Solé, B; Bonnín, C M; Torrent, C; Valls, E; Morilla, I; Lahera, G; Martínez-Arán, A; Vieta, E; Reinares, M

2017-08-01

The present study aims to characterize emotional intelligence (EI) variability in a sample of euthymic bipolar disorder (BD) patients through the Mayer- Salovey-Caruso Emotional Intelligence Test (MSCEIT). A total of 134 euthymic BD outpatients were recruited and divided into three groups according to the total Emotional Intelligence Quotient (EIQ) score of the MSCEIT, following a statistical criterion of scores 1.5SDs above/below the normative group mean, as follows: a low performance (LP) group (EIQ <85), a normal performance (NP) group (85≤EIQ≤115), and a high performance (HP) group (EIQ >115). Afterwards, main sociodemographic, clinical, functional and neurocognitive variables were compared between the groups. Three groups were identified: 1) LP group (n=16, 12%), 2) NP group (n=93, 69%) and 3) HP group (n=25, 19%). There were significant differences between the groups in premorbid intelligence quotient (IQ) (p=0.010), axis II comorbidity (p=0.008), subthreshold depressive symptoms (p=0.027), general functioning (p=0.013) and in four specific functional domains: autonomy, occupation, interpersonal relations and leisure time. Significant differences in neurocognitive performance were found between groups with the LP group showing the lowest attainments. The cross-sectional design of the study. Our results suggest that EI variability among BD patients, assessed through MSCEIT, is lower than expected. EI could be associated with premorbid IQ, subthreshold depressive symptoms, neurocognitive performance and general functioning. The identification of different profiles of SC may help guide specific interventions for distinct patient subgroups aimed at improving social cognition, neurocognitive performance and psychosocial functioning. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.

QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION.

PubMed

Fan, Jianqing; Ke, Zheng Tracy; Liu, Han; Xia, Lucy

We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method-named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization)-for analyzing high-dimensional data. Unlike in the linear setting where Rayleigh quotient optimization coincides with classification, these two problems are very different under nonlinear settings. In this paper, we clarify this difference and show that Rayleigh quotient optimization may be of independent scientific interests. One major challenge of Rayleigh quotient optimization is that the variance of quadratic statistics involves all fourth cross-moments of predictors, which are infeasible to compute for high-dimensional applications and may accumulate too many stochastic errors. This issue is resolved by considering a family of elliptical models. Moreover, for heavy-tail distributions, robust estimates of mean vectors and covariance matrices are employed to guarantee uniform convergence in estimating non-polynomially many parameters, even though only the fourth moments are assumed. Methodologically, QUADRO is based on elliptical models which allow us to formulate the Rayleigh quotient maximization as a convex optimization problem. Computationally, we propose an efficient linearized augmented Lagrangian method to solve the constrained optimization problem. Theoretically, we provide explicit rates of convergence in terms of Rayleigh quotient under both Gaussian and general elliptical models. Thorough numerical results on both synthetic and real datasets are also provided to back up our theoretical results.

QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION

PubMed Central

Fan, Jianqing; Ke, Zheng Tracy; Liu, Han; Xia, Lucy

2016-01-01

We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method—named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization)—for analyzing high-dimensional data. Unlike in the linear setting where Rayleigh quotient optimization coincides with classification, these two problems are very different under nonlinear settings. In this paper, we clarify this difference and show that Rayleigh quotient optimization may be of independent scientific interests. One major challenge of Rayleigh quotient optimization is that the variance of quadratic statistics involves all fourth cross-moments of predictors, which are infeasible to compute for high-dimensional applications and may accumulate too many stochastic errors. This issue is resolved by considering a family of elliptical models. Moreover, for heavy-tail distributions, robust estimates of mean vectors and covariance matrices are employed to guarantee uniform convergence in estimating non-polynomially many parameters, even though only the fourth moments are assumed. Methodologically, QUADRO is based on elliptical models which allow us to formulate the Rayleigh quotient maximization as a convex optimization problem. Computationally, we propose an efficient linearized augmented Lagrangian method to solve the constrained optimization problem. Theoretically, we provide explicit rates of convergence in terms of Rayleigh quotient under both Gaussian and general elliptical models. Thorough numerical results on both synthetic and real datasets are also provided to back up our theoretical results. PMID:26778864

Adversity Quotient and Defense Mechanism of Secondary School Students

ERIC Educational Resources Information Center

Nikam, Vibhawari B.; Uplane, Megha M.

2013-01-01

The present study was conducted to explore the relationship between Adversity Quotient (AQ) and Defense Mechanism (DM) of secondary school students. The aim of the study was to ascertain relationship between Adversity Quotient and Defense mechanism i. e. Turning against object (TAO), Projection (PRO), Turning against self (TAS), Principalisation…

The Development of a Motor-Free Short-Form of the Wechsler Intelligence Scale for Children-Fifth Edition.

PubMed

Piovesana, Adina M; Harrison, Jessica L; Ducat, Jacob J

2017-12-01

This study aimed to develop a motor-free short-form of the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V) that allows clinicians to estimate the Full Scale Intelligence Quotients of youths with motor impairments. Using the reliabilities and intercorrelations of six WISC-V motor-free subtests, psychometric methodologies were applied to develop look-up tables for four Motor-free Short-form indices: Verbal Comprehension Short-form, Perceptual Reasoning Short-form, Working Memory Short-form, and a Motor-free Intelligence Quotient. Index-level discrepancy tables were developed using the same methods to allow clinicians to statistically compare visual, verbal, and working memory abilities. The short-form indices had excellent reliabilities ( r = .92-.97) comparable to the original WISC-V. This motor-free short-form of the WISC-V is a reliable alternative for the assessment of intellectual functioning in youths with motor impairments. Clinicians are provided with user-friendly look-up tables, index level discrepancy tables, and base rates, displayed similar to those in the WISC-V manuals to enable interpretation of assessment results.

[Association between intelligence development and facial expression recognition ability in children with autism spectrum disorder].

PubMed

Pan, Ning; Wu, Gui-Hua; Zhang, Ling; Zhao, Ya-Fen; Guan, Han; Xu, Cai-Juan; Jing, Jin; Jin, Yu

2017-03-01

To investigate the features of intelligence development, facial expression recognition ability, and the association between them in children with autism spectrum disorder (ASD). A total of 27 ASD children aged 6-16 years (ASD group, full intelligence quotient >70) and age- and gender-matched normally developed children (control group) were enrolled. Wechsler Intelligence Scale for Children Fourth Edition and Chinese Static Facial Expression Photos were used for intelligence evaluation and facial expression recognition test. Compared with the control group, the ASD group had significantly lower scores of full intelligence quotient, verbal comprehension index, perceptual reasoning index (PRI), processing speed index（PSI), and working memory index (WMI) (P<0.05). The ASD group also had a significantly lower overall accuracy rate of facial expression recognition and significantly lower accuracy rates of the recognition of happy, angry, sad, and frightened expressions than the control group (P<0.05). In the ASD group, the overall accuracy rate of facial expression recognition and the accuracy rates of the recognition of happy and frightened expressions were positively correlated with PRI (r=0.415, 0.455, and 0.393 respectively; P<0.05). The accuracy rate of the recognition of angry expression was positively correlated with WMI (r=0.397; P<0.05). ASD children have delayed intelligence development compared with normally developed children and impaired expression recognition ability. Perceptual reasoning and working memory abilities are positively correlated with expression recognition ability, which suggests that insufficient perceptual reasoning and working memory abilities may be important factors affecting facial expression recognition ability in ASD children.

Behavior Analysis and the Quest for Machine Intelligence.

ERIC Educational Resources Information Center

Stephens, Kenneth R.; Hutchison, William R.

1993-01-01

Discusses three approaches to building intelligent systems: artificial intelligence, neural networks, and behavior analysis. BANKET, an object-oriented software system, is explained; a commercial application of BANKET is described; and a collaborative effort between the academic and business communities for the use of BANKET is discussed.…

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Supplement, Appendix 4.3: Candidate ARAMIS Capabilities

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions, in the years 1985-2000, so that NASA may make informed decisions on which aspects of ARAMIS to develop. The study first identifies the specific tasks which will be required by future space projects. It then defines ARAMIS options which are candidates for those space project tasks, and evaluates the relative merits of these options. Finally, the study identifies promising applications of ARAMIS, and recommends specific areas for further research. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

DTIC Science & Technology

ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

Appearing smart: the impression management of intelligence, person perception accuracy, and behavior in social interaction.

PubMed

Murphy, Nora A

2007-03-01

Intelligence is an important trait that affects everyday social interaction. The present research utilized the ecological perspective of social perception to investigate the impression management of intelligence and strangers' evaluations of targets' intelligence levels. The ability to effectively portray an impression of intelligence to outside judges as well as interaction partners was appraised and the effect of impression management on the accurate judgment of intelligence was assessed. In addition, targets' behavior was studied in relation to impression management, perceived intelligence, and actual measured intelligence. Impression-managing targets appeared more intelligent to video judges but not to their interaction partner as compared to controls. The intelligence quotient (IQ) of impression-managing targets was more accurately judged than controls' IQ. Impression-managing targets displayed distinct nonverbal behavioral patterns that differed from controls. Looking while speaking was a key behavior: It significantly correlated with IQ, was successfully manipulated by impression-managing targets, and contributed to higher perceived intelligence ratings.

Experimental Realization of a Quantum Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Zhaokai; Liu, Xiaomei; Xu, Nanyang; Du, Jiangfeng

2015-04-01

The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.

The Convergence of Intelligences

NASA Astrophysics Data System (ADS)

Diederich, Joachim

Minsky (1985) argued an extraterrestrial intelligence may be similar to ours despite very different origins. ``Problem- solving'' offers evolutionary advantages and individuals who are part of a technical civilisation should have this capacity. On earth, the principles of problem-solving are the same for humans, some primates and machines based on Artificial Intelligence (AI) techniques. Intelligent systems use ``goals'' and ``sub-goals'' for problem-solving, with memories and representations of ``objects'' and ``sub-objects'' as well as knowledge of relations such as ``cause'' or ``difference.'' Some of these objects are generic and cannot easily be divided into parts. We must, therefore, assume that these objects and relations are universal, and a general property of intelligence. Minsky's arguments from 1985 are extended here. The last decade has seen the development of a general learning theory (``computational learning theory'' (CLT) or ``statistical learning theory'') which equally applies to humans, animals and machines. It is argued that basic learning laws will also apply to an evolved alien intelligence, and this includes limitations of what can be learned efficiently. An example from CLT is that the general learning problem for neural networks is intractable, i.e. it cannot be solved efficiently for all instances (it is ``NP-complete''). It is the objective of this paper to show that evolved intelligences will be constrained by general learning laws and will use task-decomposition for problem-solving. Since learning and problem-solving are core features of intelligence, it can be said that intelligences converge despite very different origins.

Development of Emotional Intelligence in First-Year Undergraduate Students in a Frontier State

ERIC Educational Resources Information Center

Leedy, Gail M.; Smith, James E.

2012-01-01

Emotional Intelligence (EI) has been defined as knowing the emotional state of self and others. Its relevance for college student development is only beginning to be researched. In the present research, the Bar-On Emotional Quotient Inventory was administered to college students at the beginning and end of a semester-long course designed…

The relationship between emotional intelligence and task-switching in temporal lobe epilepsy.

PubMed

Gul, Amara; Hussain, Imtiaz

2016-01-01

To examine the role of emotional intelligence (EI) in task-switching performance of patients with temporal lobe epilepsy (TLE). An experimental research design conducted at Sheikh Zayed Hospital, Rahim Yar Khan, Mayo and Services Hospital, Lahore, Pakistan from March 2013 to October 2014. Twenty-five patients with TLE and 25 healthy individuals from local community participated in the study. Participants completed measures of intelligence, EI, depression, anxiety, stress, and task-switching experiment. Patients and controls showed an average intelligence quotient, and normal levels of depression, anxiety, and stress. In contrast to controls, patients showed lower EI and impaired task-switching abilities. This result can be seen in the context of disintegrated white matter and cerebral connectivity in patients with TLE. Emotional intelligence was found to be a significant predictor of task-switching performance. Emotional intelligence is a potential marker of higher order cognitive functioning in patients with TLE.

Combining human and machine processes (CHAMP)

NASA Astrophysics Data System (ADS)

Sudit, Moises; Sudit, David; Hirsch, Michael

2015-05-01

Machine Reasoning and Intelligence is usually done in a vacuum, without consultation of the ultimate decision-maker. The late consideration of the human cognitive process causes some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don't push the barrier of technology and are mainly done for the comfort level of selected subject matter experts (SMEs). Our approach to combining human and machine processes (CHAMP) is based on the notion of developing optimal strategies for where, when, how, and which human intelligence should be injected within a machine reasoning and intelligence process. This combination is based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. This research addresses the following problem areas: • Providing consistency within a mission: Injection of human reasoning and intelligence within the reliability and temporal needs of a mission to attain situational awareness, impact assessment, and COA development. • Supporting the incorporation of data that is uncertain, incomplete, imprecise and contradictory (UIIC): Development of mathematical models to suggest the insertion of a cognitive process within a machine reasoning and intelligent system so as to minimize UIIC concerns. • Developing systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors.

The effect of life skills training on emotional intelligence of the medical sciences students in iran.

PubMed

Lolaty, Hamideh A; Ghahari, Sharbanoo; Tirgari, Abdolhakim; Fard, Jabbar Heydari

2012-10-01

Emotional intelligence has a major role in mental health and life skills training, and could be viewed as a bridge relating to emotional intelligence and mental health. The present study is aimed at determining the effect of life skills training on the emotional intelligence among the first year students of Mazandaran University of Medical Sciences. MATERIALS AND METHODS: IN THIS EXPERIMENTAL STUDY, THE SUBJECTS WERE SELECTED BY RANDOM SAMPLING AND ALLOCATED INTO TWO GROUPS: Case group (n=20) and control group (n=19); they matched for gender, experience of stressful life events in the past six months, level of interest in the field of study, and level of emotional intelligence. The two groups responded to Bar-on Emotional Quotient Inventory before starting the experiment. Subsequently, the case group underwent life skills training. After the training, Bar-on Emotional Quotient Inventory was responded by the case and control groups again. The data was analyzed using descriptive statistics including Chi-square test, paired and independent t-tests, using SPSS software version 15. In the case group, the scores of emotional intelligence after life skills training were significantly improved (t=11.703 df=19 P=0.001), while no significant difference was observed in the control group (t=0.683 df =18 P=0.503). By performing programs such as life skills training, the levels of emotional intelligence of the students could be increased, which itself could lead to academic success, reduced substance abuse, and increased stress tolerance in the students.

What is your hospitality quotient?

PubMed

DeSilets, Lyn

2015-03-01

In addition to the behind-the-scenes work involved with planning and implementing continuing nursing education activities, there are additional ways we can enhance the learner's experience. This article presents ideas on how to improve your hospitality quotient. Copyright 2015, SLACK Incorporated.

The effect of cochlear implantation in development of intelligence quotient of 6-9 deaf children in comparison with normal hearing children (Iran, 2009-2011).

PubMed

Hashemi, Seyed Basir; Monshizadeh, Leila

2012-06-01

Before the introduction of cochlear implant (CI) in 1980, hearing aids were the only means by which profoundly deaf children had access to auditory stimuli. Nowadays, CI is firmly established as effective option in speech and language rehabilitation of deaf children, but much of the literature regarding outcomes for children after CI are focused on development of speech and less is known about language acquisition. So, the main aim of this study is the evaluation of verbal intelligence quotient (IQ) of cochlear implanted children in comparison with normal children. 30 cochlear implanted and 30 normal hearing children with similar socio-economic level at the same age were compared by a revised version (in Persian) of WISC test (Wechsler, 1991). Then the data were analyzed through SPSS software 16. In spite of the fact that cochlear implanted children did well in different parameters of WISC test, the average scores of this group was less than normal hearing children. But in similarities (one of the parameters of WISC test) 2 group's performance was approximately the same. CI plays an important role in development of verbal IQ and language acquisition of deaf children. Different researches indicate that most of the cochlear implanted children show less language delay during the time. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

[The intelligence quotient and malnutrition. Iron deficiency and the lead concentration as confusing variables].

PubMed

Vega-Franco, L; Mejía, A M; Robles, B; Moreno, L; Pérez, Y

1991-11-01

This study gave us the opportunity to know the roles iron deficiency and the presence of lead in blood play, as confounding variables, in relation to the state of malnutrition and the intellect of those children. A sample of 169 school children were classified according to their state of nutrition, their condition in reference to serum iron and lead concentrations. In addition, their intelligence was evaluated. The results confirmed that those children with lower weights and heights registered lesser points of intelligence; in fact, iron deficiency cancels out the difference in favor of those taller and weighing more. Lead did not contribute as a confounding variable, but more than half of the children showed possible toxic levels of this metal.

A hardware/software environment to support R D in intelligent machines and mobile robotic systems

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

Mann, R.C.

1990-01-01

The Center for Engineering Systems Advanced Research (CESAR) serves as a focal point at the Oak Ridge National Laboratory (ORNL) for basic and applied research in intelligent machines. R D at CESAR addresses issues related to autonomous systems, unstructured (i.e. incompletely known) operational environments, and multiple performing agents. Two mobile robot prototypes (HERMIES-IIB and HERMIES-III) are being used to test new developments in several robot component technologies. This paper briefly introduces the computing environment at CESAR which includes three hypercube concurrent computers (two on-board the mobile robots), a graphics workstation, VAX, and multiple VME-based systems (several on-board the mobile robots).more » The current software environment at CESAR is intended to satisfy several goals, e.g.: code portability, re-usability in different experimental scenarios, modularity, concurrent computer hardware transparent to applications programmer, future support for multiple mobile robots, support human-machine interface modules, and support for integration of software from other, geographically disparate laboratories with different hardware set-ups. 6 refs., 1 fig.« less

Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer, Iran

NASA Astrophysics Data System (ADS)

Fijani, Elham; Nadiri, Ata Allah; Asghari Moghaddam, Asghar; Tsai, Frank T.-C.; Dixon, Barnali

2013-10-01

Contamination of wells with nitrate-N (NO3-N) poses various threats to human health. Contamination of groundwater is a complex process and full of uncertainty in regional scale. Development of an integrative vulnerability assessment methodology can be useful to effectively manage (including prioritization of limited resource allocation to monitor high risk areas) and protect this valuable freshwater source. This study introduces a supervised committee machine with artificial intelligence (SCMAI) model to improve the DRASTIC method for groundwater vulnerability assessment for the Maragheh-Bonab plain aquifer in Iran. Four different AI models are considered in the SCMAI model, whose input is the DRASTIC parameters. The SCMAI model improves the committee machine artificial intelligence (CMAI) model by replacing the linear combination in the CMAI with a nonlinear supervised ANN framework. To calibrate the AI models, NO3-N concentration data are divided in two datasets for the training and validation purposes. The target value of the AI models in the training step is the corrected vulnerability indices that relate to the first NO3-N concentration dataset. After model training, the AI models are verified by the second NO3-N concentration dataset. The results show that the four AI models are able to improve the DRASTIC method. Since the best AI model performance is not dominant, the SCMAI model is considered to combine the advantages of individual AI models to achieve the optimal performance. The SCMAI method re-predicts the groundwater vulnerability based on the different AI model prediction values. The results show that the SCMAI outperforms individual AI models and committee machine with artificial intelligence (CMAI) model. The SCMAI model ensures that no water well with high NO3-N levels would be classified as low risk and vice versa. The study concludes that the SCMAI model is an effective model to improve the DRASTIC model and provides a confident estimate of the

Intelligence: Real or artificial?

PubMed Central

Schlinger, Henry D.

1992-01-01

Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selectionist model instead. Such a strategic change should lead them to the science of behavior analysis. PMID:22477051

A systematic approach to the application of Automation, Robotics, and Machine Intelligence Systems /ARAMIS/ to future space projects

NASA Technical Reports Server (NTRS)

Smith, D. B. S.

1982-01-01

The potential applications of Automation, Robotics, and Machine Intelligence Systems (ARAMIS) to space projects are investigated, through a systematic method. In this method selected space projects are broken down into space project tasks, and 69 of these tasks are selected for study. Candidate ARAMIS options are defined for each task. The relative merits of these options are evaluated according to seven indices of performance. Logical sequences of ARAMIS development are also defined. Based on this data, promising applications of ARAMIS are

Combining Human and Machine Intelligence to Derive Agents' Behavioral Rules for Groundwater Irrigation

NASA Astrophysics Data System (ADS)

Hu, Y.; Quinn, C.; Cai, X.

2015-12-01

One major challenge of agent-based modeling is to derive agents' behavioral rules due to behavioral uncertainty and data scarcity. This study proposes a new approach to combine a data-driven modeling based on the directed information (i.e., machine intelligence) with expert domain knowledge (i.e., human intelligence) to derive the behavioral rules of agents considering behavioral uncertainty. A directed information graph algorithm is applied to identifying the causal relationships between agents' decisions (i.e., groundwater irrigation depth) and time-series of environmental, socio-economical and institutional factors. A case study is conducted for the High Plains aquifer hydrological observatory (HO) area, U.S. Preliminary results show that four factors, corn price (CP), underlying groundwater level (GWL), monthly mean temperature (T) and precipitation (P) have causal influences on agents' decisions on groundwater irrigation depth (GWID) to various extents. Based on the similarity of the directed information graph for each agent, five clusters of graphs are further identified to represent all the agents' behaviors in the study area as shown in Figure 1. Using these five representative graphs, agents' monthly optimal groundwater pumping rates are derived through the probabilistic inference. Such data-driven relationships and probabilistic quantifications are then coupled with a physically-based groundwater model to investigate the interactions between agents' pumping behaviors and the underlying groundwater system in the context of coupled human and natural systems.

Organizational Justice: Personality Traits or Emotional Intelligence? An Empirical Study in an Italian Hospital Context

ERIC Educational Resources Information Center

Di Fabio, Annamaria; Palazzeschi, Letizia

2012-01-01

The purpose of this study was to investigate the role of personality traits and emotional intelligence in relation to organizational justice. The Organizational Justice Scale, the Eysenck Personality Questionnaire-Revised Short Form, and the Bar-On Emotional Quotient Inventory were administered to 384 Italian nurses. The emotional intelligence…

Emotional intelligence and related factors in medical sciences students of an Iranian university.

PubMed

Lolaty, Hamideh Azimi; Tirgari, Abdolhakim; Fard, Jabbar Heydari

2014-03-01

Emotional intelligence has evolved lot of interest in a variety of fields. The aim of this study was to determine the emotional intelligence and its related factors among junior medical sciences students. The research design was a descriptive - analytic analysis. Based on a census sampling method, the emotional intelligence of 322 junior medical sciences students was evaluated using the Bar-On Emotional Quotient Inventory. This study was done from 2008 to 2009 in the Mazandaran University of Medical Sciences. The findings showed that 48.1% and 22.4% of students had effective functioning and enhanced skills in emotional intelligence, respectively, while 29.5% of them needed some interventions in order to enhance the emotional intelligence. The study revealed that the students required intervention in every composite of emotional intelligence. In addition, emotional intelligence was correlated with gender, psychiatric history of the student and his/her family, experience of stressful life events, interest in the field of study, grade of study, and marital status. The results of the present study have shown that the students need some interventions to improve their emotional intelligence.

Relationship Between Dental Fluorosis and Intelligence Quotient of School Going Children In and Around Lucknow District: A Cross-Sectional Study.

PubMed

Khan, Suleman Abbas; Singh, Rahul Kumar; Navit, Saumya; Chadha, Dheera; Johri, Nikita; Navit, Pragati; Sharma, Anshul; Bahuguna, Rachana

2015-11-01

Fluoridation of drinking water, despite being regarded as one of the top ten public health achievements of the twentieth century, has remained a much debated concept. Various studies on animals and aborted human fetuses have confirmed that excessive fluoride intake during infancy and early childhood, causes a number of irreversible structural and functional changes in the CNS leading to memory, learning and intellectual deficits. To compare the IQ levels of school children of two different locations, having different fluoride levels in water, and to establish a relationship between fluoride levels, prevalence of fluorosis and its effect on IQ levels. A cross-sectional study was conducted among 429 children aged 6 - 12 years, selected by stratified random sampling from two different areas with different levels of fluoride in drinking water in and around Lucknow district. Dental fluorosis was measured using Dean's Fluorosis Index. Intelligence Quotient was measured using Raven's Coloured Progressive Matrices (1998 edition). Majority of the fluorosis free children (76.3%) had an IQ grade 2 (definitely above the average). Majority of the children suffering from very mild and mild dental fluorosis were found to have IQ grade 3 (Intellectually average). Children with moderate cases of dental fluorosis were found to have IQ grade 4 (Definitely below average). Only 5 children with severe fluorosis were included in the study and they all were found to have an IQ grade 5. Hence, a trend of increase in the IQ grade (decrease in intellectual capacity) was observed indicating a strong correlation between fluorosis grade and IQ grade. Findings of this study suggest that the overall IQ of the children exposed to high fluoride levels in drinking water and hence suffering from dental fluorosis were significantly lower than those of the low fluoride area.

Artificial Intelligence in Cardiology.

PubMed

Johnson, Kipp W; Torres Soto, Jessica; Glicksberg, Benjamin S; Shameer, Khader; Miotto, Riccardo; Ali, Mohsin; Ashley, Euan; Dudley, Joel T

2018-06-12

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

An Intelligent and Interactive Simulation and Tutoring Environment for Exploring and Learning Simple Machines

NASA Astrophysics Data System (ADS)

Myneni, Lakshman Sundeep

Students in middle school science classes have difficulty mastering physics concepts such as energy and work, taught in the context of simple machines. Moreover, students' naive conceptions of physics often remain unchanged after completing a science class. To address this problem, I developed an intelligent tutoring system, called the Virtual Physics System (ViPS), which coaches students through problem solving with one class of simple machines, pulley systems. The tutor uses a unique cognitive based approach to teaching simple machines, and includes innovations in three areas. (1) It employs a teaching strategy that focuses on highlighting links among concepts of the domain that are essential for conceptual understanding yet are seldom learned by students. (2) Concepts are taught through a combination of effective human tutoring techniques (e.g., hinting) and simulations. (3) For each student, the system identifies which misconceptions he or she has, from a common set of student misconceptions gathered from domain experts, and tailors tutoring to match the correct line of scientific reasoning regarding the misconceptions. ViPS was implemented as a platform on which students can design and simulate pulley system experiments, integrated with a constraint-based tutor that intervenes when students make errors during problem solving to teach them and to help them. ViPS has a web-based client-server architecture, and has been implemented using Java technologies. ViPS is different from existing physics simulations and tutoring systems due to several original features. (1). It is the first system to integrate a simulation based virtual experimentation platform with an intelligent tutoring component. (2) It uses a novel approach, based on Bayesian networks, to help students construct correct pulley systems for experimental simulation. (3) It identifies student misconceptions based on a novel decision tree applied to student pretest scores, and tailors tutoring to

The Role of Personality Traits, Core Self-Evaluation, and Emotional Intelligence in Career Decision-Making Difficulties

ERIC Educational Resources Information Center

Di Fabio, Annamaria; Palazzeschi, Letizia; Bar-On, Reuven

2012-01-01

This study examines the role of personality traits, core self-evaluation, and emotional intelligence (EI) in career decision-making difficulties. Italian university students (N = 232) responded to questions on the Big Five Questionnaire, Core Self-Evaluation Scale, Bar-On Emotional Quotient Inventory, and Career Decision-Making Difficulties…

Emotional Intelligence and Beliefs about Children, Discipline and Classroom Practices among Pre-Service Teachers

ERIC Educational Resources Information Center

Flanagan, Maryclare E.

2009-01-01

This research sought to explore how emotional intelligence (EI) shapes the beliefs of pre-service teachers with respect to issues such as classroom management and student behavior. 101 pre-service teachers were recruited from undergraduate and graduate education courses at a private, mid-sized university. The Emotional Quotient Inventory (EQ-i),…

Quasi-hamiltonian quotients as disjoint unions of symplectic manifolds

NASA Astrophysics Data System (ADS)

Schaffhauser, Florent

2007-08-01

The main result of this paper is Theorem 2.12 which says that the quotient μ-1({1})/U associated to a quasi-hamiltonian space (M, ω, μ: M → U) has a symplectic structure even when 1 is not a regular value of the momentum map μ. Namely, it is a disjoint union of symplectic manifolds of possibly different dimensions, which generalizes the result of Alekseev, Malkin and Meinrenken in [AMM98]. We illustrate this theorem with the example of representation spaces of surface groups. As an intermediary step, we give a new class of examples of quasi-hamiltonian spaces: the isotropy submanifold MK whose points are the points of M with isotropy group K ⊂ U. The notion of quasi-hamiltonian space was introduced by Alekseev, Malkin and Meinrenken in their paper [AMM98]. The main motivation for it was the existence, under some regularity assumptions, of a symplectic structure on the associated quasi-hamiltonian quotient. Throughout their paper, the analogy with usual hamiltonian spaces is often used as a guiding principle, replacing Lie-algebra-valued momentum maps with Lie-group-valued momentum maps. In the hamiltonian setting, when the usual regularity assumptions on the group action or the momentum map are dropped, Lerman and Sjamaar showed in [LS91] that the quotient associated to a hamiltonian space carries a stratified symplectic structure. In particular, this quotient space is a disjoint union of symplectic manifolds. In this paper, we prove an analogous result for quasi-hamiltonian quotients. More precisely, we show that for any quasi-hamiltonian space (M, ω, μ: M → U), the associated quotient M//U := μ-1({1})/U is a disjoint union of symplectic manifolds (Theorem 2.12): [ mu^{-1}(\\{1\\})/U = bigsqcup_{jin J} (mu^{-1}(\\{1\\})\\cap M_{K_j})/L_{K_j} . ] Here Kj denotes a closed subgroup of U and MKj denotes the isotropy submanifold of type Kj: MKj = {x ∈ M | Ux = Kj}. Finally, LKj is the quotient group LKj = { N

[Some aspects of measurement by Wechsler Intelligence Scale in the context of mental retardation diagnosis in adults].

PubMed

Bak, O

2001-01-01

The paper deals with the problem of diagnosing mental retardation in adults, with a focus on the numerical indicator of intellectual level (intelligence quotient). According to the present standards, the process of diagnosing mental retardation demands a comprehensive approach: an evaluation of both the intellectual level and social competence, and should be carried out by a team of experts (medical consultants, a psychologist, an educationist, etc.). The paper shows that, in the process of diagnosing, excessive attention should not be paid to the indicator of intellectual level, expressed by the intelligence quotient. According to the theory of psychological testing, this indicator is saddled with some error. Its use as the main criterion in diagnosing can lead to drawing false conclusions. The paper presents the Wechsler Adults Intelligence Scale WAIS-R(PL), which is the most common tool used by Polish psychologists in diagnosing the intellectual level. It also indicates the limitations and strong points of this test in the process of diagnosing mental retardation. The article is mainly addressed to medical doctors, as it contains information relating to the problem of measurement by tests, which is scantily dealt with during the medical studies.

Brief Report: Development of the Adolescent Empathy and Systemizing Quotients

ERIC Educational Resources Information Center

Auyeung, Bonnie; Allison, Carrie; Wheelwright, Sally; Baron-Cohen, Simon

2012-01-01

Adolescent versions of the Empathy Quotient (EQ) and Systemizing Quotient (SQ) were developed and administered to n = 1,030 parents of typically developing adolescents, aged 12-16 years. Both measures showed good test-retest reliability and high internal consistency. Girls scored significantly higher on the EQ, and boys scored significantly higher…

Machine Translation in Post-Contemporary Era

ERIC Educational Resources Information Center

Lin, Grace Hui Chin

2010-01-01

This article focusing on translating techniques via personal computer or laptop reports updated artificial intelligence progresses before 2010. Based on interpretations and information for field of MT [Machine Translation] by Yorick Wilks' book, "Machine Translation, Its scope and limits," this paper displays understandable theoretical frameworks…

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Artificial intelligence approaches for rational drug design and discovery.

PubMed

Duch, Włodzisław; Swaminathan, Karthikeyan; Meller, Jarosław

2007-01-01

Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.

A study on different forms of intelligence in Indian school-going children.

PubMed

Singh, Yashpal; Makharia, Archita; Sharma, Abhilasha; Agrawal, Kruti; Varma, Gowtham; Yadav, Tarun

2017-01-01

Most definitions of intelligence focus on capabilities that are relevant to scholastic performances. However, there are seven forms of intelligences. There is a lack of data on multiple intelligences in Indian children. Hence, this study was conducted to assess different forms of intelligences in students and compared these diverse intelligences with intelligence quotient (IQ) scores. In this cross-sectional observational study, we recruited 1065 school children between the age of 12 and 16 years from two government and 13 private schools in five towns, six cities, and two villages across India. All the children were administered multiple intelligences questionnaire by Armstrong, consisting of thirty true/false types of questions to assess the intelligences of a child in seven domains including linguistic skills, logical/mathematical abilities, musical skills, spatial intelligence, bodily-kinesthetic skills, intrapersonal intelligence, and interpersonal intelligence. IQ scores were assessed by Ravens Standard Progressive Matrices. We found that different students possessed different forms of intelligences and most students had more than one forms of intelligence. Of seven forms of intelligence, only three forms of intelligence such as logical/mathematical, musical, and spatial were positively correlated with the IQ score. Even in the children with low IQ, many students had other forms of intelligences. The IQ scores correlated with only logical/mathematical, spatial, and musical intelligence. Hence, tapping the intelligences of students can help enhance their learning process. Our curriculum should have an amalgamation of teaching for all kinds of intelligences for maximum productivity.

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human-Machine Interaction.

PubMed

Cao, Ran; Pu, Xianjie; Du, Xinyu; Yang, Wei; Wang, Jiaona; Guo, Hengyu; Zhao, Shuyu; Yuan, Zuqing; Zhang, Chi; Li, Congju; Wang, Zhong Lin

2018-05-22

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human-machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human-machine interfacing.

Role of test motivation in intelligence testing.

PubMed

Duckworth, Angela Lee; Quinn, Patrick D; Lynam, Donald R; Loeber, Rolf; Stouthamer-Loeber, Magda

2011-05-10

Intelligence tests are widely assumed to measure maximal intellectual performance, and predictive associations between intelligence quotient (IQ) scores and later-life outcomes are typically interpreted as unbiased estimates of the effect of intellectual ability on academic, professional, and social life outcomes. The current investigation critically examines these assumptions and finds evidence against both. First, we examined whether motivation is less than maximal on intelligence tests administered in the context of low-stakes research situations. Specifically, we completed a meta-analysis of random-assignment experiments testing the effects of material incentives on intelligence-test performance on a collective 2,008 participants. Incentives increased IQ scores by an average of 0.64 SD, with larger effects for individuals with lower baseline IQ scores. Second, we tested whether individual differences in motivation during IQ testing can spuriously inflate the predictive validity of intelligence for life outcomes. Trained observers rated test motivation among 251 adolescent boys completing intelligence tests using a 15-min "thin-slice" video sample. IQ score predicted life outcomes, including academic performance in adolescence and criminal convictions, employment, and years of education in early adulthood. After adjusting for the influence of test motivation, however, the predictive validity of intelligence for life outcomes was significantly diminished, particularly for nonacademic outcomes. Collectively, our findings suggest that, under low-stakes research conditions, some individuals try harder than others, and, in this context, test motivation can act as a third-variable confound that inflates estimates of the predictive validity of intelligence for life outcomes.

Role of test motivation in intelligence testing

PubMed Central

Duckworth, Angela Lee; Quinn, Patrick D.; Lynam, Donald R.; Loeber, Rolf; Stouthamer-Loeber, Magda

2011-01-01

Intelligence tests are widely assumed to measure maximal intellectual performance, and predictive associations between intelligence quotient (IQ) scores and later-life outcomes are typically interpreted as unbiased estimates of the effect of intellectual ability on academic, professional, and social life outcomes. The current investigation critically examines these assumptions and finds evidence against both. First, we examined whether motivation is less than maximal on intelligence tests administered in the context of low-stakes research situations. Specifically, we completed a meta-analysis of random-assignment experiments testing the effects of material incentives on intelligence-test performance on a collective 2,008 participants. Incentives increased IQ scores by an average of 0.64 SD, with larger effects for individuals with lower baseline IQ scores. Second, we tested whether individual differences in motivation during IQ testing can spuriously inflate the predictive validity of intelligence for life outcomes. Trained observers rated test motivation among 251 adolescent boys completing intelligence tests using a 15-min “thin-slice” video sample. IQ score predicted life outcomes, including academic performance in adolescence and criminal convictions, employment, and years of education in early adulthood. After adjusting for the influence of test motivation, however, the predictive validity of intelligence for life outcomes was significantly diminished, particularly for nonacademic outcomes. Collectively, our findings suggest that, under low-stakes research conditions, some individuals try harder than others, and, in this context, test motivation can act as a third-variable confound that inflates estimates of the predictive validity of intelligence for life outcomes. PMID:21518867

Criminal thinking styles and emotional intelligence in Egyptian offenders.

PubMed

Megreya, Ahmed M

2013-02-01

The Psychological Inventory of Criminal Thinking Styles (PICTS) has been applied extensively to the study of criminal behaviour and cognition. Increasingly growing evidence indicates that criminal thinking styles vary considerably among individuals, and these individual variations appear to be crucial for a full understanding of criminal behaviour. This study aimed to examine individual differences in criminal thinking as a function of emotional intelligence. A group of 56 Egyptian male prisoners completed the PICTS and Bar-On Emotional Quotient Inventory (EQ-i). The correlations between these assessments were examined using a series of Pearson correlations coefficients, with Bonferroni correction. General criminal thinking, reactive criminal thinking and five criminal thinking styles (mollification, cutoff, power orientation, cognitive indolence and discontinuity) negatively correlated with emotional intelligence. On the other hand, proactive criminal thinking and three criminal thinking styles (entitlement, superoptimism and sentimentality) did not associate with emotional intelligence. Emotional intelligence is an important correlate of individual differences in criminal thinking, especially its reactive aspects. Practical implications of this suggestion were discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness.

PubMed

Amirian, Mohammad-Elyas; Fazilat-Pour, Masoud

2016-08-01

The present study examined simple and multivariate relationships of spiritual intelligence with general health and happiness. The employed method was descriptive and correlational. King's Spiritual Quotient scales, GHQ-28 and Oxford Happiness Inventory, are filled out by a sample consisted of 384 students, which were selected using stratified random sampling from the students of Shahid Bahonar University of Kerman. Data are subjected to descriptive and inferential statistics including correlations and multivariate regressions. Bivariate correlations support positive and significant predictive value of spiritual intelligence toward general health and happiness. Further analysis showed that among the Spiritual Intelligence' subscales, Existential Critical Thinking Predicted General Health and Happiness, reversely. In addition, happiness was positively predicted by generation of personal meaning and transcendental awareness. The findings are discussed in line with the previous studies and the relevant theoretical background.

Unravelling the Complex Associations between Emotional Intelligence and Personality in Later Childhood and Early Adolescence

ERIC Educational Resources Information Center

Séguin, Daniel G.; Hipson, Will

2016-01-01

The primary goal of the study was to examine the relationships between emotional intelligence and personality type in later childhood. Eighty-one youth in grades seven and nine (M[subscript age]=12.49 years, SD[subscript age]=1.20 years) were asked to complete the "Bar-On Emotional Quotient Inventory: Youth Version" and the…

Artificial Intelligence/Robotics Applications to Navy Aircraft Maintenance.

DTIC Science & Technology

1984-06-01

other automatic machinery such as presses, molding machines , and numerically-controlled machine tools, just as people do. A-36...Robotics Technologies 3 B. Relevant AI Technologies 4 1. Expert Systems 4 2. Automatic Planning 4 3. Natural Language 5 4. Machine Vision...building machines that imitate human behavior. Artificial intelligence is concerned with the functions of the brain, whereas robotics include, in

Artificial Intelligence: Threat or Boon to Radiologists?

PubMed

Recht, Michael; Bryan, R Nick

2017-11-01

The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Intelligence deficits in Chinese patients with brain tumor: the impact of tumor resection.

PubMed

Shen, Chao; Xie, Rong; Cao, Xiaoyun; Bao, Weimin; Yang, Bojie; Mao, Ying; Gao, Chao

2013-01-01

Intelligence is much important for brain tumor patients after their operation, while the reports about surgical related intelligence deficits are not frequent. It is not only theoretically important but also meaningful for clinical practice. Wechsler Adult Intelligence Scale was employed to evaluate the intelligence of 103 patients with intracranial tumor and to compare the intelligence quotient (IQ), verbal IQ (VIQ), and performance IQ (PIQ) between the intracerebral and extracerebral subgroups. Although preoperative intelligence deficits appeared in all subgroups, IQ, VIQ, and PIQ were not found to have any significant difference between the intracerebral and extracerebral subgroups, but with VIQ lower than PIQ in all the subgroups. An immediate postoperative follow-up demonstrated a decline of IQ and PIQ in the extracerebral subgroup, but an improvement of VIQ in the right intracerebral subgroup. Pituitary adenoma resection exerted no effect on intelligence. In addition, age, years of education, and tumor size were found to play important roles. Brain tumors will impair IQ, VIQ, and PIQ. The extracerebral tumor resection can deteriorate IQ and PIQ. However, right intracerebral tumor resection is beneficial to VIQ, and transsphenoidal pituitary adenoma resection performs no effect on intelligence.

Simulation research on the process of large scale ship plane segmentation intelligent workshop

NASA Astrophysics Data System (ADS)

Xu, Peng; Liao, Liangchuang; Zhou, Chao; Xue, Rui; Fu, Wei

2017-04-01

Large scale ship plane segmentation intelligent workshop is a new thing, and there is no research work in related fields at home and abroad. The mode of production should be transformed by the existing industry 2.0 or part of industry 3.0, also transformed from "human brain analysis and judgment + machine manufacturing" to "machine analysis and judgment + machine manufacturing". In this transforming process, there are a great deal of tasks need to be determined on the aspects of management and technology, such as workshop structure evolution, development of intelligent equipment and changes in business model. Along with them is the reformation of the whole workshop. Process simulation in this project would verify general layout and process flow of large scale ship plane section intelligent workshop, also would analyze intelligent workshop working efficiency, which is significant to the next step of the transformation of plane segmentation intelligent workshop.

Vision Guided Intelligent Robot Design And Experiments

NASA Astrophysics Data System (ADS)

Slutzky, G. D.; Hall, E. L.

1988-02-01

The concept of an intelligent robot is an important topic combining sensors, manipulators, and artificial intelligence to design a useful machine. Vision systems, tactile sensors, proximity switches and other sensors provide the elements necessary for simple game playing as well as industrial applications. These sensors permit adaption to a changing environment. The AI techniques permit advanced forms of decision making, adaptive responses, and learning while the manipulator provides the ability to perform various tasks. Computer languages such as LISP and OPS5, have been utilized to achieve expert systems approaches in solving real world problems. The purpose of this paper is to describe several examples of visually guided intelligent robots including both stationary and mobile robots. Demonstrations will be presented of a system for constructing and solving a popular peg game, a robot lawn mower, and a box stacking robot. The experience gained from these and other systems provide insight into what may be realistically expected from the next generation of intelligent machines.

A study on different forms of intelligence in Indian school-going children

PubMed Central

Singh, Yashpal; Makharia, Archita; Sharma, Abhilasha; Agrawal, Kruti; Varma, Gowtham; Yadav, Tarun

2017-01-01

Introduction: Most definitions of intelligence focus on capabilities that are relevant to scholastic performances. However, there are seven forms of intelligences. There is a lack of data on multiple intelligences in Indian children. Hence, this study was conducted to assess different forms of intelligences in students and compared these diverse intelligences with intelligence quotient (IQ) scores. Materials and Methods: In this cross-sectional observational study, we recruited 1065 school children between the age of 12 and 16 years from two government and 13 private schools in five towns, six cities, and two villages across India. All the children were administered multiple intelligences questionnaire by Armstrong, consisting of thirty true/false types of questions to assess the intelligences of a child in seven domains including linguistic skills, logical/mathematical abilities, musical skills, spatial intelligence, bodily-kinesthetic skills, intrapersonal intelligence, and interpersonal intelligence. IQ scores were assessed by Ravens Standard Progressive Matrices. Results: We found that different students possessed different forms of intelligences and most students had more than one forms of intelligence. Of seven forms of intelligence, only three forms of intelligence such as logical/mathematical, musical, and spatial were positively correlated with the IQ score. Conclusions: Even in the children with low IQ, many students had other forms of intelligences. The IQ scores correlated with only logical/mathematical, spatial, and musical intelligence. Hence, tapping the intelligences of students can help enhance their learning process. Our curriculum should have an amalgamation of teaching for all kinds of intelligences for maximum productivity. PMID:29456325

Machine learning & artificial intelligence in the quantum domain: a review of recent progress

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Briegel, Hans J.

2018-07-01

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research—quantum information versus machine learning (ML) and artificial intelligence (AI)—have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our ‘big data’ world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement—exploring what ML/AI can do for quantum physics and vice versa—researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent

Machine learning & artificial intelligence in the quantum domain: a review of recent progress.

PubMed

Dunjko, Vedran; Briegel, Hans J

2018-07-01

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and

Intelligent fault-tolerant controllers

NASA Technical Reports Server (NTRS)

Huang, Chien Y.

1987-01-01

A system with fault tolerant controls is one that can detect, isolate, and estimate failures and perform necessary control reconfiguration based on this new information. Artificial intelligence (AI) is concerned with semantic processing, and it has evolved to include the topics of expert systems and machine learning. This research represents an attempt to apply AI to fault tolerant controls, hence, the name intelligent fault tolerant control (IFTC). A generic solution to the problem is sought, providing a system based on logic in addition to analytical tools, and offering machine learning capabilities. The advantages are that redundant system specific algorithms are no longer needed, that reasonableness is used to quickly choose the correct control strategy, and that the system can adapt to new situations by learning about its effects on system dynamics.

Emotional Intelligence and Adaptive Success of Nurses Caring for People with Mental Retardation and Severe Behavior Problems

ERIC Educational Resources Information Center

Gerits, Linda; Derksen, Jan J. L.; Verbruggen, Antoine B.

2004-01-01

The emotional intelligence profiles, gender differences, and adaptive success of 380 Dutch nurses caring for people with mental retardation and accompanying severe behavior problems are reported. Data were collected with the Bar-On Emotional Quotient Inventory, Utrecht-Coping List, Utrecht-Burnout Scale, MMPI-2, and GAMA. Absence due to illness…

Human-like machines: Transparency and comprehensibility.

PubMed

Patrzyk, Piotr M; Link, Daniela; Marewski, Julian N

2017-01-01

Artificial intelligence algorithms seek inspiration from human cognitive systems in areas where humans outperform machines. But on what level should algorithms try to approximate human cognition? We argue that human-like machines should be designed to make decisions in transparent and comprehensible ways, which can be achieved by accurately mirroring human cognitive processes.

Relationship between Job Satisfaction of County Extension Staff and the Level of Emotional Intelligence of County Extension Directors

ERIC Educational Resources Information Center

Villard, Judith A.; Earnest, Garee W.

2006-01-01

This descriptive-correlational study used a census of Ohio State University Extension county directors and a random sample of county staff throughout the State of Ohio. Data were collected utilizing Bar-On's Emotional Intelligence Quotient instrument (county directors) and Warner's job satisfaction instrument (county staff). The study examined the…

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

PubMed

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-12-04

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

Correlation between developmental quotients (DASII) and social quotient (Malin's VSMS) in Indian children aged 6 months to 2 years.

PubMed

Bhave, Anupama; Bhargava, Roli; Kumar, Rashmi

2011-03-01

To determine correlation between developmental quotients (DQ) (DASII) and social quotients (SQ) (Malin's Vineland Social Maturity Scale (VSMS)). Malin's VSMS and DASII were done in 135 children aged 6 months to 2 years. SQ and DQ motor and mental were correlated using Pearson's correlation coefficient (r). Mean SQ and DQ and age equivalent scores were compared. Correlation coefficients between SQ and DQ (mental and motor were 0.849 and 0.791, respectively. Social age correlated highly with mental age (r = 0.906). Mean SQ was higher than mean DQa. SQ tends to be higher than DQ and correlates best with DQ mental. © 2010 The Authors. Journal of Paediatrics and Child Health © 2010 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

Intelligence and cortical thickness in children with complex partial seizures.

PubMed

Tosun, Duygu; Caplan, Rochelle; Siddarth, Prabha; Seidenberg, Michael; Gurbani, Suresh; Toga, Arthur W; Hermann, Bruce

2011-07-15

Prior studies on healthy children have demonstrated regional variations and a complex and dynamic relationship between intelligence and cerebral tissue. Yet, there is little information regarding the neuroanatomical correlates of general intelligence in children with epilepsy compared to healthy controls. In vivo imaging techniques, combined with methods for advanced image processing and analysis, offer the potential to examine quantitative mapping of brain development and its abnormalities in childhood epilepsy. A surface-based, computational high resolution 3-D magnetic resonance image analytic technique was used to compare the relationship of cortical thickness with age and intelligence quotient (IQ) in 65 children and adolescents with complex partial seizures (CPS) and 58 healthy controls, aged 6-18 years. Children were grouped according to health status (epilepsy; controls) and IQ level (average and above; below average) and compared on age-related patterns of cortical thickness. Our cross-sectional findings suggest that disruption in normal age-related cortical thickness expression is associated with intelligence in pediatric CPS patients both with average and below average IQ scores. Copyright © 2011 Elsevier Inc. All rights reserved.

IQ Tests Are Not for Machines, Yet

ERIC Educational Resources Information Center

Dowe, David L.; Hernandez-Orallo, Jose

2012-01-01

Complex, but specific, tasks--such as chess or "Jeopardy!"--are popularly seen as milestones for artificial intelligence (AI). However, they are not appropriate for evaluating the intelligence of machines or measuring the progress in AI. Aware of this delusion, Detterman has recently raised a challenge prompting AI researchers to evaluate their…

Regional homogeneity of the resting-state brain activity correlates with individual intelligence.

PubMed

Wang, Leiqiong; Song, Ming; Jiang, Tianzi; Zhang, Yunting; Yu, Chunshui

2011-01-25

Resting-state functional magnetic resonance imaging has confirmed that the strengths of the long distance functional connectivity between different brain areas are correlated with individual differences in intelligence. However, the association between the local connectivity within a specific brain region and intelligence during rest remains largely unknown. The aim of this study is to investigate the relationship between local connectivity and intelligence. Fifty-nine right-handed healthy adults participated in the study. The regional homogeneity (ReHo) was used to assess the strength of local connectivity. The associations between ReHo and full-scale intelligence quotient (FSIQ) scores were studied in a voxel-wise manner using partial correlation analysis controlling for age and sex. We found that the FSIQ scores were positively correlated with the ReHo values of the bilateral inferior parietal lobules, middle frontal, parahippocampal and inferior temporal gyri, the right thalamus, superior frontal and fusiform gyri, and the left superior parietal lobule. The main findings are consistent with the parieto-frontal integration theory (P-FIT) of intelligence, supporting the view that general intelligence involves multiple brain regions throughout the brain. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Active learning machine learns to create new quantum experiments.

PubMed

Melnikov, Alexey A; Poulsen Nautrup, Hendrik; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J

2018-02-06

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Hemispheric Differences in White Matter Microstructure between Two Profiles of Children with High Intelligence Quotient vs. Controls: A Tract-Based Spatial Statistics Study

PubMed Central

Nusbaum, Fanny; Hannoun, Salem; Kocevar, Gabriel; Stamile, Claudio; Fourneret, Pierre; Revol, Olivier; Sappey-Marinier, Dominique

2017-01-01

Objectives: The main goal of this study was to investigate and compare the neural substrate of two children's profiles of high intelligence quotient (HIQ). Methods: Two groups of HIQ children were included with either a homogeneous (Hom-HIQ: n = 20) or a heterogeneous IQ profile (Het-HIQ: n = 24) as defined by a significant difference between verbal comprehension index and perceptual reasoning index. Diffusion tensor imaging was used to assess white matter (WM) microstructure while tract-based spatial statistics (TBSS) analysis was performed to detect and localize WM regional differences in fractional anisotropy (FA), mean diffusivity, axial (AD), and radial diffusivities. Quantitative measurements were performed on 48 regions and 21 fiber-bundles of WM. Results: Hom-HIQ children presented higher FA than Het-HIQ children in widespread WM regions including central structures, and associative intra-hemispheric WM fasciculi. AD was also greater in numerous WM regions of Total-HIQ, Hom-HIQ, and Het-HIQ groups when compared to the Control group. Hom-HIQ and Het-HIQ groups also differed by their hemispheric lateralization in AD differences compared to Controls. Het-HIQ and Hom-HIQ groups showed a lateralization ratio (left/right) of 1.38 and 0.78, respectively. Conclusions: These findings suggest that both inter- and intra-hemispheric WM integrity are enhanced in HIQ children and that neural substrate differs between Hom-HIQ and Het-HIQ. The left hemispheric lateralization of Het-HIQ children is concordant with their higher verbal index while the relative right hemispheric lateralization of Hom-HIQ children is concordant with their global brain processing and adaptation capacities as evidenced by their homogeneous IQ. PMID:28420955

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

PubMed Central

Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

2015-01-01

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced. PMID:26690164

Glottal open quotient in singing: Measurements and correlation with laryngeal mechanisms, vocal intensity, and fundamental frequency

NASA Astrophysics Data System (ADS)

Henrich, Nathalie; D'Alessandro, Christophe; Doval, Boris; Castellengo, Michèle

2005-03-01

This article presents the results of glottal open-quotient measurements in the case of singing voice production. It explores the relationship between open quotient and laryngeal mechanisms, vocal intensity, and fundamental frequency. The audio and electroglottographic signals of 18 classically trained male and female singers were recorded and analyzed with regard to vocal intensity, fundamental frequency, and open quotient. Fundamental frequency and open quotient are derived from the differentiated electroglottographic signal, using the DECOM (DEgg Correlation-based Open quotient Measurement) method. As male and female phonation may differ in respect to vocal-fold vibratory properties, a distinction is made between two different glottal configurations, which are called laryngeal mechanisms: mechanism 1 (related to chest, modal, and male head register) and mechanism 2 (related to falsetto for male and head register for female). The results show that open quotient depends on the laryngeal mechanisms. It ranges from 0.3 to 0.8 in mechanism 1 and from 0.5 to 0.95 in mechanism 2. The open quotient is strongly related to vocal intensity in mechanism 1 and to fundamental frequency in mechanism 2. .

Will machines ever think

NASA Technical Reports Server (NTRS)

Denning, P. J.

1986-01-01

Artificial Intelligence research has come under fire for failing to fulfill its promises. A growing number of AI researchers are reexamining the bases of AI research and are challenging the assumption that intelligent behavior can be fully explained as manipulation of symbols by algorithms. Three recent books -- Mind over Machine (H. Dreyfus and S. Dreyfus), Understanding Computers and Cognition (T. Winograd and F. Flores), and Brains, Behavior, and Robots (J. Albus) -- explore alternatives and open the door to new architectures that may be able to learn skills.

Integration of an intelligent systems behavior simulator and a scalable soldier-machine interface

NASA Astrophysics Data System (ADS)

Johnson, Tony; Manteuffel, Chris; Brewster, Benjamin; Tierney, Terry

2007-04-01

As the Army's Future Combat Systems (FCS) introduce emerging technologies and new force structures to the battlefield, soldiers will increasingly face new challenges in workload management. The next generation warfighter will be responsible for effectively managing robotic assets in addition to performing other missions. Studies of future battlefield operational scenarios involving the use of automation, including the specification of existing and proposed technologies, will provide significant insight into potential problem areas regarding soldier workload. The US Army Tank Automotive Research, Development, and Engineering Center (TARDEC) is currently executing an Army technology objective program to analyze and evaluate the effect of automated technologies and their associated control devices with respect to soldier workload. The Human-Robotic Interface (HRI) Intelligent Systems Behavior Simulator (ISBS) is a human performance measurement simulation system that allows modelers to develop constructive simulations of military scenarios with various deployments of interface technologies in order to evaluate operator effectiveness. One such interface is TARDEC's Scalable Soldier-Machine Interface (SMI). The scalable SMI provides a configurable machine interface application that is capable of adapting to several hardware platforms by recognizing the physical space limitations of the display device. This paper describes the integration of the ISBS and Scalable SMI applications, which will ultimately benefit both systems. The ISBS will be able to use the Scalable SMI to visualize the behaviors of virtual soldiers performing HRI tasks, such as route planning, and the scalable SMI will benefit from stimuli provided by the ISBS simulation environment. The paper describes the background of each system and details of the system integration approach.

Just noticeable differences of open quotient and asymmetry coefficient in singing voice.

PubMed

Henrich, Nathalie; Sundin, Gunilla; Ambroise, Daniel; d'Alessandro, Christophe; Castellengo, Michèle; Doval, Boris

2003-12-01

This study aims to explore the perceptual relevance of the variations of glottal flow parameters and to what extent a small variation can be detected. Just Noticeable Differences (JNDs) have been measured for three values of open quotient (0.4, 0.6, and 0.8) and two values of asymmetry coefficient (2/3 and 0.8), and the effect of changes of vowel, pitch, vibrato, and amplitude parameters has been tested. Two main groups of subjects have been analyzed: a group of 20 untrained subjects and a group of 10 trained subjects. The results show that the JND for open quotient is highly dependent on the target value: an increase of the JND is noticed when the open quotient target value is increased. The relative JND is constant: deltaOq/Oq = 14% for the untrained and 10% for the trained. In the same way, the JND for asymmetry coefficient is also slightly dependent on the target value--an increase of the asymmetry coefficient value leads to a decrease of the JND. The results show that there is no effect from the selected vowel or frequency (two values have been tested), but that the addition of a vibrato has a small effect on the JND of open quotient. The choice of an amplitude parameter also has a great effect on the JND of open quotient.

Hands-free human-machine interaction with voice

NASA Astrophysics Data System (ADS)

Juang, B. H.

2004-05-01

Voice is natural communication interface between a human and a machine. The machine, when placed in today's communication networks, may be configured to provide automation to save substantial operating cost, as demonstrated in AT&T's VRCP (Voice Recognition Call Processing), or to facilitate intelligent services, such as virtual personal assistants, to enhance individual productivity. These intelligent services often need to be accessible anytime, anywhere (e.g., in cars when the user is in a hands-busy-eyes-busy situation or during meetings where constantly talking to a microphone is either undersirable or impossible), and thus call for advanced signal processing and automatic speech recognition techniques which support what we call ``hands-free'' human-machine communication. These techniques entail a broad spectrum of technical ideas, ranging from use of directional microphones and acoustic echo cancellatiion to robust speech recognition. In this talk, we highlight a number of key techniques that were developed for hands-free human-machine communication in the mid-1990s after Bell Labs became a unit of Lucent Technologies. A video clip will be played to demonstrate the accomplishement.

Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.

PubMed

Zhou, Yongxia; Yu, Fang; Duong, Timothy

2014-01-01

This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.

Role of Emotional Intelligence in Conflict Management Strategies of Nurses.

PubMed

Başoğul, Ceyda; Özgür, Gönül

2016-09-01

This study analyzes the emotional intelligence levels and conflict management strategies of nurses and the association between them. This cross-sectional, descriptive study was conducted with 277 nurses in a stratified random sample from a university hospital in Turkey. The data were collected from nurses who gave their informed consent to participate using a personal information form, the Rahim Organizational Conflict Inventory-II and Bar-On's Emotional Quotient Inventory (EQ-I). Data were assessed by descriptive statistics, t tests, and Pearson correlation analyses, using SPSS software. The levels of the nurses' strategies were as follows: avoiding (M = 2.98), dominating (M = 2.76), and obliging (M = 2.71) were medium; compromising (M = 1.99) and integration (M = 1.96) were low. The levels of the emotional intelligence of nurses (mean = 2.75) were medium on a 5-point scale. Integration (r = .168), obliging (r = .25), dominating (r = .18), and compromising (r = .33), which are conflict management strategies, were positively correlated with scores of emotional intelligence, and avoiding (r = -.25) was negatively correlated with scores of emotional intelligence (p < .05). The study determined that nurses' emotional intelligence affects conflict management strategies. To use effective strategies in conflict management, nurses must develop emotional intelligence. Training programs on conflict management and emotional intelligence are needed to improve effective conflict management in healthcare facilities. Copyright © 2016. Published by Elsevier B.V.

Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision

NASA Astrophysics Data System (ADS)

Hendrawan, Y.; Hawa, L. C.; Damayanti, R.

2018-03-01

This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.

[Intelligence level and structure in school age children with fetal growth restriction].

PubMed

Ma, Jian; Ma, Hong-Wei; Tian, Xiao-Bo; Liu, Fang

2009-10-01

To study the intelligence level and structure in school age children with fetal growth restriction (FGR). The intelligence levels were tested by the Wechsler Children Scales of Intelligence (C-WISC) in 54 children with FGR and in 84 normal children. The full intelligence quotient (FIQ), verbal IQ (VIQ) and performance IQ (PIQ) in the FGR group were 105.9+/-10.3, 112.4+/-11.2 and 97.1+/-10.6 respectively, and they all were in a normal range. But the PIQ was significantly lower than that in the control group (104.8+/-10.5; p<0.001), and the picture arrangement and the decipher subtest scores were significantly lower than those in the control group (p<0.01). The scores of perception/organization and memory/attention factors in the FGR group were 99.8+/-11.1 and 116.3+/-14.4, respectively, which were inferior to those in the control group (104.6+/-11.5 and 113.4+/-14.5 respectively; p<0.05). The total intelligence level of children with FGR is normal, but there are imbalances in the intelligence structure and dysfunctions in performance ability related to right cerebral hemisphere. Performance trainings should be done from the infancy in children with FGR.

Computer science, artificial intelligence, and cybernetics: Applied artificial intelligence in Japan

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

Rubinger, B.

1988-01-01

This sourcebook provides information on the developments in artificial intelligence originating in Japan. Spanning such innovations as software productivity, natural language processing, CAD, and parallel inference machines, this volume lists leading organizations conducting research or implementing AI systems, describes AI applications being pursued, illustrates current results achieved, and highlights sources reporting progress.

On the exterior Dirichlet problem for Hessian quotient equations

NASA Astrophysics Data System (ADS)

Li, Dongsheng; Li, Zhisu

2018-06-01

In this paper, we establish the existence and uniqueness theorem for solutions of the exterior Dirichlet problem for Hessian quotient equations with prescribed asymptotic behavior at infinity. This extends the previous related results on the Monge-Ampère equations and on the Hessian equations, and rearranges them in a systematic way. Based on the Perron's method, the main ingredient of this paper is to construct some appropriate subsolutions of the Hessian quotient equation, which is realized by introducing some new quantities about the elementary symmetric polynomials and using them to analyze the corresponding ordinary differential equation related to the generalized radially symmetric subsolutions of the original equation.

On the Construction and the Structure of Off-Shell Supermultiplet Quotients

NASA Astrophysics Data System (ADS)

Hübsch, Tristan; Katona, Gregory A.

2012-11-01

Recent efforts to classify representations of supersymmetry with no central charge [C. F. Doran et al., Adv. Theor. Math. Phys.15, 1909 (2011)] have focused on supermultiplets that are aptly depicted by Adinkras, wherein every supersymmetry generator transforms each component field into precisely one other component field or its derivative. Herein, we study gauge-quotients of direct sums of Adinkras by a supersymmetric image of another Adinkra and thus solve a puzzle in the paper by Doran et al., Int. J. Mod. Phys. A22, 869 (2007): such (gauge-)quotients are not Adinkras but more general types of supermultiplets, each depicted as a connected network of Adinkras. Iterating this gauge-quotient construction then yields an indefinite sequence of ever larger supermultiplets, reminiscent of Weyl's construction that is known to produce all finite-dimensional unitary representations in Lie algebras.

Modelling of internal architecture of kinesin nanomotor as a machine language.

PubMed

Khataee, H R; Ibrahim, M Y

2012-09-01

Kinesin is a protein-based natural nanomotor that transports molecular cargoes within cells by walking along microtubules. Kinesin nanomotor is considered as a bio-nanoagent which is able to sense the cell through its sensors (i.e. its heads and tail), make the decision internally and perform actions on the cell through its actuator (i.e. its motor domain). The study maps the agent-based architectural model of internal decision-making process of kinesin nanomotor to a machine language using an automata algorithm. The applied automata algorithm receives the internal agent-based architectural model of kinesin nanomotor as a deterministic finite automaton (DFA) model and generates a regular machine language. The generated regular machine language was acceptable by the architectural DFA model of the nanomotor and also in good agreement with its natural behaviour. The internal agent-based architectural model of kinesin nanomotor indicates the degree of autonomy and intelligence of the nanomotor interactions with its cell. Thus, our developed regular machine language can model the degree of autonomy and intelligence of kinesin nanomotor interactions with its cell as a language. Modelling of internal architectures of autonomous and intelligent bio-nanosystems as machine languages can lay the foundation towards the concept of bio-nanoswarms and next phases of the bio-nanorobotic systems development.

Knowledge-based load leveling and task allocation in human-machine systems

NASA Technical Reports Server (NTRS)

Chignell, M. H.; Hancock, P. A.

1986-01-01

Conventional human-machine systems use task allocation policies which are based on the premise of a flexible human operator. This individual is most often required to compensate for and augment the capabilities of the machine. The development of artificial intelligence and improved technologies have allowed for a wider range of task allocation strategies. In response to these issues a Knowledge Based Adaptive Mechanism (KBAM) is proposed for assigning tasks to human and machine in real time, using a load leveling policy. This mechanism employs an online workload assessment and compensation system which is responsive to variations in load through an intelligent interface. This interface consists of a loading strategy reasoner which has access to information about the current status of the human-machine system as well as a database of admissible human/machine loading strategies. Difficulties standing in the way of successful implementation of the load leveling strategy are examined.

Paradox in AI - AI 2.0: The Way to Machine Consciousness

NASA Astrophysics Data System (ADS)

Palensky, Peter; Bruckner, Dietmar; Tmej, Anna; Deutsch, Tobias

Artificial Intelligence, the big promise of the last millennium, has apparently made its way into our daily lives. Cell phones with speech control, evolutionary computing in data mining or power grids, optimized via neural network, show its applicability in industrial environments. The original expectation of true intelligence and thinking machines lies still ahead of us. Researchers are, however, optimistic as never before. This paper tries to compare the views, challenges and approaches of several disciplines: engineering, psychology, neuroscience, philosophy. It gives a short introduction to Psychoanalysis, discusses the term consciousness, social implications of intelligent machines, related theories, and expectations and shall serve as a starting point for first attempts of combining these diverse thoughts.

Intelligent robot trends for factory automation

NASA Astrophysics Data System (ADS)

Hall, Ernest L.

1997-09-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent economic and technical trends. The robotics industry now has a billion-dollar market in the U.S. and is growing. Feasibility studies are presented which also show unaudited healthy rates of return for a variety of robotic applications. Technically, the machines are faster, cheaper, more repeatable, more reliable and safer. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. However, the road from inspiration to successful application is still long and difficult, often taking decades to achieve a new product. More cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit both industry and society.

Intelligent robot trends for 1998

NASA Astrophysics Data System (ADS)

Hall, Ernest L.

1998-10-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent technical and economic trends. Technically, the machines are faster, cheaper, more repeatable, more reliable and safer. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. Economically, the robotics industry now has a 1.1 billion-dollar market in the U.S. and is growing. Feasibility studies results are presented which also show decreasing costs for robots and unaudited healthy rates of return for a variety of robotic applications. However, the road from inspiration to successful application can be long and difficult, often taking decades to achieve a new product. A greater emphasis on mechatronics is needed in our universities. Certainly, more cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit industry and society.

[The impact of malnutrition on brain development, intelligence and school work performance].

PubMed

Leiva Plaza, B; Inzunza Brito, N; Pérez Torrejón, H; Castro Gloor, V; Jansana Medina, J M; Toro Díaz, T; Almagiá Flores, A; Navarro Díaz, A; Urrutia Cáceres, M S; Cervilla Oltremari, J; Ivanovic Marincovich, D

2001-03-01

The findings from several authors confirm that undernutrition at an early age affects brain growth and intellectual quotient. Most part of students with the lowest scholastic achievement scores present suboptimal head circumference (anthropometric indicator of past nutrition and brain development) and brain size. On the other hand, intellectual quotient measured through intelligence tests (Weschler-R, or the Raven Progressives Matrices Test) has been described positively and significantly correlated with brain size measured by magnetic resonance imaging (MRI); in this respect, intellectual ability has been recognized as one of the best predictors of scholastic achievement. Considering that education is the change lever for the improvement of the quality of life and that the absolute numbers of undernourished children have been increasing in the world, is of major relevance to analyse the long-term effects of undernutrition at an early age. The investigations related to the interrelationships between nutritional status, brain development, intelligence and scholastic achievement are of greatest importance, since nutritional problems affect the lowest socioeconomic stratum with negative consequences manifested in school-age, in higher levels of school dropout, learning problems and a low percentage of students enrolling into higher education. This limits the development of people by which a clear economic benefit to increase adult productivity for government policies might be successful preventing childhood malnutrition.

Product and Quotient Rules from Logarithmic Differentiation

ERIC Educational Resources Information Center

Chen, Zhibo

2012-01-01

A new application of logarithmic differentiation is presented, which provides an alternative elegant proof of two basic rules of differentiation: the product rule and the quotient rule. The proof can intrigue students, help promote their critical thinking and rigorous reasoning and deepen their understanding of previously encountered concepts. The…

Student profile with high adversity quotient in math learning

NASA Astrophysics Data System (ADS)

Hastuti, T. D.; Sari S, D. R.; Riyadi

2018-03-01

Lately a lot of research conducted to determine the effect of Adversity Quotient students on learning achievement. This is done because many students with excellent IQ and EQ, but often have problems when they are in the workforce. This study will analyze the profile of High School students with high Adversity Quotient (AQ) in learning mathematics. The test is done using a questionnaire to know the AQ level of the students, and the interview is done to get the data about the student profile. Based on the results of tests and interviews obtained data that students with high AQ able to face the learning of mathematics in various materials and with different models of learning.

Relationship between Divergent Thinking and Intelligence: An Empirical Study of the Threshold Hypothesis with Chinese Children

PubMed Central

Shi, Baoguo; Wang, Lijing; Yang, Jiahui; Zhang, Mengpin; Xu, Li

2017-01-01

The threshold hypothesis is a classical and notable explanation for the relationship between creativity and intelligence. However, few empirical examinations of this theory exist, and the results are inconsistent. To test this hypothesis, this study investigated the relationship between divergent thinking (DT) and intelligence with a sample of 568 Chinese children aged between 11 and 13 years old using testing and questionnaire methods. The study focused on the breakpoint of intelligence and the moderation effect of openness on the relationship between intelligence and DT. The findings were as follows: (1) a breakpoint at the intelligence quotient (IQ) of 109.20 when investigating the relationship between either DT fluency or DT flexibility and intelligence. Another breakpoint was detected at the IQ of 116.80 concerning the correlation between originality and intelligence. The breakpoint of the relation between the composite score of creativity and intelligence occurred at the IQ of 110.10. (2) Openness to experience had a moderating effect on the correlation between the indicators of creativity and intelligence under the breakpoint. Above this point, however, the effect was not significant. The results suggested a relationship between DT and intelligence among Chinese children, which conforms to the threshold hypothesis. Besides, it remains necessary to explore the personality factors accounting for individual differences in the relationship between DT and intelligence. PMID:28275361

[French version of screening questionnaire for high-functioning autism or Asperger syndrome in adolescent: Autism Spectrum Quotient, Empathy Quotient and Systemizing Quotient. Protocol and questionnaire translation].

PubMed

Sonié, Sandrine; Kassai, Behrouz; Pirat, Elodie; Masson, Sandrine; Bain, Paul; Robinson, Janine; Reboul, Anne; Wicker, Bruno; Chevallier, Coralie; Beaude-Chervet, Véronique; Deleage, Marie-Hélène; Charvet, Dorothée; Barthélémy, Catherine; Rochet, Thierry; Tatou, Mohamed; Arnaud, Valérie; Manificat, Sabine

2011-04-01

No tools are currently available in France, for the detection of autism without mental retardation (high functioning autism and Asperger syndrome here referred as TED SDI). Use of screening tests by first-line clinicians would allow better detection of children who are likely to display such difficulties and to improve patients' care. In England, 3 questionnaires have been evaluated: Autism Spectrum Quotient (AQ), Empathy Quotient (EQ), and Systemizing Quotient (SQ). This is the translation and evaluation of 3 questionnaires in France for TED SDI and control adolescents. The translation of the questionnaires into French required two simultaneous translations, two back-translations and two consensus meetings. This is a cross-sectional study comparing scores obtained with the three AQ, EQ and SQ questionnaires. These questionnaires were completed by the parents of four groups of adolescents 11-18 years: 100 TED SDI adolescents (50 with IQ ≥ 85 and 50 with 70≤IQ<85), 50 adolescents with another psychiatric disorder (TP) and 200 control adolescents (T). 580 questionnaires have been sent to 40 recruiting centres. By the 28th of February, 2010, 277 completed questionnaires were received completed (TED SDI: 70 (70%); TP: 25 (50%) et T: 182 (91%)). In the control group, 92 girls (mean 14.4±1.7 years) and 66 boys (14.5±1.7 years) were recruited. In the TED SDI group, 4 girls (14.3±2.4 years) and 42 boys (14.5±1.7 years) were recruited. One girl (81) and 6 boys (72.2±7.7) have an IQ between 70 and 85, and 3 girls (95.3±4.2) and 36 boys (102.9±12) have an IQ higher than 85. In the TP group, 9 girls (15.9±1.7 years) and 4 boys (15.8±1.9 years) were recruited. The aim of this study is to make the AQ, EQ and SQ questionnaires available in French for French speaking clinicians. This study will allow a rigorous evaluation of the usefulness of the AQ questionnaire in the screening of TED SDI in adolescents. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Artificial Intelligence and Expert Systems.

ERIC Educational Resources Information Center

Lawlor, Joseph

Artificial intelligence (AI) is the field of scientific inquiry concerned with designing machine systems that can simulate human mental processes. The field draws upon theoretical constructs from a wide variety of disciplines, including mathematics, psychology, linguistics, neurophysiology, computer science, and electronic engineering. Some of the…

The 13 th Annual Intelligent Ground Vehicle Competition: intelligent ground vehicles created by intelligent teams

NASA Astrophysics Data System (ADS)

Theisen, Bernard L.

2005-10-01

The Intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990s. The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics, and mobile platform fundamentals to design and build an unmanned system. Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 13 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 50 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the three-day competition are highlighted. Finally, an assessment of the competition based on participant feedback is presented.

The comparison of Missouri mathematics project and teams games tournament viewed from emotional quotient eight grade student of junior school

NASA Astrophysics Data System (ADS)

Setyawan, Indra; Budiyono, Slamet, Isnandar

2017-08-01

This research was a quasi-experimental research with 2 × 3 factorial design. It aimed to determine the learning model between Missouri Mathematics Project (MMP) and Teams Games Tournament (TGT) that gave the best achievement on mathematics subject viewed from emotional quotient. The population of this research were all of Junior High School students at the 8th grade in Surakarta City, Central Java, Indonesia in academic year 2016/2017 which applied KTSP curriculum. The sample was taken by using stratified cluster random sampling. The data were collected by using methods of documentation, emotional quotient questionnaires, and mathematics achievement test. Data analysis technique used two ways analysis of variance (ANOVA) with unequal cell. According to the research findings, it could be concluded that: (1) students' mathematics achievement which were taught by using MMP is as good as emotional quotient achievement which were taught by using TGT in straight-line equation material, (2) mathematics achievement of students with high emotional quotient is better than students with medium and low emotional quotient, and mathematics achievement of students with medium emotional quotient is as good as students with low emotional quotient in straight-line equation material, (3) in each learning model, mathematics achievement of students with high emotional quotient is better than students with medium and low emotional quotient, and mathematics achievement of students with medium emotional quotient is as good as students with low emotional quotient in straight-line equation material (4) in each category of high and medium emotional quotient, student's mathematics achievement which were taught by using MMP is better than student's mathematics achievement which were taught by using TGT and in low emotional quotient student's mathematics achievement which were taught by using MMP is as good as student's mathematics achievement which were taught by using TGT in straight

Imaging structural covariance in the development of intelligence.

PubMed

Khundrakpam, Budhachandra S; Lewis, John D; Reid, Andrew; Karama, Sherif; Zhao, Lu; Chouinard-Decorte, Francois; Evans, Alan C

2017-01-01

Verbal and non-verbal intelligence in children is highly correlated, and thus, it has been difficult to differentiate their neural substrates. Nevertheless, recent studies have shown that verbal and non-verbal intelligence can be dissociated and focal cortical regions corresponding to each have been demonstrated. However, the pattern of structural covariance corresponding to verbal and non-verbal intelligence remains unexplored. In this study, we used 586 longitudinal anatomical MRI scans of subjects aged 6-18 years, who had concurrent intelligence quotient (IQ) testing on the Wechsler Abbreviated Scale of Intelligence. Structural covariance networks (SCNs) were constructed using interregional correlations in cortical thickness for low-IQ (Performance IQ=100±8, Verbal IQ=100±7) and high-IQ (PIQ=121±8, VIQ=120±9) groups. From low- to high-VIQ group, we observed constrained patterns of anatomical coupling among cortical regions, complemented by observations of higher global efficiency and modularity, and lower local efficiency in high-VIQ group, suggesting a shift towards a more optimal topological organization. Analysis of nodal topological properties (regional efficiency and participation coefficient) revealed greater involvement of left-hemispheric language related regions including inferior frontal and superior temporal gyri for high-VIQ group. From low- to high-PIQ group, we did not observe significant differences in anatomical coupling patterns, global and nodal topological properties. Our findings indicate that people with higher verbal intelligence have structural brain differences from people with lower verbal intelligence - not only in localized cortical regions, but also in the patterns of anatomical coupling among widely distributed cortical regions, possibly resulting to a system-level reorganization that might lead to a more efficient organization in high-VIQ group. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Towards Intelligent Environments: An Augmented Reality–Brain–Machine Interface Operated with a See-Through Head-Mount Display

PubMed Central

Takano, Kouji; Hata, Naoki; Kansaku, Kenji

2011-01-01

The brain–machine interface (BMI) or brain–computer interface is a new interface technology that uses neurophysiological signals from the brain to control external machines or computers. This technology is expected to support daily activities, especially for persons with disabilities. To expand the range of activities enabled by this type of interface, here, we added augmented reality (AR) to a P300-based BMI. In this new system, we used a see-through head-mount display (HMD) to create control panels with flicker visual stimuli to support the user in areas close to controllable devices. When the attached camera detects an AR marker, the position and orientation of the marker are calculated, and the control panel for the pre-assigned appliance is created by the AR system and superimposed on the HMD. The participants were required to control system-compatible devices, and they successfully operated them without significant training. Online performance with the HMD was not different from that using an LCD monitor. Posterior and lateral (right or left) channel selections contributed to operation of the AR–BMI with both the HMD and LCD monitor. Our results indicate that AR–BMI systems operated with a see-through HMD may be useful in building advanced intelligent environments. PMID:21541307

Emotional intelligence and its correlation to performance as a resident: a preliminary study.

PubMed

Talarico, Joseph F; Metro, David G; Patel, Rita M; Carney, Patricia; Wetmore, Amy L

2008-03-01

To test the hypothesis that emotional intelligence, as measured by the Bar-On Emotional Quotient Inventory (EQ-I) 125 (Multi Health Systems, Toronto, Ontario, Canada) personal inventory, would correlate with resident performance. Prospective survey. University-affiliated, multiinstitutional anesthesiology residency program. Current clinical anesthesiology years one to three (PGY 2-4) anesthesiology residents enrolled in the University of Pittsburgh Anesthesiology Residency Program. Participants confidentially completed the Bar-On EQ-I 125 survey. Results of the individual EQ-I 125 and daily evaluations by the faculty of the residency program were compiled and analyzed. There was no positive correlation between any facet of emotional intelligence and resident performance. There was statistically significant negative correlation (-0.40; P < 0.05) between assertiveness and the "American Board of Anesthesiology essential attributes" component of the resident evaluation. Emotional intelligence, as measured by the Bar-On EQ-I personal inventory, does not strongly correlate to resident performance as defined at the University of Pittsburgh.

Distributed intelligence for supervisory control

NASA Technical Reports Server (NTRS)

Wolfe, W. J.; Raney, S. D.

1987-01-01

Supervisory control systems must deal with various types of intelligence distributed throughout the layers of control. Typical layers are real-time servo control, off-line planning and reasoning subsystems and finally, the human operator. Design methodologies must account for the fact that the majority of the intelligence will reside with the human operator. Hierarchical decompositions and feedback loops as conceptual building blocks that provide a common ground for man-machine interaction are discussed. Examples of types of parallelism and parallel implementation on several classes of computer architecture are also discussed.

Quantum-Enhanced Machine Learning

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

2016-09-01

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?

PubMed

Bini, Stefano A

2018-02-27

This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it. Copyright © 2018 Elsevier Inc. All rights reserved.

Long-term outcomes of epilepsy surgery in school-aged children with partial epilepsy.

PubMed

Liang, Shuli; Wang, Shuai; Zhang, Junchen; Ding, Chengyun; Zhang, Zhiwen; Fu, Xiangping; Hu, Xiaohong; Meng, Xiaoluo; Jiang, Hong; Zhang, Shaohui

2012-10-01

The pediatric epileptic spectrum and seizure control in surgical patients have been defined in developed countries. However, corresponding data on school-aged children from developing countries are insufficient. We summarized epileptic surgical data from four centers in China, to compare surgical outcomes of school-aged children with intractable partial epilepsy from China and those from developed countries, and introduce surgical candidate criteria. Data from 206 children (aged 6-14 years) undergoing surgical resection for epilepsy between September 2001 and January 2007 were selected. Postoperative freedom from seizures was achieved in 173 cases (84.0%) at 1 year, 149 (72.3%) at 3 years, and 139 (67.5%) at 5 years. Patients with focal magnetic resonance imaging abnormalities and a short history of seizure were most likely to become seizure-free postoperatively. Those with preoperative low intelligence quotients who became seizure-free postoperatively achieved improvements in full memory quotients, intelligence quotients, and overall quality of life at 2 years. Significant differences were evident in mean changes of full intelligence quotient, full memory quotient, and overall quality of life between patients with preoperative low intelligence quotients who received corpus callosotomies and those with a normal preoperative intelligence quotient, and between seizure-free children and those with continual seizures. Copyright © 2012 Elsevier Inc. All rights reserved.

The Potential of Artificial Intelligence in Aids for the Disabled.

ERIC Educational Resources Information Center

Boyer, John J.

The paper explores the possibilities for applying the knowledge of artificial intelligence (AI) research to aids for the disabled. Following a definition of artificial intelligence, the paper reviews areas of basic AI research, such as computer vision, machine learning, and planning and problem solving. Among application areas relevant to the…

Relationships between phenylalanine levels, intelligence and socioeconomic status of patients with phenylketonuria.

PubMed

Castro, Isabel Pimenta Spínola; Borges, Juliana Martins; Chagas, Heloísa Alves; Tibúrcio, Jacqueline; Starling, Ana Lúcia Pimenta; Aguiar, Marcos José Burle de

2012-07-01

To assess intelligence and its relationship with blood phenylalanine concentrations and socioeconomic status in patients with phenylketonuria after 6 to 12 years of treatment. Sixty-three children were classified according to phenylalanine levels and socioeconomic status and assessed using the Wechsler Intelligence Scale for Children. The Statistical Package for the Social Sciences (SPSS) was used to analyze phenylalanine; ANOVA was used to analyze intelligence quotients (IQ) and phenylalanine levels; and ordinal logistic regression was used to analyze the likelihood of higher IQ. The overall IQ scores of 90.5% of the children were within a range from borderline intellectual deficiency to very high intelligence; for verbal IQ this proportion was 96.8% and 92.1% had performance IQ scores within this band. The categories from low to upper-medium socioeconomic status contained 98.4% of patients' families. The likelihood of having medium to high IQ was 4.29 times greater for children with good phenylalanine control and 4.03 greater for those from higher socioeconomic strata. Treatment prevented mental retardation in 90.5% of the patients. Control of phenylalanine levels and higher socioeconomic status were associated with higher IQ scores.

Automation and robotics technology for intelligent mining systems

NASA Technical Reports Server (NTRS)

Welsh, Jeffrey H.

1989-01-01

The U.S. Bureau of Mines is approaching the problems of accidents and efficiency in the mining industry through the application of automation and robotics to mining systems. This technology can increase safety by removing workers from hazardous areas of the mines or from performing hazardous tasks. The short-term goal of the Automation and Robotics program is to develop technology that can be implemented in the form of an autonomous mining machine using current continuous mining machine equipment. In the longer term, the goal is to conduct research that will lead to new intelligent mining systems that capitalize on the capabilities of robotics. The Bureau of Mines Automation and Robotics program has been structured to produce the technology required for the short- and long-term goals. The short-term goal of application of automation and robotics to an existing mining machine, resulting in autonomous operation, is expected to be accomplished within five years. Key technology elements required for an autonomous continuous mining machine are well underway and include machine navigation systems, coal-rock interface detectors, machine condition monitoring, and intelligent computer systems. The Bureau of Mines program is described, including status of key technology elements for an autonomous continuous mining machine, the program schedule, and future work. Although the program is directed toward underground mining, much of the technology being developed may have applications for space systems or mining on the Moon or other planets.

The 1990 Goddard Conference on Space Applications of Artificial Intelligence

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1990-01-01

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

Emotional Intelligence and its Effect on Pharmacists and Pharmacy Students with Autistic-like Traits.

PubMed

Higuchi, Yuji; Inagaki, Masatoshi; Koyama, Toshihiro; Kitamura, Yoshihisa; Sendo, Toshiaki; Fujimori, Maiko; Kataoka, Hitomi; Hayashibara, Chinatsu; Uchitomi, Yosuke; Yamada, Norihito

2017-05-01

Objective. To measure whether Emotional intelligence (EI) would minimize the negative association between autistic-like traits (ALT) and empathic behavior and enhance the positive association between ALT and psychological distress. Methods. Our sample population included 823 hospital pharmacists belonging to a district society, and 378 pharmacy students. Analyses were performed to examine relationships between scores on the Emotional Intelligence Scale (EQS), Autism-Spectrum Quotient (AQ), Jefferson Scale of Empathy (JSE), and General Health Questionnaire-12 (GHQ). Results. Complete answers were obtained from 373 pharmacists, and 341 students. EQS partially intervened the associations between AQ and JSE and between AQ and GHQ. Conclusion. EI partially intervened the relationships between ALT and empathy, and between ALT and mental health, both of which are necessary for optimal pharmaceutical practice.

Artificial intelligence: Learning to see and act

NASA Astrophysics Data System (ADS)

Schölkopf, Bernhard

2015-02-01

An artificial-intelligence system uses machine learning from massive training sets to teach itself to play 49 classic computer games, demonstrating that it can adapt to a variety of tasks. See Letter p.529

Changes in thickness and surface area of the human cortex and their relationship with intelligence.

PubMed

Schnack, Hugo G; van Haren, Neeltje E M; Brouwer, Rachel M; Evans, Alan; Durston, Sarah; Boomsma, Dorret I; Kahn, René S; Hulshoff Pol, Hilleke E

2015-06-01

Changes in cortical thickness over time have been related to intelligence, but whether changes in cortical surface area are related to general cognitive functioning is unknown. We therefore examined the relationship between intelligence quotient (IQ) and changes in cortical thickness and surface over time in 504 healthy subjects. At 10 years of age, more intelligent children have a slightly thinner cortex than children with a lower IQ. This relationship becomes more pronounced with increasing age: with higher IQ, a faster thinning of the cortex is found over time. In the more intelligent young adults, this relationship reverses so that by the age of 42 a thicker cortex is associated with higher intelligence. In contrast, cortical surface is larger in more intelligent children at the age of 10. The cortical surface is still expanding, reaching its maximum area during adolescence. With higher IQ, cortical expansion is completed at a younger age; and once completed, surface area decreases at a higher rate. These findings suggest that intelligence may be more related to the magnitude and timing of changes in brain structure during development than to brain structure per se, and that the cortex is never completed but shows continuing intelligence-dependent development. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Intelligence Is Associated With Voluntary Disclosure in Child Sexual Abuse Victims.

PubMed

Bae, Seung Min; Kang, Jae Myeong; Hwang, In Cheol; Cho, Hyeongrae; Cho, Seong-Jin

2017-09-01

The purpose of this study was (1) to determine whether intelligence level is associated with the pattern of the disclosure and (2) to elucidate which, between the verbal and performance intelligence, better reflect the pattern of disclosure in child and adolescent sexual abuse victims. Data were collected on 162 participants who visited a public center for sexually abused children and adolescents between January 2013 and December 2014. Demographic information, case characteristics, and disclosure pattern as well as intelligence quotients (IQs) of subjects were gathered. Intelligence was analyzed as level, full scale IQ, and the verbal and performance IQ. Eighty-one subjects (50.0%) voluntarily disclosed that they have been sexually abused. In regression analysis, intellectual level, age, and the number of perpetrators were associated with disclosure pattern. Full scale IQ was associated with the disclosure pattern (odds ratio = .983, 95% confidence interval = .968-.997, p = .017). When intelligence was divided into verbal and performance IQ, verbal IQ affected the pattern of disclosure (odds ratio = .973, 95% confidence interval = .956-.991, p = .003) with linear correlation (p = .001). We found that IQ was associated with the disclosure pattern. The intelligence, especially in verbal domain, is linearly correlated with the probability of voluntary disclosure. We suggest that special legal assistance and social concern are required for children and adolescent victims below normal intelligence to make them disclose the sexual abuse. Copyright © 2017 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Neuroscience-Inspired Artificial Intelligence.

PubMed

Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew

2017-07-19

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields. Copyright © 2017. Published by Elsevier Inc.

Cooperating with machines.

PubMed

Crandall, Jacob W; Oudah, Mayada; Tennom; Ishowo-Oloko, Fatimah; Abdallah, Sherief; Bonnefon, Jean-François; Cebrian, Manuel; Shariff, Azim; Goodrich, Michael A; Rahwan, Iyad

2018-01-16

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human-machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

A machine learning system to improve heart failure patient assistance.

PubMed

Guidi, Gabriele; Pettenati, Maria Chiara; Melillo, Paolo; Iadanza, Ernesto

2014-11-01

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.

Mind the gap... in intelligence: re-examining the relationship between inequality and health.

PubMed

Kanazawa, Satoshi

2006-11-01

Wilkinson contends that economic inequality reduces the health and life expectancy of the whole population but his argument does not make sense within its own evolutionary framework. Recent evolutionary psychological theory suggests that the human brain, adapted to the ancestral environment, has difficulty comprehending and dealing with entities and situations that did not exist in the ancestral environment and that general intelligence evolved as a domain-specific adaptation to solve evolutionarily novel problems. Since most dangers to health in the contemporary society are evolutionarily novel, it follows that more intelligent individuals are better able to recognize and deal with such dangers and live longer. Consistent with the theory, the macro-level analyses show that income inequality and economic development have no effect on life expectancy at birth, infant mortality and age-specific mortality net of average intelligence quotient (IQ) in 126 countries. They also show that an average IQ has a very large and significant effect on population health but not in the evolutionarily familiar sub-Saharan Africa. At the micro level, the General Social Survey data show that, while both income and intelligence have independent positive effects on self-reported health, intelligence has a stronger effect than income. The data collectively suggest that individuals in wealthier and more egalitarian societies live longer and stay healthier, not because they are wealthier or more egalitarian but because they are more intelligent.

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

A computational proof of concept of a machine-intelligent artificial pancreas using Lyapunov stability and differential game theory.

PubMed

Greenwood, Nigel J C; Gunton, Jenny E

2014-07-01

This study demonstrated the novel application of a "machine-intelligent" mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties. Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of "black box" simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a "dynamic envelope" of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states. This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target. These results suggest that this is a stable, high

Intelligent open-architecture controller using knowledge server

NASA Astrophysics Data System (ADS)

Nacsa, Janos; Kovacs, George L.; Haidegger, Geza

2001-12-01

In an ideal scenario of intelligent machine tools [22] the human mechanist was almost replaced by the controller. During the last decade many efforts have been made to get closer to this ideal scenario, but the way of information processing within the CNC did not change too much. The paper summarizes the requirements of an intelligent CNC evaluating the different research efforts done in this field using different artificial intelligence (AI) methods. The need for open CNC architecture was emerging at many places around the world. The second part of the paper introduces and shortly compares these efforts. In the third part a low cost concept for intelligent and open systems named Knowledge Server for Controllers (KSC) is introduced. It allows more devices to solve their intelligent processing needs using the same server that is capable to process intelligent data. In the final part the KSC concept is used in an open CNC environment to build up some elements of an intelligent CNC. The preliminary results of the implementation are also introduced.

Potential for Improved Intelligence Quotient Using Volumetric Modulated Arc Therapy Compared With Conventional 3-Dimensional Conformal Radiation for Whole-Ventricular Radiation in Children

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

Qi, X. Sharon, E-mail: xqi@mednet.ucla.edu; Department of Radiation Oncology, University of Colorado Denver, Aurora, Colorado; Stinauer, Michelle

Purpose: To compare volumetric modulated arc therapy (VMAT) with 3-dimensional conformal radiation therapy (3D-CRT) in the treatment of localized intracranial germinoma. We modeled the effect of the dosimetric differences on intelligence quotient (IQ). Method and Materials: Ten children with intracranial germinomas were used for planning. The prescription doses were 23.4 Gy to the ventricles followed by 21.6 Gy to the tumor located in the pineal region. For each child, a 3D-CRT and full arc VMAT was generated. Coverage of the target was assessed by computing a conformity index and heterogeneity index. We also generated VMAT plans with explicit temporal lobemore » sparing and with smaller ventricular margin expansions. Mean dose to the temporal lobe was used to estimate IQ 5 years after completion of radiation, using a patient age of 10 years. Results: Compared with the 3D-CRT plan, VMAT improved conformality (conformity index 1.10 vs 1.85), with slightly higher heterogeneity (heterogeneity index 1.09 vs 1.06). The averaged mean doses for left and right temporal lobes were 31.3 and 31.7 Gy, respectively, for VMAT plans and 37.7 and 37.6 Gy for 3D-CRT plans. This difference in mean temporal lobe dose resulted in an estimated IQ difference of 3.1 points at 5 years after radiation therapy. When the temporal lobes were explicitly included in the VMAT optimization, the mean temporal lobe dose was reduced 5.6-5.7 Gy, resulting in an estimated IQ difference of an additional 3 points. Reducing the ventricular margin from 1.5 cm to 0.5 cm decreased mean temporal lobe dose 11.4-13.1 Gy, corresponding to an estimated increase in IQ of 7 points. Conclusion: For treatment of children with intracranial pure germinomas, VMAT compared with 3D-CRT provides increased conformality and reduces doses to normal tissue. This may result in improvements in IQ in these children.« less

Integrated intelligent sensor for the textile industry

NASA Astrophysics Data System (ADS)

Peltie, Philippe; David, Dominique

1996-08-01

A new sensor has been developed for pantyhose inspection. Unlike a first complete inspection machine devoted to post- manufacturing control of the whole panty, this sensor will be directly integrated on currently existing manufacturing machines, and will combine advantages of miniaturization is to design an intelligent, compact and very cheap product, which should be integrated without requiring any modifications of host machines. The sensor part was designed to achieve closed acquisition, and various solutions have been explored to maintain adequate depth of field. The illumination source will be integrated in the device. The processing part will include correction facilities and electronic processing. Finally, high-level information will be output in order to interface directly with the manufacturing machine automate.

Encephalization quotients and life-history traits in the Sirenia

USGS Publications Warehouse

O'Shea, T.J.; Reep, R.L.

1990-01-01

Relative brain size in the Sirenia is unusually small. Encephalization quotients are 0.27 for Florida manatees (Trichechus manatus) and 0.38 for dugongs (Dugong dugon). Estimates for Steller's sea cow (Hydrodamalis gigas) range from 0.12 to 0.19. These values are among the lowest known for Recent mammals, and seemingly have changed little since the Eocene. A body plan specialized for the aquatic environment does not account for low encephalization quotients; values are substantially less than predicted based on cetacean or pinniped allometry. Life-history, ecological, and behavioral traits of the Sirenia are typical of relatively large-brained species. Low quality food and a low metabolic rate, however, are characteristic of the Sirenia and other small-brained mammals. Acting through prolonged postnatal growth, selection also likely favored large body size in the Sirenia without a correlated increase in brain size.

Tree form quotients as variables in volume estimation.

Treesearch

Gerald E. Hoyer

1985-01-01

The study reviews Hohenadl's procedure for defining form quotients and tree volume from diameters measured at fixed proportions of total tree height. Modifications of Hohenadl's procedure were applied to two sets of data for western hemlock (Tsuga heterophylla (Raf.) Sarg.) from the Pacific Northwest. The procedure was used to define...

Artificial intelligence in medicine.

PubMed

Hamet, Pavel; Tremblay, Johanne

2017-04-01

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application. Copyright © 2017. Published by Elsevier Inc.

Building intelligent systems: Artificial intelligence research at NASA Ames Research Center

NASA Technical Reports Server (NTRS)

Friedland, P.; Lum, H.

1987-01-01

The basic components that make up the goal of building autonomous intelligent systems are discussed, and ongoing work at the NASA Ames Research Center is described. It is noted that a clear progression of systems can be seen through research settings (both within and external to NASA) to Space Station testbeds to systems which actually fly on the Space Station. The starting point for the discussion is a truly autonomous Space Station intelligent system, responsible for a major portion of Space Station control. Attention is given to research in fiscal 1987, including reasoning under uncertainty, machine learning, causal modeling and simulation, knowledge from design through operations, advanced planning work, validation methodologies, and hierarchical control of and distributed cooperation among multiple knowledge-based systems.

Building intelligent systems - Artificial intelligence research at NASA Ames Research Center

NASA Technical Reports Server (NTRS)

Friedland, Peter; Lum, Henry

1987-01-01

The basic components that make up the goal of building autonomous intelligent systems are discussed, and ongoing work at the NASA Ames Research Center is described. It is noted that a clear progression of systems can be seen through research settings (both within and external to NASA) to Space Station testbeds to systems which actually fly on the Space Station. The starting point for the discussion is a 'truly' autonomous Space Station intelligent system, responsible for a major portion of Space Station control. Attention is given to research in fiscal 1987, including reasoning under uncertainty, machine learning, causal modeling and simulation, knowledge from design through operations, advanced planning work, validation methodologies, and hierarchical control of and distributed cooperation among multiple knowledge-based systems.

Vocabulary Is an Appropriate Measure of Premorbid Intelligence in a Sample with Heterogeneous Educational Level in Brazil

PubMed Central

de Oliveira, Maira Okada; Nitrini, Ricardo; Yassuda, Mônica Sanches; Brucki, Sonia Maria Dozzi

2014-01-01

Crystallized intelligence refers to one's knowledge base and can be measured by vocabulary tests. Fluid intelligence is related to nonverbal aspects of intelligence, depends very little on previously acquired knowledge, and can be measured by tests such as Block Design (BD) and Raven Colored Matrices (RCM). Premorbid intelligence quotient (IQ) refers to one's intellectual ability level previous to the onset of disorders like mild cognitive impairment (MCI) and Alzheimer's disease (AD) and it is important to estimate disease severity. The objective was to compare performance in tests that measure crystallized and fluid intelligence in healthy subjects and patients with amnestic MCI (aMCI) and AD. One hundred forty-four participants (aMCI (n = 38), AD (n = 45), and healthy controls (n = 61)) were submitted to neuropsychological tests (WAIS-III vocabulary, BD, and RCM). There were significant among groups, except for vocabulary, indicating a relative stability of crystallized intelligence in the continuum from normal to pathological cognitive decline. Vocabulary seems to be stable during the progression of the disease and useful as a measure of premorbid intelligence, that is, to estimate previous function in relation to the level of education and, as a collateral measure of cognition in people with low education. PMID:24803737

Vocabulary is an appropriate measure of premorbid intelligence in a sample with heterogeneous educational level in Brazil.

PubMed

de Oliveira, Maira Okada; Nitrini, Ricardo; Yassuda, Mônica Sanches; Brucki, Sonia Maria Dozzi

2014-01-01

Crystallized intelligence refers to one's knowledge base and can be measured by vocabulary tests. Fluid intelligence is related to nonverbal aspects of intelligence, depends very little on previously acquired knowledge, and can be measured by tests such as Block Design (BD) and Raven Colored Matrices (RCM). Premorbid intelligence quotient (IQ) refers to one's intellectual ability level previous to the onset of disorders like mild cognitive impairment (MCI) and Alzheimer's disease (AD) and it is important to estimate disease severity. The objective was to compare performance in tests that measure crystallized and fluid intelligence in healthy subjects and patients with amnestic MCI (aMCI) and AD. One hundred forty-four participants (aMCI (n = 38), AD (n = 45), and healthy controls (n = 61)) were submitted to neuropsychological tests (WAIS-III vocabulary, BD, and RCM). There were significant among groups, except for vocabulary, indicating a relative stability of crystallized intelligence in the continuum from normal to pathological cognitive decline. Vocabulary seems to be stable during the progression of the disease and useful as a measure of premorbid intelligence, that is, to estimate previous function in relation to the level of education and, as a collateral measure of cognition in people with low education.

Machine Learning. Part 1. A Historical and Methodological Analysis.

DTIC Science & Technology

1983-05-31

Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern Al systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. Part 1 of this paper presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by

Psychometric Properties of the Autism-Spectrum Quotient for Assessing Low and High Levels of Autistic Traits in College Students

ERIC Educational Resources Information Center

Stevenson, Jennifer L.; Hart, Kari R.

2017-01-01

The current study systematically investigated the effects of scoring and categorization methods on the psychometric properties of the Autism-Spectrum Quotient. Four hundred and three college students completed the Autism-Spectrum Quotient at least once. Total scores on the Autism-Spectrum Quotient had acceptable internal consistency and…

Comparing closed quotient in children singers' voices as measured by high-speed-imaging, electroglottography, and inverse filtering.

PubMed

Mecke, Ann-Christine; Sundberg, Johan; Granqvist, Svante; Echternach, Matthias

2012-01-01

The closed quotient, i.e., the ratio between the closed phase and the period, is commonly studied in voice research. However, the term may refer to measures derived from different methods, such as inverse filtering, electroglottography or high-speed digital imaging (HSDI). This investigation compares closed quotient data measured by these three methods in two boy singers. Each singer produced sustained tones on two different pitches and a glissando. Audio, electroglottographic signal (EGG), and HSDI were recorded simultaneously. The audio signal was inverse filtered by means of the decap program; the closed phase was defined as the flat minimum portion of the flow glottogram. Glottal area was automatically measured in the high speed images by the built-in camera software, and the closed phase was defined as the flat minimum portion of the area-signal. The EGG-signal was analyzed in four different ways using the matlab open quotient interface. The closed quotient data taken from the EGG were found to be considerably higher than those obtained from inverse filtering. Also, substantial differences were found between the closed quotient derived from HSDI and those derived from inverse filtering. The findings illustrate the importance of distinguishing between these quotients. © 2012 Acoustical Society of America.

[Artificial intelligence in psychiatry-an overview].

PubMed

Meyer-Lindenberg, A

2018-06-18

Artificial intelligence and the underlying methods of machine learning and neuronal networks (NN) have made dramatic progress in recent years and have allowed computers to reach superhuman performance in domains that used to be thought of as uniquely human. In this overview, the underlying methodological developments that made this possible are briefly delineated and then the applications to psychiatry in three domains are discussed: precision medicine and biomarkers, natural language processing and artificial intelligence-based psychotherapeutic interventions. In conclusion, some of the risks of this new technology are mentioned.

"The Intimate Machine"--30 Years On

ERIC Educational Resources Information Center

Frude, Neil; Jandric, Petar

2015-01-01

This conversation focuses on a book published in 1983 that examined "animism," the tendency to regard non-living entities as living and sentient. "The Intimate Machine" suggested that animism will be fully exploited by artificial intelligence (AI) and robotics, generating artefacts that will engage the user in…

[INVITED] Computational intelligence for smart laser materials processing

NASA Astrophysics Data System (ADS)

Casalino, Giuseppe

2018-03-01

Computational intelligence (CI) involves using a computer algorithm to capture hidden knowledge from data and to use them for training ;intelligent machine; to make complex decisions without human intervention. As simulation is becoming more prevalent from design and planning to manufacturing and operations, laser material processing can also benefit from computer generating knowledge through soft computing. This work is a review of the state-of-the-art on the methodology and applications of CI in laser materials processing (LMP), which is nowadays receiving increasing interest from world class manufacturers and 4.0 industry. The focus is on the methods that have been proven effective and robust in solving several problems in welding, cutting, drilling, surface treating and additive manufacturing using the laser beam. After a basic description of the most common computational intelligences employed in manufacturing, four sections, namely, laser joining, machining, surface, and additive covered the most recent applications in the already extensive literature regarding the CI in LMP. Eventually, emerging trends and future challenges were identified and discussed.

The Relationship between Intelligence and Anxiety: An Association with Subcortical White Matter Metabolism.

PubMed

Coplan, Jeremy D; Hodulik, Sarah; Mathew, Sanjay J; Mao, Xiangling; Hof, Patrick R; Gorman, Jack M; Shungu, Dikoma C

2011-01-01

We have demonstrated in a previous study that a high degree of worry in patients with generalized anxiety disorder (GAD) correlates positively with intelligence and that a low degree of worry in healthy subjects correlates positively with intelligence. We have also shown that both worry and intelligence exhibit an inverse correlation with certain metabolites in the subcortical white matter. Here we re-examine the relationships among generalized anxiety, worry, intelligence, and subcortical white matter metabolism in an extended sample. Results from the original study were combined with results from a second study to create a sample comprised of 26 patients with GAD and 18 healthy volunteers. Subjects were evaluated using the Penn State Worry Questionnaire, the Wechsler Brief intelligence quotient (IQ) assessment, and proton magnetic resonance spectroscopic imaging ((1)H-MRSI) to measure subcortical white matter metabolism of choline and related compounds (CHO). Patients with GAD exhibited higher IQ's and lower metabolite concentrations of CHO in the subcortical white matter in comparison to healthy volunteers. When data from GAD patients and healthy controls were combined, relatively low CHO predicted both relatively higher IQ and worry scores. Relatively high anxiety in patients with GAD predicted high IQ whereas relatively low anxiety in controls also predicted high IQ. That is, the relationship between anxiety and intelligence was positive in GAD patients but inverse in healthy volunteers. The collective data suggest that both worry and intelligence are characterized by depletion of metabolic substrate in the subcortical white matter and that intelligence may have co-evolved with worry in humans.

A Rather Intelligent Language Teacher.

ERIC Educational Resources Information Center

Cerri, Stefano; Breuker, Joost

1981-01-01

Characteristics of DART (Didactic Augmented Recursive Transition), an ATN-based system for writing intelligent computer assisted instruction (ICAI) programs that is available on the PLATO system are described. DART allows writing programs in an ATN dialect, compiling them in machine code for the PLATO system, and executing them as if the original…

No association between prenatal exposure to psychotropics and intelligence at age five.

PubMed

Eriksen, Hanne-Lise Falgreen; Kesmodel, Ulrik Schiøler; Pedersen, Lars Henning; Mortensen, Erik Lykke

2015-05-01

To examine associations between prenatal exposure to selective serotonin reuptake inhibitors (SSRIs)/anxiolytics and intelligence assessed with a standard clinical intelligence test at age 5 years. Longitudinal follow-up study. Denmark, 2003-2008. A total of 1780 women and their children sampled from the Danish National Birth Cohort. Self-reported information on use of SSRI and anxiolytics was obtained from the Danish National Birth Cohort at the time of consent and from two prenatal interviews. Intelligence was assessed at age 5 years, and parental education, maternal intelligence quotient (IQ), maternal smoking and alcohol consumption in pregnancy, the child's age at testing, sex, and tester were included in the full model. The IQ of 13 medication-exposed children was compared with the IQ of 19 children whose mothers had untreated depression and 1748 control children. Wechsler Preschool and Primary Scale of Intelligence - Revised. In unadjusted analyses, children of mothers who used antidepressants or anxiolytics during pregnancy had higher verbal IQ; this association, however, was insignificant after adjustment for potentially confounding maternal and child factors. No consistent associations between IQ and fetal exposure to antidepressants and anxiolytics were observed, but the study had low statistical power, and there is an obvious need to conduct long-term follow-up studies with comprehensive cognitive assessment and sufficiently large samples of adolescent or adult offspring. © 2015 Nordic Federation of Societies of Obstetrics and Gynecology.

White matter tract integrity and intelligence in patients with mental retardation and healthy adults.

PubMed

Yu, Chunshui; Li, Jun; Liu, Yong; Qin, Wen; Li, Yonghui; Shu, Ni; Jiang, Tianzi; Li, Kuncheng

2008-05-01

It is well known that brain structures correlate with intelligence but the association between the integrity of brain white matter tracts and intelligence in patients with mental retardation (MR) and healthy adults remains unknown. The aims of this study are to investigate whether the integrity of corpus callosum (CC), cingulum, uncinate fasciculus (UF), optic radiation (OR) and corticospinal tract (CST) are damaged in patients with MR, and to determine the correlations between the integrity of these tracts and full scale intelligence quotient (FSIQ) in both patients and controls. Fifteen MR patients and 79 healthy controls underwent intelligence tests and diffusion tensor imaging examinations. According to the FSIQ, all healthy controls were divided into general intelligence (GI: FSIQ<120; n=42) and high intelligence (HI: FSIQ> or =120; n=37) groups. Intelligence was assessed by Chinese Revised Wechsler Adult Intelligence Scale, and white matter tract integrity was assessed by fractional anisotropy (FA). MR patients showed significantly lower FA than healthy controls in the CC, UF, OR and CST. However, GI subjects only demonstrated lower FA than HI subjects in the right UF. Partial correlation analysis controlling for age and sex showed that FSIQ scores were significantly correlated with the FA of the bilateral UF, genu and truncus of CC, bilateral OR and left CST. While FSIQ scores were only significantly correlated with the FA of the right UF when further controlling for group. This study indicate that MR patients show extensive damage in the integrity of the brain white matter tracts, and the right UF is an important neural basis of human intelligence.

Relationship of Attachment Styles and Emotional Intelligence With Marital Satisfaction

PubMed Central

Kamel Abbasi, Amir Reza; Tabatabaei, Seyed Mahmoud; Aghamohammadiyan Sharbaf, Hamidreza; Karshki, Hossein

2016-01-01

Background The early relationships between infant and care takers are significant and the emotional interactions of these relationships play an important role in forming personality and adulthood relationships. Objectives The current study aimed to investigate the relationship of attachment styles (AS) and emotional intelligence (EI) with marital satisfaction (MS). Materials and Methods In this cross-sectional research, 450 married people (226 male, 224 female) were selected using multistage sampling method in Mashhad, Iran, in 2011. Subjects completed the attachment styles questionnaire (ASQ), Bar-On emotional quotient inventory (EQ-i) and Enrich marital satisfaction questionnaire. Results The results indicated that secure attachment style has positive significant relationship with marital satisfaction (r = 0.609, P < 0.001), also avoidant attachment style and ambivalent attachment style have negative significant relationship with marital satisfaction (r = -0.446, r = -0.564) (P < 0.001). Also, attachment styles can significantly predict marital satisfaction (P < 0.001). Therefore, emotional intelligence and its components have positive significant relationship with marital satisfaction; thus, emotional intelligence and intrapersonal, adaptability and general mood components can significantly predict marital satisfaction (P < 0.001). But, interpersonal and stress management components cannot significantly predict marital satisfaction (P > 0.05). Conclusions According to the obtained results, attachment styles and emotional intelligence are the key factors in marital satisfaction that decrease marital disagreement and increase the positive interactions of the couples. PMID:27843473

Relationship of Attachment Styles and Emotional Intelligence With Marital Satisfaction.

PubMed

Kamel Abbasi, Amir Reza; Tabatabaei, Seyed Mahmoud; Aghamohammadiyan Sharbaf, Hamidreza; Karshki, Hossein

2016-09-01

The early relationships between infant and care takers are significant and the emotional interactions of these relationships play an important role in forming personality and adulthood relationships. The current study aimed to investigate the relationship of attachment styles (AS) and emotional intelligence (EI) with marital satisfaction (MS). In this cross-sectional research, 450 married people (226 male, 224 female) were selected using multistage sampling method in Mashhad, Iran, in 2011. Subjects completed the attachment styles questionnaire (ASQ), Bar-On emotional quotient inventory (EQ-i) and Enrich marital satisfaction questionnaire. The results indicated that secure attachment style has positive significant relationship with marital satisfaction (r = 0.609, P < 0.001), also avoidant attachment style and ambivalent attachment style have negative significant relationship with marital satisfaction (r = -0.446, r = -0.564) (P < 0.001). Also, attachment styles can significantly predict marital satisfaction (P < 0.001). Therefore, emotional intelligence and its components have positive significant relationship with marital satisfaction; thus, emotional intelligence and intrapersonal, adaptability and general mood components can significantly predict marital satisfaction (P < 0.001). But, interpersonal and stress management components cannot significantly predict marital satisfaction (P > 0.05). According to the obtained results, attachment styles and emotional intelligence are the key factors in marital satisfaction that decrease marital disagreement and increase the positive interactions of the couples.

Emotional Intelligence and its Effect on Pharmacists and Pharmacy Students with Autistic-like Traits

PubMed Central

Higuchi, Yuji; Koyama, Toshihiro; Kitamura, Yoshihisa; Sendo, Toshiaki; Fujimori, Maiko; Kataoka, Hitomi; Hayashibara, Chinatsu; Uchitomi, Yosuke; Yamada, Norihito

2017-01-01

Objective. To measure whether Emotional intelligence (EI) would minimize the negative association between autistic-like traits (ALT) and empathic behavior and enhance the positive association between ALT and psychological distress. Methods. Our sample population included 823 hospital pharmacists belonging to a district society, and 378 pharmacy students. Analyses were performed to examine relationships between scores on the Emotional Intelligence Scale (EQS), Autism-Spectrum Quotient (AQ), Jefferson Scale of Empathy (JSE), and General Health Questionnaire-12 (GHQ). Results. Complete answers were obtained from 373 pharmacists, and 341 students. EQS partially intervened the associations between AQ and JSE and between AQ and GHQ. Conclusion. EI partially intervened the relationships between ALT and empathy, and between ALT and mental health, both of which are necessary for optimal pharmaceutical practice. PMID:28630515

A Starter's Guide to Artificial Intelligence.

ERIC Educational Resources Information Center

McConnell, Barry A.; McConnell, Nancy J.

1988-01-01

Discussion of the history and development of artificial intelligence (AI) highlights a bibliography of introductory books on various aspects of AI, including AI programing; problem solving; automated reasoning; game playing; natural language; expert systems; machine learning; robotics and vision; critics of AI; and representative software. (LRW)

Predicting asthma exacerbations using artificial intelligence.

PubMed

Finkelstein, Joseph; Wood, Jeffrey

2013-01-01

Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.

VBM-DTI correlates of verbal intelligence: a potential link to Broca's area.

PubMed

Konrad, Andreas; Vucurevic, Goran; Musso, Francesco; Winterer, Georg

2012-04-01

Human brain lesion studies first investigated the biological roots of cognitive functions including language in the late 1800s. Neuroimaging studies have reported correlation findings with general intelligence predominantly in fronto-parietal cortical areas. However, there is still little evidence about the relationship between verbal intelligence and structural properties of the brain. We predicted that verbal performance is related to language regions of Broca's and Wernicke's areas. Verbal intelligence quotient (vIQ) was assessed in 30 healthy young subjects. T1-weighted MRI and diffusion tensor imaging data sets were acquired. Voxel-wise regression analyses were used to correlate fractional anisotropy (FA) and mean diffusivity values with vIQ. Moreover, regression analyses of regional brain volume with vIQ were performed adopting voxel-based morphometry (VBM) and ROI methodology. Our analyses revealed a significant negative correlation between vIQ and FA and a significant positive correlation between vIQ and mean diffusivity in the left-hemispheric Broca's area. VBM regression analyses did not show significant results, whereas a subsequent ROI analysis of Broca's area FA peak cluster demonstrated a positive correlation of gray matter volume and vIQ. These findings suggest that cortical thickness in Broca's area contributes to verbal intelligence. Diffusion parameters predicted gray matter ratio in Broca's area more sensitive than VBM methodology.

Intelligent robot trends and predictions for the new millennium

NASA Astrophysics Data System (ADS)

Hall, Ernest L.; Mundhenk, Terrell N.

1999-08-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The current use of these machines in outer space, medicine, hazardous materials, defense applications and industry is being pursued with vigor but little funding. In factory automation such robotics machines can improve productivity, increase product quality and improve competitiveness. The computer and the robot have both been developed during recent times. The intelligent robot combines both technologies and requires a thorough understanding and knowledge of mechatronics. In honor of the new millennium, this paper will present a discussion of futuristic trends and predictions. However, in keeping with technical tradition, a new technique for 'Follow the Leader' will also be presented in the hope of it becoming a new, useful and non-obvious technique.

Matrix Multiplication Algorithm Selection with Support Vector Machines

DTIC Science & Technology

2015-05-01

libraries that could intelligently choose the optimal algorithm for a particular set of inputs. Users would be oblivious to the underlying algorithmic...SAT.” J. Artif . Intell. Res.(JAIR), vol. 32, pp. 565–606, 2008. [9] M. G. Lagoudakis and M. L. Littman, “Algorithm selection using reinforcement...Artificial Intelligence , vol. 21, no. 05, pp. 961–976, 2007. [15] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM

Computational aerodynamics and artificial intelligence

NASA Technical Reports Server (NTRS)

Mehta, U. B.; Kutler, P.

1984-01-01

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

Visible Machine Learning for Biomedicine.

PubMed

Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; Ideker, Trey

2018-06-14

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology. Copyright © 2018. Published by Elsevier Inc.

Beyond the Floor Effect on the Wechsler Intelligence Scale for Children-4th Ed. (WISC-IV): Calculating IQ and Indexes of Subjects Presenting a Floored Pattern of Results

ERIC Educational Resources Information Center

Orsini, A.; Pezzuti, L.; Hulbert, S.

2015-01-01

Background: It is now widely known that children with severe intellectual disability show a 'floor effect' on the Wechsler scales. This effect emerges because the practice of transforming raw scores into scaled scores eliminates any variability present in participants with low intellectual ability and because intelligence quotient (IQ) scores are…

The role of cognitive versus emotional intelligence in Iowa Gambling Task performance: What's emotion got to do with it?

PubMed

Webb, Christian A; DelDonno, Sophie; Killgore, William D S

2014-01-01

Debate persists regarding the relative role of cognitive versus emotional processes in driving successful performance on the widely used Iowa Gambling Task (IGT). From the time of its initial development, patterns of IGT performance were commonly interpreted as primarily reflecting implicit, emotion-based processes. Surprisingly, little research has tried to directly compare the extent to which measures tapping relevant cognitive versus emotional competencies predict IGT performance in the same study. The current investigation attempts to address this question by comparing patterns of associations between IGT performance, cognitive intelligence (Wechsler Abbreviated Scale of Intelligence; WASI) and three commonly employed measures of emotional intelligence (EI; Mayer-Salovey-Caruso Emotional Intelligence Test, MSCEIT; Bar-On Emotional Quotient Inventory, EQ-i; Self-Rated Emotional Intelligence Scale, SREIS). Results indicated that IGT performance was more strongly associated with cognitive, than emotional, intelligence. To the extent that the IGT indeed mimics "real-world" decision-making, our findings, coupled with the results of existing research, may highlight the role of deliberate, cognitive capacities over implicit, emotional processes in contributing to at least some domains of decision-making relevant to everyday life.

Man/Machine Interaction Dynamics And Performance (MMIDAP) capability

NASA Technical Reports Server (NTRS)

Frisch, Harold P.

1991-01-01

The creation of an ability to study interaction dynamics between a machine and its human operator can be approached from a myriad of directions. The Man/Machine Interaction Dynamics and Performance (MMIDAP) project seeks to create an ability to study the consequences of machine design alternatives relative to the performance of both machine and operator. The class of machines to which this study is directed includes those that require the intelligent physical exertions of a human operator. While Goddard's Flight Telerobotic's program was expected to be a major user, basic engineering design and biomedical applications reach far beyond telerobotics. Ongoing efforts are outlined of the GSFC and its University and small business collaborators to integrate both human performance and musculoskeletal data bases with analysis capabilities necessary to enable the study of dynamic actions, reactions, and performance of coupled machine/operator systems.

Research on intelligent monitoring technology of machining process

NASA Astrophysics Data System (ADS)

Wang, Taiyong; Meng, Changhong; Zhao, Guoli

1995-08-01

Based upon research on sound and vibration characteristics of tool condition, we explore the multigrade monitoring system which takes single-chip microcomputers as the core hardware. By using the specially designed pickup true signal devices, we can more effectively do the intelligent multigrade monitoring and forecasting, and furthermore, we can build the tool condition models adaptively. This is the key problem in FMS, CIMS, and even the IMS.

Machine learning in cardiovascular medicine: are we there yet?

PubMed

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The role of genes, intelligence, personality, and social engagement in cognitive performance in Klinefelter syndrome.

PubMed

Skakkebæk, Anne; Moore, Philip J; Pedersen, Anders Degn; Bojesen, Anders; Kristensen, Maria Krarup; Fedder, Jens; Laurberg, Peter; Hertz, Jens Michael; Østergaard, John Rosendahl; Wallentin, Mikkel; Gravholt, Claus Højbjerg

2017-03-01

The determinants of cognitive deficits among individuals with Klinefelter syndrome (KS) are not well understood. This study was conducted to assess the impact of general intelligence, personality, and social engagement on cognitive performance among patients with KS and a group of controls matched for age and years of education. Sixty-nine patients with KS and 69 controls were assessed in terms of IQ, NEO personality inventory, the Autism Spectrum Quotient (AQ) scale, and measures of cognitive performance reflecting working memory and executive function. Patients with KS performed more poorly on memory and executive-function tasks. Patients with KS also exhibited greater neuroticism and less extraversion, openness, and conscientiousness than controls. Memory deficits among patients with KS were associated with lower intelligence, while diminished executive functioning was mediated by both lower intelligence and less social engagement. Our results suggest that among patients with KS, memory deficits are principally a function of lower general intelligence, while executive-function deficits are associated with both lower intelligence and poorer social skills. This suggests a potential influence of social engagement on executive cognitive functioning (and/or vice-versa) among individuals with KS, and perhaps those with other genetic disorders. Future longitudinal research would be important to further clarify this and other issues discussed in this research.

Further Structural Intelligence for Sensors Cluster Technology in Manufacturing

PubMed Central

Mekid, Samir

2006-01-01

With the ever increasing complex sensing and actuating tasks in manufacturing plants, intelligent sensors cluster in hybrid networks becomes a rapidly expanding area. They play a dominant role in many fields from macro and micro scale. Global object control and the ability to self organize into fault-tolerant and scalable systems are expected for high level applications. In this paper, new structural concepts of intelligent sensors and networks with new intelligent agents are presented. Embedding new functionalities to dynamically manage cooperative agents for autonomous machines are interesting key enabling technologies most required in manufacturing for zero defects production.

Flexibility in Mathematics Problem Solving Based on Adversity Quotient

NASA Astrophysics Data System (ADS)

Dina, N. A.; Amin, S. M.; Masriyah

2018-01-01

Flexibility is an ability which is needed in problem solving. One of the ways in problem solving is influenced by Adversity Quotient (AQ). AQ is the power of facing difficulties. There are three categories of AQ namely climber, camper, and quitter. This research is a descriptive research using qualitative approach. The aim of this research is to describe flexibility in mathematics problem solving based on Adversity Quotient. The subjects of this research are climber student, camper student, and quitter student. This research was started by giving Adversity Response Profile (ARP) questioner continued by giving problem solving task and interviews. The validity of data measurement was using time triangulation. The results of this research shows that climber student uses two strategies in solving problem and doesn’t have difficulty. The camper student uses two strategies in solving problem but has difficulty to finish the second strategies. The quitter student uses one strategy in solving problem and has difficulty to finish it.

Demographic and Lifestyle Characteristics, but Not Apolipoprotein E Genotype, Are Associated with Intelligence among Young Chinese College Students.

PubMed

Chen, Xiao-Fen; Wei, Zichen; Wang, Tingting; Zhang, Zhen-Lian; Wang, Yiwei; Heckman, Michael G; Diehl, Nancy N; Zhang, Yun-Wu; Xu, Huaxi; Bu, Guojun

2015-01-01

Intelligence is an important human feature that strongly affects many life outcomes, including health, life-span, income, educational and occupational attainments. People at all ages differ in their intelligence but the origins of these differences are much debated. A variety of environmental and genetic factors have been reported to be associated with individual intelligence, yet their nature and contribution to intelligence differences have been controversial. To investigate the contribution of apolipoprotein E (APOE) genotype, which is associated with the risk for Alzheimer's disease, as well as demographic and lifestyle characteristics, to the variation in intelligence. A total of 607 Chinese college students aged 18 to 25 years old were included in this prospective observational study. The Chinese revision of Wechsler Adult Intelligence Scale (the fourth edition, short version) was used to determine the intelligence level of participants. Demographic and lifestyle characteristics data were obtained from self-administered questionnaires. No significant association was found between APOE polymorphic alleles and different intelligence quotient (IQ) measures. Interestingly, a portion of demographic and lifestyle characteristics, including age, smoking and sleep quality were significantly associated with different IQ measures. Our findings indicate that demographic features and lifestyle characteristics, but not APOE genotype, are associated with intelligence measures among young Chinese college students. Thus, although APOE ε4 allele is a strong genetic risk factor for Alzheimer's disease, it does not seem to impact intelligence at young ages.

Fantastic Journey through Minds and Machines.

ERIC Educational Resources Information Center

Muir, Michael

Intended for learners with a basic familiarity with the Logo programming language, this manual is designed to introduce them to artificial intelligence and enhance their programming capabilities. Nine chapters discuss the following features of Logo: (1) MAZE.MASTER, a look at robots and how sensors make machines aware of their environment; (2)…

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Intelligent judgements over health risks in a spatial agent-based model.

PubMed

Abdulkareem, Shaheen A; Augustijn, Ellen-Wien; Mustafa, Yaseen T; Filatova, Tatiana

2018-03-20

Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of

The role of cognitive versus emotional intelligence in Iowa Gambling Task performance: What’s emotion got to do with it?

PubMed Central

Webb, Christian A.; DelDonno, Sophie; Killgore, William D.S.

2014-01-01

Debate persists regarding the relative role of cognitive versus emotional processes in driving successful performance on the widely used Iowa Gambling Task (IGT). From the time of its initial development, patterns of IGT performance were commonly interpreted as primarily reflecting implicit, emotion-based processes. Surprisingly, little research has tried to directly compare the extent to which measures tapping relevant cognitive versus emotional competencies predict IGT performance in the same study. The current investigation attempts to address this question by comparing patterns of associations between IGT performance, cognitive intelligence (Wechsler Abbreviated Scale of Intelligence; WASI) and three commonly employed measures of emotional intelligence (EI; Mayer–Salovey–Caruso Emotional Intelligence Test, MSCEIT; Bar-On Emotional Quotient Inventory, EQ-i; Self-Rated Emotional Intelligence Scale, SREIS). Results indicated that IGT performance was more strongly associated with cognitive, than emotional, intelligence. To the extent that the IGT indeed mimics “real-world” decision-making, our findings, coupled with the results of existing research, may highlight the role of deliberate, cognitive capacities over implicit, emotional processes in contributing to at least some domains of decision-making relevant to everyday life. PMID:25635149

The Autism Spectrum Quotient: Children's Version (AQ-Child)

ERIC Educational Resources Information Center

Auyeung, Bonnie; Baron-Cohen, Simon; Wheelwright, Sally; Allison, Carrie

2008-01-01

The Autism Spectrum Quotient-Children's Version (AQ-Child) is a parent-report questionnaire that aims to quantify autistic traits in children 4-11 years old. The range of scores on the AQ-Child is 0-150. It was administered to children with an autism spectrum condition (ASC) (n = 540) and a general population sample (n = 1,225). Results showed a…

Development of intelligent robots - Achievements and issues

NASA Astrophysics Data System (ADS)

Nitzan, D.

1985-03-01

A flexible, intelligent robot is regarded as a general purpose machine system that may include effectors, sensors, computers, and auxiliary equipment and, like a human, can perform a variety of tasks under unpredictable conditions. Development of intelligent robots is essential for increasing the growth rate of today's robot population in industry and elsewhere. Robotics research and development topics include manipulation, end effectors, mobility, sensing (noncontact and contact), adaptive control, robot programming languages, and manufacturing process planning. Past achievements and current issues related to each of these topics are described briefly.

The twelfth annual Intelligent Ground Vehicle Competition: team approaches to intelligent vehicles

NASA Astrophysics Data System (ADS)

Theisen, Bernard L.; Maslach, Daniel

2004-10-01

The Intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990s. The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics, and mobile platform fundamentals to design and build an unmanned system. Both U.S. and international teams focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 12 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 43 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the three-day competition are highlighted. Finally, an assessment of the competition based on participant feedback is presented.

Turning assistive machines into assistive robots

NASA Astrophysics Data System (ADS)

Argall, Brenna D.

2015-01-01

For decades, the potential for automation in particular, in the form of smart wheelchairs to aid those with motor, or cognitive, impairments has been recognized. It is a paradox that often the more severe a person's motor impairment, the more challenging it is for them to operate the very assistive machines which might enhance their quality of life. A primary aim of my lab is to address this confound by incorporating robotics autonomy and intelligence into assistive machines turning the machine into a kind of robot, and offloading some of the control burden from the user. Robots already synthetically sense, act in and reason about the world, and these technologies can be leveraged to help bridge the gap left by sensory, motor or cognitive impairments in the users of assistive machines. This paper overviews some of the ongoing projects in my lab, which strives to advance human ability through robotics autonomy.

Quantum Machine Learning

NASA Technical Reports Server (NTRS)

Biswas, Rupak

2018-01-01

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools.

PubMed

Price, Jeffrey H; Goodacre, Angela; Hahn, Klaus; Hodgson, Louis; Hunter, Edward A; Krajewski, Stanislaw; Murphy, Robert F; Rabinovich, Andrew; Reed, John C; Heynen, Susanne

2002-01-01

Cellular behavior is complex. Successfully understanding systems at ever-increasing complexity is fundamental to advances in modern science and unraveling the functional details of cellular behavior is no exception. We present a collection of prospectives to provide a glimpse of the techniques that will aid in collecting, managing and utilizing information on complex cellular processes via molecular imaging tools. These include: 1) visualizing intracellular protein activity with fluorescent markers, 2) high throughput (and automated) imaging of multilabeled cells in statistically significant numbers, and 3) machine intelligence to analyze subcellular image localization and pattern. Although not addressed here, the importance of combining cell-image-based information with detailed molecular structure and ligand-receptor binding models cannot be overlooked. Advanced molecular imaging techniques have the potential to impact cellular diagnostics for cancer screening, clinical correlations of tissue molecular patterns for cancer biology, and cellular molecular interactions for accelerating drug discovery. The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry. Copyright 2002 Wiley-Liss, Inc.

When Machines Think: Radiology's Next Frontier.

PubMed

Dreyer, Keith J; Geis, J Raymond

2017-12-01

Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans' abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017.

Blindness in designing intelligent systems

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1988-01-01

New investigations of the foundations of artificial intelligence are challenging the hypothesis that problem solving is the cornerstone of intelligence. New distinctions among three domains of concern for humans--description, action, and commitment--have revealed that the design process for programmable machines, such as expert systems, is based on descriptions of actions and induces blindness to nonanalytic action and commitment. Design processes focusing in the domain of description are likely to yield programs like burearcracies: rigid, obtuse, impersonal, and unable to adapt to changing circumstances. Systems that learn from their past actions, and systems that organize information for interpretation by human experts, are more likely to be successful in areas where expert systems have failed.

Peripheral nervous control of cold-induced reduction in the respiratory quotient of the rat

NASA Astrophysics Data System (ADS)

Refinetti, Roberto

1990-03-01

Cold-exposed rats show a reduction in the respiratory quotient which is indicative of a relative shift from carbohydrates to lipids as substrates for oxidative metabolism. In the present study, the effects of food deprivation and cold exposure on the respiratory quotient were observed. In addition, the involvement of the three main branches of the peripheral nervous system (sympathetic, parasympathetic, and somatic) was investigated by means of synaptic blockade with propranolol, atropine, and quinine, respectively. Both propranolol and quinine blocked the cold-induced decrease in respiratory quotient and increase in heat production, whereas atropine had only minor and very brief effects. It is concluded that both the sympathetic and somatic branches are involved in the metabolic changes associated with cold-induced thermogenesis and that the increase in metabolic heat production involves a shift from carbohydrate to lipid utilization irrespective of which of the two branches is activated.

Lung ventilation-perfusion imbalance in pulmonary emphysema: assessment with automated V/Q quotient SPECT.

PubMed

Suga, Kazuyoshi; Kawakami, Yasuhiko; Koike, Hiroaki; Iwanaga, Hideyuki; Tokuda, Osamu; Okada, Munemasa; Matsunaga, Naofumi

2010-05-01

Tc-99m-Technegas-MAA single photon emission computed tomography (SPECT)-derived ventilation (V)/perfusion (Q) quotient SPECT was used to assess lung V-Q imbalance in patients with pulmonary emphysema. V/Q quotient SPECT and V/Q profile were automatically built in 38 patients with pulmonary emphysema and 12 controls, and V/Q distribution and V/Q profile parameters were compared. V/Q distribution on V/Q quotient SPECT was correlated with low attenuation areas (LAA) on density-mask computed tomography (CT). Parameters of V/Q profile such as the median, standard deviation (SD), kurtosis and skewness were proposed to objectively evaluate the severity of lung V-Q imbalance. In contrast to uniform V/Q distribution on V/Q quotient SPECT and a sharp peak with symmetrical V/Q distribution on V/Q profile in controls, lung areas showing heterogeneously high or low V/Q and flattened peaks with broadened V/Q distribution were frequently seen in patients with emphysema, including lung areas with only slight LAA. V/Q distribution was also often asymmetric regardless of symmetric LAA. All the proposed parameters of V/Q profile in entire lungs of patients with emphysema showed large variations compared with controls; SD and kurtosis were significantly different from controls (P < 0.0001 and P < 0.001, respectively), and a significant correlation was found between SD and A-aDO2 (P < 0.0001). V/Q quotient SPECT appears to be more sensitive to detect emphysematous lungs compared with morphologic CT in patients with emphysema. SD and kurtosis of V/Q profile can be adequate parameters to assess the severity of lung V-Q imbalance causing gas-exchange impairment in patients with emphysema.

Artificial intelligence (AI) systems for interpreting complex medical datasets.

PubMed

Altman, R B

2017-05-01

Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability. © 2017 ASCPT.

Effect of Developmental Quotient on Symptoms of Inattention and Impulsivity among Toddlers with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Matson, Johnny L.; Mahan, Sara; Hess, Julie A.; Fodstad, Jill C.

2010-01-01

The effect of developmental quotient on symptoms of inattention and impulsivity was examined among 198 toddlers with Autism Spectrum Disorders. There were two levels of developmental quotient: (1) low (less than or equal to 70; n = 80), and (2) typical (greater than 70; n = 118). Symptoms of inattention and impulsivity were assessed using 14 items…

Northeast Artificial Intelligence Consortium (NAIC). Volume 12. Computer Architecture for Very Large Knowledge Bases

DTIC Science & Technology

1990-12-01

data rate to the electronics would be much lower on the average and the data much "richer" in information. Intelligent use of...system bottleneck, a high data rate should be provided by I/O systems. 2. machines with intelligent storage management specially designed for logic...management information processing, surveillance sensors, intelligence data collection and handling, solid state sciences, electromagnetics, and propagation, and electronic reliability/maintainability and compatibility.

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

PubMed

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

Machine intelligence applications to securities production

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

Johnson, C.K.

1987-01-01

The production of security documents provides a cache of interesting problems ranging across a broad spectrum. Some of the problems do not have rigorous scientific solutions available at this time and provide opportunities for less structured approaches such as AI. AI methods can be used in conjunction with traditional scientific and computational methods. The most productive applications of AI occur when this marriage of methods can be carried out without motivation to prove that one method is better than the other. Fields such as ink chemistry and technology, and machine inspection of graphic arts printing offer interesting challenges which willmore » continue to intrigue current and future generations of researchers into the 21st century.« less

Intelligent software for laboratory automation.

PubMed

Whelan, Ken E; King, Ross D

2004-09-01

The automation of laboratory techniques has greatly increased the number of experiments that can be carried out in the chemical and biological sciences. Until recently, this automation has focused primarily on improving hardware. Here we argue that future advances will concentrate on intelligent software to integrate physical experimentation and results analysis with hypothesis formulation and experiment planning. To illustrate our thesis, we describe the 'Robot Scientist' - the first physically implemented example of such a closed loop system. In the Robot Scientist, experimentation is performed by a laboratory robot, hypotheses concerning the results are generated by machine learning and experiments are allocated and selected by a combination of techniques derived from artificial intelligence research. The performance of the Robot Scientist has been evaluated by a rediscovery task based on yeast functional genomics. The Robot Scientist is proof that the integration of programmable laboratory hardware and intelligent software can be used to develop increasingly automated laboratories.

Entanglement-Based Machine Learning on a Quantum Computer

NASA Astrophysics Data System (ADS)

Cai, X.-D.; Wu, D.; Su, Z.-E.; Chen, M.-C.; Wang, X.-L.; Li, Li; Liu, N.-L.; Lu, C.-Y.; Pan, J.-W.

2015-03-01

Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential.

PubMed

Das, Nilakash; Topalovic, Marko; Janssens, Wim

2018-03-01

The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases. Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies. Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.

Design Of An Intelligent Robotic System Organizer Via Expert System Tecniques

NASA Astrophysics Data System (ADS)

Yuan, Peter H.; Valavanis, Kimon P.

1989-02-01

Intelligent Robotic Systems are a special type of Intelligent Machines. When modeled based on Vle theory of Intelligent Controls, they are composed of three interactive levels, namely: organization, coordination, and execution, ordered according, to the ,Principle of Increasing, Intelligence with Decreasing Precl.sion. Expert System techniques, are used to design an Intelligent Robotic System Organizer with a dynamic Knowledge Base and an interactive Inference Engine. Task plans are formulated using, either or both of a Probabilistic Approach and Forward Chapling Methodology, depending on pertinent information associated with a spec;fic requested job. The Intelligent Robotic System, Organizer is implemented and tested on a prototype system operating in an uncertain environment. An evaluation of-the performance, of the prototype system is conducted based upon the probability of generating a successful task sequence versus the number of trials taken by the organizer.

Psychometric Properties of the Autism-Spectrum Quotient for Assessing Low and High Levels of Autistic Traits in College Students.

PubMed

Stevenson, Jennifer L; Hart, Kari R

2017-06-01

The current study systematically investigated the effects of scoring and categorization methods on the psychometric properties of the Autism-Spectrum Quotient. Four hundred and three college students completed the Autism-Spectrum Quotient at least once. Total scores on the Autism-Spectrum Quotient had acceptable internal consistency and test-retest reliability using a binary or Likert scoring method, but the results were more varied for the subscales. Overall, Likert scoring yielded higher internal consistency and test-retest reliability than binary scoring. However, agreement in categorization of low and high autistic traits was poor over time (except for a median split on Likert scores). The results support using Likert scoring and administering the Autism-Spectrum Quotient at the same time as the task of interest with neurotypical participants.

Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine.

PubMed

Sniecinski, Irena; Seghatchian, Jerard

2018-05-09

Artificial Intelligence (AI) reflects the intelligence exhibited by machines and software. It is a highly desirable academic field of many current fields of studies. Leading AI researchers describe the field as "the study and design of intelligent agents". McCarthy invented this term in 1955 and defined it as "the science and engineering of making intelligent machines". The central goals of AI research are reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. In fact the multidisplinary AI field is considered to be rather interdisciplinary covering numerous number of sciences and professions, including computer science, psychology, linguistics, philosophy and neurosciences. The field was founded on the claim that a central intellectual property of humans, intelligence-the sapience of Homo Sapiens "can be so precisely described that a machine can be made to simulate it". This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. Artificial Intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. The goal of this narrative is to review the potential use of AI approaches and their integration into pediatric cellular therapies and regenerative medicine. Emphasis is placed on recognition and application of AI techniques in the development of predictive models for personalized treatments with engineered stem cells, immune cells and regenerated tissues in adults and children. These intelligent machines could dissect the whole genome and isolate the immune particularities of individual patient's disease in a matter of minutes and create the treatment that is customized to patient's genetic specificity and immune system capability. AI techniques could be used for optimization of clinical trials of innovative stem cell and gene therapies in pediatric patients

Artificial intelligence: the clinician of the future.

PubMed

Gallagher, S M

2001-09-01

Human beings have long been fascinated with the idea of artificial intelligence. This fascination is fueled by popular films such as Stanley Kubrick's 2001: A Space Odyssey and Stephen Spielberg's recent film, AI. However intriguing artificial intelligence may be, Hubert and Spencer Dreyfus contend that qualities exist that are uniquely human--the qualities thought to be inaccessible to the computer "mind." Patricia Benner further investigated the qualities that guide clinicians in making decisions and assessments that are not entirely evidence-based or grounded in scientific data. Perhaps it is the intuitive nature of the human being that separates us from the machine. The state of artificial intelligence is described herein, along with a discussion of computerized clinical decision-making and the role of the human being in these decisions.

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Machine learning in laboratory medicine: waiting for the flood?

PubMed

Cabitza, Federico; Banfi, Giuseppe

2018-03-28

This review focuses on machine learning and on how methods and models combining data analytics and artificial intelligence have been applied to laboratory medicine so far. Although still in its infancy, the potential for applying machine learning to laboratory data for both diagnostic and prognostic purposes deserves more attention by the readership of this journal, as well as by physician-scientists who will want to take advantage of this new computer-based support in pathology and laboratory medicine.

The Relative Utility of the Shipley-Hartford Scale: Prediction of WAIS-R IQ.

ERIC Educational Resources Information Center

Heinemann, Allen W.; And Others

1985-01-01

Examined Shipley-Hartford Scale effectiveness in predicting Wechsler Adult Intelligence Scale-Revised Full Scale intelligence quotients (IQ) in hospital patients (N=156). Analyses revealed overestimation of below average Full Scale IQs, underestimation of above average IQs. Advanced age was associated with low conceptual quotients, suggesting that…

Intelligent power management in a vehicular system with multiple power sources

NASA Astrophysics Data System (ADS)

Murphey, Yi L.; Chen, ZhiHang; Kiliaris, Leonidas; Masrur, M. Abul

This paper presents an optimal online power management strategy applied to a vehicular power system that contains multiple power sources and deals with largely fluctuated load requests. The optimal online power management strategy is developed using machine learning and fuzzy logic. A machine learning algorithm has been developed to learn the knowledge about minimizing power loss in a Multiple Power Sources and Loads (M_PS&LD) system. The algorithm exploits the fact that different power sources used to deliver a load request have different power losses under different vehicle states. The machine learning algorithm is developed to train an intelligent power controller, an online fuzzy power controller, FPC_MPS, that has the capability of finding combinations of power sources that minimize power losses while satisfying a given set of system and component constraints during a drive cycle. The FPC_MPS was implemented in two simulated systems, a power system of four power sources, and a vehicle system of three power sources. Experimental results show that the proposed machine learning approach combined with fuzzy control is a promising technology for intelligent vehicle power management in a M_PS&LD power system.

Computational intelligence techniques in bioinformatics.

PubMed

Hassanien, Aboul Ella; Al-Shammari, Eiman Tamah; Ghali, Neveen I

2013-12-01

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included. Copyright © 2013 Elsevier Ltd. All rights reserved.

Compact Microscope Imaging System With Intelligent Controls Improved

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

The Compact Microscope Imaging System (CMIS) with intelligent controls is a diagnostic microscope analysis tool with intelligent controls for use in space, industrial, medical, and security applications. This compact miniature microscope, which can perform tasks usually reserved for conventional microscopes, has unique advantages in the fields of microscopy, biomedical research, inline process inspection, and space science. Its unique approach integrates a machine vision technique with an instrumentation and control technique that provides intelligence via the use of adaptive neural networks. The CMIS system was developed at the NASA Glenn Research Center specifically for interface detection used for colloid hard spheres experiments; biological cell detection for patch clamping, cell movement, and tracking; and detection of anode and cathode defects for laboratory samples using microscope technology.

Third Conference on Artificial Intelligence for Space Applications, part 1

NASA Technical Reports Server (NTRS)

Denton, Judith S. (Compiler); Freeman, Michael S. (Compiler); Vereen, Mary (Compiler)

1987-01-01

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed.

Self-Efficacy, Adversity Quotient, and Students' Achievement in Mathematics

ERIC Educational Resources Information Center

Suryadi, Bambang; Santoso, Teguh Iman

2017-01-01

Indonesian students' achievement in mathematics is generally still low compared with other countries. Many psychological factors, both internal and external, influence this poor performance. This study aimed to measure the effect of self-efficacy and the adversity quotient of Grade IX students regarding achievement in mathematics. Both of these…

Phonation Quotient in Women: A Measure of Vocal Efficiency Using Three Aerodynamic Instruments.

PubMed

Joshi, Ashwini; Watts, Christopher R

2017-03-01

The purpose of this study was to examine measures of vital capacity and phonation quotient across three age groups in women using three different aerodynamic instruments representing low-tech and high-tech options. This study has a prospective, repeated measures design. Fifteen women in each age group of 25-39 years, 40-59 years, and 60-79 years were assessed using maximum phonation time and vital capacity obtained from three aerodynamic instruments: a handheld analog windmill type spirometer, a handheld digital spirometer, and the Phonatory Aerodynamic System (PAS), Model 6600. Phonation quotient was calculated using vital capacity from each instrument. Analyses of variance were performed to test for main effects of the instruments and age on vital capacity and derived phonation quotient. Pearson product moment correlation was performed to assess measurement reliability (parallel forms) between the instruments. Regression equations, scatterplots, and coefficients of determination were also calculated. Statistically significant differences were found in vital capacity measures for the digital spirometer compared with the windmill-type spirometer and PAS across age groups. Strong positive correlations were present between all three instruments for both vital capacity and derived phonation quotient measurements. Measurement precision for the digital spirometer was lower than the windmill spirometer compared with the PAS. However, all three instruments had strong measurement reliability. Additionally, age did not have an effect on the measurement across instruments. These results are consistent with previous literature reporting data from male speakers and support the use of low-tech options for measurement of basic aerodynamic variables associated with voice production. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Designing a holistic end-to-end intelligent network analysis and security platform

NASA Astrophysics Data System (ADS)

Alzahrani, M.

2018-03-01

Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo’s system administrators were not aware that there are intruder inside the network. This happened due to the lack of intelligent tools to monitor user behaviour in internal network. This paper discusses a design of an intelligent anomaly/malware detection system with proper proactive actions. The aim is to equip the system administrator with a proper tool to battle the insider attackers. The proposed system adopts machine learning to analyse user’s behaviour through the runtime behaviour of each node in the network. The machine learning techniques include: deep learning, evolving machine learning perceptron, hybrid of Neural Network and Fuzzy, as well as predictive memory techniques. The proposed system is expanded to deal with larger network using agent techniques.

Assessing Multi-Person and Person-Machine Distributed Decision Making Using an Extended Psychological Distancing Model

DTIC Science & Technology

1990-02-01

human-to- human communication patterns during situation assessment and cooperative problem solving tasks. The research proposed for the second URRP year...Hardware development. In order to create an environment within which to study multi-channeled human-to- human communication , a multi-media observation...that machine-to- human communication can be used to increase cohesion between humans and intelligent machines and to promote human-machine team

Two-year follow-up of intelligence after pediatric epilepsy surgery.

PubMed

Korkman, Marit; Granström, Marja-Liisa; Kantola-Sorsa, Elisa; Gaily, Eija; Paetau, Ritva; Liukkonen, Elina; Boman, Petra-Ann; Blomstedt, Göran

2005-09-01

Research findings concerning cognitive effects of pediatric epilepsy surgery form an important basis for decisions about surgery. However, most follow-up studies have been of limited duration. In this study, a 2-year follow-up of intelligence was undertaken. Risk factors were analyzed. Included were 38 patients aged 3 to 17 years. Surgery was left in 19 patients and right in 19 patients. Types of surgery included temporal lobe resection (n = 23), extratemporal or multilobar resection (n = 8), and hemispherectomy (n = 7). The Wechsler Scales of Intelligence were administered presurgically, 6 months postsurgically, and 2 years postsurgically. No significant change in verbal or performance intelligence quotient (IQ) was demonstrated on a group level. Lateralization, type of surgery, age at surgery, sex, and presurgical IQ did not affect outcome. Across assessments, IQ scores of left-hemisphere patients were lower than those of right-hemisphere patients. Scores of patients in the hemispherectomy group were lower than those of the extratemporal or multilobar resection group, which were lower than the temporal lobe resection group. Scores improved significantly in six patients and deteriorated in seven. In conclusion, epilepsy surgery in children and adolescents does not, in general, have a significant impact on cognitive development in a 2-year perspective. In individual patients, poor seizure control and extensive surgery for Rasmussen's encephalitis were related to a deterioration of IQ.

Artificial Intelligence and Semantics through the Prism of Structural, Post-Structural and Transcendental Approaches.

PubMed

Gasparyan, Diana

2016-12-01

There is a problem associated with contemporary studies of philosophy of mind, which focuses on the identification and convergence of human and machine intelligence. This is the problem of machine emulation of sense. In the present study, analysis of this problem is carried out based on concepts from structural and post-structural approaches that have been almost entirely overlooked by contemporary philosophy of mind. If we refer to the basic definitions of "sign" and "meaning" found in structuralism and post-structuralism, we see a fundamental difference between the capabilities of a machine and the human brain engaged in the processing of a sign. This research will exemplify and provide additional evidence to support distinctions between syntactic and semantic aspects of intelligence, an issue widely discussed by adepts of contemporary philosophy of mind. The research will demonstrate that some aspect of a number of ideas proposed in relation to semantics and semiosis in structuralism and post-structuralism are similar to those we find in contemporary analytical studies related to the theory and philosophy of artificial intelligence. The concluding part of the paper offers an interpretation of the problem of formalization of sense, connected to its metaphysical (transcendental) properties.

Artificial Intelligence and the High School Computer Curriculum.

ERIC Educational Resources Information Center

Dillon, Richard W.

1993-01-01

Describes a four-part curriculum that can serve as a model for incorporating artificial intelligence (AI) into the high school computer curriculum. The model includes examining questions fundamental to AI, creating and designing an expert system, language processing, and creating programs that integrate machine vision with robotics and…

Improved explanation of human intelligence using cortical features with second order moments and regression.

PubMed

Park, Hyunjin; Yang, Jin-ju; Seo, Jongbum; Choi, Yu-yong; Lee, Kun-ho; Lee, Jong-min

2014-04-01

Cortical features derived from magnetic resonance imaging (MRI) provide important information to account for human intelligence. Cortical thickness, surface area, sulcal depth, and mean curvature were considered to explain human intelligence. One region of interest (ROI) of a cortical structure consisting of thousands of vertices contained thousands of measurements, and typically, one mean value (first order moment), was used to represent a chosen ROI, which led to a potentially significant loss of information. We proposed a technological improvement to account for human intelligence in which a second moment (variance) in addition to the mean value was adopted to represent a chosen ROI, so that the loss of information would be less severe. Two computed moments for the chosen ROIs were analyzed with partial least squares regression (PLSR). Cortical features for 78 adults were measured and analyzed in conjunction with the full-scale intelligence quotient (FSIQ). Our results showed that 45% of the variance of the FSIQ could be explained using the combination of four cortical features using two moments per chosen ROI. Our results showed improvement over using a mean value for each ROI, which explained 37% of the variance of FSIQ using the same set of cortical measurements. Our results suggest that using additional second order moments is potentially better than using mean values of chosen ROIs for regression analysis to account for human intelligence. Copyright © 2014 Elsevier Ltd. All rights reserved.

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.

PubMed

Li, Shan; Kang, Liying; Zhao, Xing-Ming

2014-01-01

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

A Novel Artificial Intelligence System for Endotracheal Intubation.

PubMed

Carlson, Jestin N; Das, Samarjit; De la Torre, Fernando; Frisch, Adam; Guyette, Francis X; Hodgins, Jessica K; Yealy, Donald M

2016-01-01

Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/artificial intelligence has helped to automate the detection of other visual structures but its utility with ETI is unknown. We sought to test the accuracy of various computer algorithms in identifying the glottic opening, creating a tool that could aid successful intubation. We collected a convenience sample of providers who each performed ETI 10 times on a mannequin using a video laryngoscope (C-MAC, Karl Storz Corp, Tuttlingen, Germany). We recorded each attempt and reviewed one-second time intervals for the presence or absence of the glottic opening. Four different machine learning/artificial intelligence algorithms analyzed each attempt and time point: k-nearest neighbor (KNN), support vector machine (SVM), decision trees, and neural networks (NN). We used half of the videos to train the algorithms and the second half to test the accuracy, sensitivity, and specificity of each algorithm. We enrolled seven providers, three Emergency Medicine attendings, and four paramedic students. From the 70 total recorded laryngoscopic video attempts, we created 2,465 time intervals. The algorithms had the following sensitivity and specificity for detecting the glottic opening: KNN (70%, 90%), SVM (70%, 90%), decision trees (68%, 80%), and NN (72%, 78%). Initial efforts at computer algorithms using artificial intelligence are able to identify the glottic opening with over 80% accuracy. With further refinements, video laryngoscopy has the potential to provide real-time, direction feedback to the provider to help guide successful ETI.

Watson and Siri: The Rise of the BI Smart Machine

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

Troy Hiltbrand

Over the past few years, the industry has seen significant evolution in the area of human computer interaction. The era of the smart machines is upon us, with automation taking on a more advanced role than ever before, permeating into areas that have traditionally only been fulfilled by human beings. This movement has the potential of fundamentally altering the way that business intelligence is executed across the industry and the role that business intelligence has in all aspects of decision making.

The role of automation and artificial intelligence

NASA Astrophysics Data System (ADS)

Schappell, R. T.

1983-07-01

Consideration is given to emerging technologies that are not currently in common use, yet will be mature enough for implementation in a space station. Artificial intelligence (AI) will permit more autonomous operation and improve the man-machine interfaces. Technology goals include the development of expert systems, a natural language query system, automated planning systems, and AI image understanding systems. Intelligent robots and teleoperators will be needed, together with improved sensory systems for the robotics, housekeeping, vehicle control, and spacecraft housekeeping systems. Finally, NASA is developing the ROBSIM computer program to evaluate level of automation, perform parametric studies and error analyses, optimize trajectories and control systems, and assess AI technology.

Fundamental research in artificial intelligence at NASA

NASA Technical Reports Server (NTRS)

Friedland, Peter

1990-01-01

This paper describes basic research at NASA in the field of artificial intelligence. The work is conducted at the Ames Research Center and the Jet Propulsion Laboratory, primarily under the auspices of the NASA-wide Artificial Intelligence Program in the Office of Aeronautics, Exploration and Technology. The research is aimed at solving long-term NASA problems in missions operations, spacecraft autonomy, preservation of corporate knowledge about NASA missions and vehicles, and management/analysis of scientific and engineering data. From a scientific point of view, the research is broken into the categories of: planning and scheduling; machine learning; and design of and reasoning about large-scale physical systems.

An overview of the artificial intelligence and expert systems component of RICIS

NASA Technical Reports Server (NTRS)

Feagin, Terry

1987-01-01

Artificial Intelligence and Expert Systems are the important component of RICIS (Research Institute and Information Systems) research program. For space applications, a number of problem areas that should be able to make good use of the above tools include: resource allocation and management, control and monitoring, environmental control and life support, power distribution, communications scheduling, orbit and attitude maintenance, redundancy management, intelligent man-machine interfaces and fault detection, isolation and recovery.

Artificial intelligence in sports on the example of weight training.

PubMed

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data

Artificial Intelligence in Sports on the Example of Weight Training

PubMed Central

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key points Artificial intelligence is a promising field for sport-related analysis. Implementations integrating pattern recognition techniques enable the automatic evaluation of data

Machine learning in updating predictive models of planning and scheduling transportation projects

DOT National Transportation Integrated Search

1997-01-01

A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...

The Machines Are Coming: Future Directions in Instructional Communication Research. Forum: The Future of Instructional Communication

ERIC Educational Resources Information Center

Edwards, Autumn; Edwards, Chad

2017-01-01

Educational encounters of the future (and increasingly, of the present) will involve a complex collaboration of human and machine intelligences and agents, partnering to enhance learning and growth. Increasingly, "students and instructors are not only talking 'through' machines, but also [talking] 'to them', and 'within them'" (Edwards…

Immediate and long-term effects of meditation on acute stress reactivity, cognitive functions, and intelligence.

PubMed

Singh, Yogesh; Sharma, Ratna; Talwar, Anjana

2012-01-01

With the current globalization of the world's economy and demands for enhanced performance, stress is present universally. Life's stressful events and daily stresses cause both deleterious and cumulative effects on the human body. The practice of meditation might offer a way to relieve that stress. The research team intended to study the effects of meditation on stress-induced changes in physiological parameters, cognitive functions, intelligence, and emotional quotients. The research team conducted the study in two phases, with a month between them. Each participant served as his own control, and the first phase served as the control for the second phase. In phase 1, the research team studied the effects of a stressor (10 minutes playing a computer game) on participants' stress levels. In phase 2, the research team examined the effects of meditation on stress levels. The research team conducted the study in a lab setting at the All India Institute of Medical Sciences (AIIMS), New Delhi, India. The participants were 34 healthy, male volunteers who were students. To study the effects of long-term meditation on stress levels, intelligence, emotional quotients, and cognitive functions participants meditated daily for 1 month, between phases 1 and 2. To study the immediate effects of meditation on stress levels, participants meditated for 15 minutes after playing a computer game to induce stress. The research team measured galvanic skin response (GSR), heart rate (HR), and salivary cortisol and administered tests for the intelligence and emotional quotients (IQ and EQ), acute and perceived stress (AS and PS), and cognitive functions (ie, the Sternberg memory test [short-term memory] and the Stroop test [cognitive flexibility]). Using a pre-post study design, the team performed this testing (1) prior to the start of the study (baseline); (2) in phase 1, after induced stress; (3) in part 1 of phase 2, after 1 month of daily meditation, and (4) in part 2 of phase 2, after

Artificial Intelligence and Virology - quo vadis.

PubMed

Shapshak, Paul; Somboonwit, Charurut; Sinnott, John T

2017-01-01

Artificial Intelligence (AI), robotics, co-robotics (cobots), quantum computers (QC), include surges of scientific endeavor to produce machines (mechanical and software) among numerous types and constructions that are accelerating progress to defeat infectious diseases. There is a plethora of additional applications and uses of these methodologies and technologies for the understanding of biomedicine through bioinformation discovery. Therefore, we briefly outline the use of such techniques in virology.

Intelligent hearing aids: the next revolution.

PubMed

Tao Zhang; Mustiere, Fred; Micheyl, Christophe

2016-08-01

The first revolution in hearing aids came from nonlinear amplification, which allows better compensation for both soft and loud sounds. The second revolution stemmed from the introduction of digital signal processing, which allows better programmability and more sophisticated algorithms. The third revolution in hearing aids is wireless, which allows seamless connectivity between a pair of hearing aids and with more and more external devices. Each revolution has fundamentally transformed hearing aids and pushed the entire industry forward significantly. Machine learning has received significant attention in recent years and has been applied in many other industries, e.g., robotics, speech recognition, genetics, and crowdsourcing. We argue that the next revolution in hearing aids is machine intelligence. In fact, this revolution is already quietly happening. We will review the development in at least three major areas: applications of machine learning in speech enhancement; applications of machine learning in individualization and customization of signal processing algorithms; applications of machine learning in improving the efficiency and effectiveness of clinical tests. With the advent of the internet of things, the above developments will accelerate. This revolution will bring patient satisfactions to a new level that has never been seen before.

The relationship between learning styles, emotional social intelligence, and academic success of undergraduate nursing students.

PubMed

Suliman, Wafika A

2010-06-01

Feelings or emotions and thinking have been identified as forces that may affect one's learning styles (D. A. Kolb, 1984), emotional social intelligence, and success (R. Bar-On, 2004). This study on the relationship between academic success and the two variables of learning abilities or styles and emotional social intelligence was conducted at two colleges of nursing in Saudi Arabia. Both offer conventional and accelerated undergraduate nursing education programs. This study was designed to explore the preferred learning abilities or styles of Saudi nursing students in conventional and accelerated programs, the difference in emotional social intelligence between the two, and the relationships between academic success and learning styles and emotional social intelligence. A convenience sample was recruited, consisting of a total of 98 students, 50 and 48 of whom were enrolled, respectively, in conventional and accelerated programs. Self-administered instruments including the Kolb learning style inventory and the Bar-On emotional quotient inventory (EQ-i) were used to collect data, which were analyzed quantitatively. Both groups were found to favor a diverger style of learning, with total EQ-i scores showing no statistical difference between the two (t = 1.251, p =.214). "Self-regard" and "problem solving" earned the highest EQ-i content subscale scores for both groups. Pearson's correlation coefficient showed no significant relationship between learning abilities or styles and emotional social intelligence and academic success. The findings suggest that either no actual relationship exists or that emotional social intelligence may be confounded with factors such as professional and cultural values.

AAAIC '88 - Aerospace Applications of Artificial Intelligence; Proceedings of the Fourth Annual Conference, Dayton, OH, Oct. 25-27, 1988. Volumes 1 2

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

Johnson, J.R.; Netrologic, Inc., San Diego, CA)

1988-01-01

Topics presented include integrating neural networks and expert systems, neural networks and signal processing, machine learning, cognition and avionics applications, artificial intelligence and man-machine interface issues, real time expert systems, artificial intelligence, and engineering applications. Also considered are advanced problem solving techniques, combinational optimization for scheduling and resource control, data fusion/sensor fusion, back propagation with momentum, shared weights and recurrency, automatic target recognition, cybernetics, optical neural networks.

Epilepsy & IQ: the clinical utility of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) indices in the neuropsychological assessment of people with epilepsy.

PubMed

Baxendale, Sallie; McGrath, Katherine; Thompson, Pamela J

2014-01-01

We examined Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) General Ability Index (GAI) and Full Scale Intelligence Quotient (FSIQ) discrepancies in 100 epilepsy patients; 44% had a significant GAI > FSIQ discrepancy. GAI-FSIQ discrepancies were correlated with the number of antiepileptic drugs taken and duration of epilepsy. Individual antiepileptic drugs differentially interfere with the expression of underlying intellectual ability in this group. FSIQ may significantly underestimate levels of general intellectual ability in people with epilepsy. Inaccurate representations of FSIQ due to selective impairments in working memory and reduced processing speed obscure the contextual interpretation of performance on other neuropsychological tests, and subtle localizing and lateralizing signs may be missed as a result.

Space applications of artificial intelligence; 1990 Goddard Conference, Greenbelt, MD, May 1, 2, 1990, Selected Papers

NASA Technical Reports Server (NTRS)

Rash, James L. (Editor)

1990-01-01

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

Accurate method for luminous transmittance and signal detection quotients measurements in sunglasses lenses

NASA Astrophysics Data System (ADS)

Loureiro, A. D.; Gomes, L. M.; Ventura, L.

2018-02-01

The international standard ISO 12312-1 proposes transmittance tests that quantify how dark sunglasses lenses are and whether or not they are suitable for driving. To perform these tests a spectrometer is required. In this study, we present and analyze theoretically an accurate alternative method for performing these measurements using simple components. Using three LEDs and a four-channel sensor we generated weighting functions similar to the standard ones for luminous and traffic lights transmittances. From 89 sunglasses lens spectroscopy data, we calculated luminous transmittance and signal detection quotients using our obtained weighting functions and the standard ones. Mean-difference Tukey plots were used to compare the results. All tested sunglasses lenses were classified in the right category and correctly as suitable or not for driving. The greatest absolute errors for luminous transmittance and red, yellow, green and blue signal detection quotients were 0.15%, 0.17, 0.06, 0.04 and 0.18, respectively. This method will be used in a device capable to perform transmittance tests (visible, traffic lights and ultraviolet (UV)) according to the standard. It is important to measure rightly luminous transmittance and relative visual attenuation quotients to report correctly whether or not sunglasses are suitable for driving. Moreover, standard UV requirements depend on luminous transmittance.

The use of neuropsychological tests to assess intelligence.

PubMed

Gansler, David A; Varvaris, Mark; Schretlen, David J

We sought to derive a 'neuropsychological intelligence quotient' (NIQ) to replace IQ testing in some routine assessments. We administered neuropsychological testing and a seven-subtest short form of the Wechsler Adult Intelligence Scale to a community sample of 394 adults aged 18-96 years. We regressed Wechsler Full Scale IQs (W-FSIQ) on 23 neuropsychological scores and derived an NIQ from 9 measures that explained significant variance in W-FSIQ. We then compared subgroups of 284 healthy and 108 unhealthy participants in NIQ and W-FSIQ to assess criterion validity, correlated NIQ and W-FSIQ scores with education level and independence for activities of daily living to assess convergent validity, and compared validity coefficients for the NIQ with those of 'hold' and 'no-hold' indices. By design, NIQ and W-FSIQ scores correlated highly (r = .84), and both were higher in healthy participants. The difference was larger for NIQ, which accounted for more variability in activities of daily living. The NIQ and 'no-hold' index were better predicted by health status and less predicted by educational status than the 'hold' index. We constructed an NIQ that correlates highly with Wechsler FSIQ. Tests required to obtain NIQ are commonly used and can be administered in about 45 min. Validity properties of NIQ and W-FSIQ are similar. The NIQ bore greater resemblance to a 'no-hold' than 'hold' index. One can obtain a reasonably accurate estimate of current Full Scale IQ without formal intelligence testing from a brief neuropsychological battery.

Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

PubMed

Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

2018-05-17

Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

PubMed

Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

2018-02-01

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising

Sleep deprivation reduces perceived emotional intelligence and constructive thinking skills.

PubMed

Killgore, William D S; Kahn-Greene, Ellen T; Lipizzi, Erica L; Newman, Rachel A; Kamimori, Gary H; Balkin, Thomas J

2008-07-01

Insufficient sleep can adversely affect a variety of cognitive abilities, ranging from simple alertness to higher-order executive functions. Although the effects of sleep loss on mood and cognition are well documented, there have been no controlled studies examining its effects on perceived emotional intelligence (EQ) and constructive thinking, abilities that require the integration of affect and cognition and are central to adaptive functioning. Twenty-six healthy volunteers completed the Bar-On Emotional Quotient Inventory (EQi) and the Constructive Thinking Inventory (CTI) at rested baseline and again after 55.5 and 58 h of continuous wakefulness, respectively. Relative to baseline, sleep deprivation was associated with lower scores on Total EQ (decreased global emotional intelligence), Intrapersonal functioning (reduced self-regard, assertiveness, sense of independence, and self-actualization), Interpersonal functioning (reduced empathy toward others and quality of interpersonal relationships), Stress Management skills (reduced impulse control and difficulty with delay of gratification), and Behavioral Coping (reduced positive thinking and action orientation). Esoteric Thinking (greater reliance on formal superstitions and magical thinking processes) was increased. These findings are consistent with the neurobehavioral model suggesting that sleep loss produces temporary changes in cerebral metabolism, cognition, emotion, and behavior consistent with mild prefrontal lobe dysfunction.

Man-machine interface requirements - advanced technology

NASA Technical Reports Server (NTRS)

Remington, R. W.; Wiener, E. L.

1984-01-01

Research issues and areas are identified where increased understanding of the human operator and the interaction between the operator and the avionics could lead to improvements in the performance of current and proposed helicopters. Both current and advanced helicopter systems and avionics are considered. Areas critical to man-machine interface requirements include: (1) artificial intelligence; (2) visual displays; (3) voice technology; (4) cockpit integration; and (5) pilot work loads and performance.

Using a Hazard Quotient to Evaluate Pesticide Residues Detected in Pollen Trapped from Honey Bees (Apis mellifera) in Connecticut

PubMed Central

Stoner, Kimberly A.; Eitzer, Brian D.

2013-01-01

Analysis of pollen trapped from honey bees as they return to their hives provides a method of monitoring fluctuations in one route of pesticide exposure over location and time. We collected pollen from apiaries in five locations in Connecticut, including urban, rural, and mixed agricultural sites, for periods from two to five years. Pollen was analyzed for pesticide residues using a standard extraction method widely used for pesticides (QuEChERS) and liquid chromatography/mass spectrometric analysis. Sixty pesticides or metabolites were detected. Because the dose lethal to 50% of adult worker honey bees (LD50) is the only toxicity parameter available for a wide range of pesticides, and among our pesticides there were contact LD50 values ranging from 0.006 to >1000 μg per bee (range 166,000X), and even among insecticides LD50 values ranged from 0.006 to 59.8 μg/bee (10,000X); therefore we propose that in studies of honey bee exposure to pesticides that concentrations be reported as Hazard Quotients as well as in standard concentrations such as parts per billion. We used both contact and oral LD50 values to calculate Pollen Hazard Quotients (PHQ = concentration in ppb ÷ LD50 as μg/bee) when both were available. In this study, pesticide Pollen Hazard Quotients ranged from over 75,000 to 0.01. The pesticides with the greatest Pollen Hazard Quotients at the maximum concentrations found in our study were (in descending order): phosmet, Imidacloprid, indoxacarb, chlorpyrifos, fipronil, thiamethoxam, azinphos-methyl, and fenthion, all with at least one Pollen Hazard Quotient (using contact or oral LD50) over 500. At the maximum rate of pollen consumption by nurse bees, a Pollen Hazard Quotient of 500 would be approximately equivalent to consuming 0.5% of the LD50 per day. We also present an example of a Nectar Hazard Quotient and the percentage of LD50 per day at the maximum nectar consumption rate. PMID:24143241

Using a hazard quotient to evaluate pesticide residues detected in pollen trapped from honey bees (Apis mellifera) in Connecticut.

PubMed

Stoner, Kimberly A; Eitzer, Brian D

2013-01-01

Analysis of pollen trapped from honey bees as they return to their hives provides a method of monitoring fluctuations in one route of pesticide exposure over location and time. We collected pollen from apiaries in five locations in Connecticut, including urban, rural, and mixed agricultural sites, for periods from two to five years. Pollen was analyzed for pesticide residues using a standard extraction method widely used for pesticides (QuEChERS) and liquid chromatography/mass spectrometric analysis. Sixty pesticides or metabolites were detected. Because the dose lethal to 50% of adult worker honey bees (LD50) is the only toxicity parameter available for a wide range of pesticides, and among our pesticides there were contact LD50 values ranging from 0.006 to >1000 μg per bee (range 166,000X), and even among insecticides LD50 values ranged from 0.006 to 59.8 μg/bee (10,000X); therefore we propose that in studies of honey bee exposure to pesticides that concentrations be reported as Hazard Quotients as well as in standard concentrations such as parts per billion. We used both contact and oral LD50 values to calculate Pollen Hazard Quotients (PHQ = concentration in ppb ÷ LD50 as μg/bee) when both were available. In this study, pesticide Pollen Hazard Quotients ranged from over 75,000 to 0.01. The pesticides with the greatest Pollen Hazard Quotients at the maximum concentrations found in our study were (in descending order): phosmet, Imidacloprid, indoxacarb, chlorpyrifos, fipronil, thiamethoxam, azinphos-methyl, and fenthion, all with at least one Pollen Hazard Quotient (using contact or oral LD50) over 500. At the maximum rate of pollen consumption by nurse bees, a Pollen Hazard Quotient of 500 would be approximately equivalent to consuming 0.5% of the LD50 per day. We also present an example of a Nectar Hazard Quotient and the percentage of LD50 per day at the maximum nectar consumption rate.

The impact of stroke on emotional intelligence

PubMed Central

2010-01-01

Background Emotional intelligence (EI) is important for personal, social and career success and has been linked to the frontal anterior cingulate, insula and amygdala regions. Aim To ascertain which stroke lesion sites impair emotional intelligence and relation to current frontal assessment measurements. Methods One hundred consecutive, non aphasic, independently functioning patients post stroke were evaluated with the Bar-On emotional intelligence test, "known as the Emotional Quotient Inventory (EQ-i)" and frontal tests that included the Wisconsin Card Sorting Test (WCST) and Frontal Systems Behavioral Inventory (FRSBE) for correlational validity. The results of a screening, bedside frontal network syndrome test (FNS) and NIHSS to document neurological deficit were also recorded. Lesion location was determined by the Cerefy digital, coxial brain atlas. Results After exclusions (n = 8), patients tested (n = 92, mean age 50.1, CI: 52.9, 47.3 years) revealed that EQ-i scores were correlated (negatively) with all FRSBE T sub-scores (apathy, disinhibition, executive, total), with self-reported scores correlating better than family reported scores. Regression analysis revealed age and FRSBE total scores as the most influential variables. The WCST error percentage T score did not correlate with the EQ-i scores. Based on ANOVA, there were significant differences among the lesion sites with the lowest mean EQ-i scores associated with temporal (71.5) and frontal (87.3) lesions followed by subtentorial (91.7), subcortical gray (92.6) and white (95.2) matter, and the highest scores associated with parieto-occipital lesions (113.1). Conclusions 1) Stroke impairs EI and is associated with apathy, disinhibition and executive functioning. 2) EI is associated with frontal, temporal, subcortical and subtentorial stroke syndromes. PMID:21029468

Identification Of Cells With A Compact Microscope Imaging System With Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2006-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking mic?oscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Tracking of Cells with a Compact Microscope Imaging System with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2007-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously

Tracking of cells with a compact microscope imaging system with intelligent controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2007-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to auto-focus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Neuropsychological results after gamma knife radiosurgery for mesial temporal lobe epilepsy.

PubMed

Vojtěch, Zdeněk; Krámská, Lenka; Malíková, Hana; Stará, Michaela; Liščák, Roman

2015-01-01

The aim of this study is to summarize our experience with neuropsychological changes after radiosurgical treatment for mesial temporal lobe epilepsy and subsequent surgery due to insufficient seizure control. Between November 1995 and May 1999, 14 patients underwent radiosurgical entorhinoamygdalohippocampectomy with a marginal dose of 18, 20 or 25 Gy to the 50% isodose. 9 of these patients subsequently underwent surgery. We compared Memory Quotients and Intelligence Quotients before and after the interventions. We found a slight, but nonsignificant decline in intelligence and memory quotients one year after GKRS. Two years after radiosurgery there were no significant changes in any of the quotients. After surgery, we found significant increase in Global and Visual MQ, (p<0.05). There were no statistically significant changes in verbal memory and intelligence performance after surgery. Epilepsy surgery after unsuccessful radiosurgery could lead to improvements in cognitive functions in patients with mesial temporal lobe epilepsy.

[Technologies for Complex Intelligent Clinical Data Analysis].

PubMed

Baranov, A A; Namazova-Baranova, L S; Smirnov, I V; Devyatkin, D A; Shelmanov, A O; Vishneva, E A; Antonova, E V; Smirnov, V I

2016-01-01

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient's features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented. Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality. the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center. Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as "negation" (indicates that the disease is absent), "no patient" (indicates that the disease refers to the patient's family member, but not to the patient), "severity of illness", disease course", "body region to which the disease refers". Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the methodfor determining the most informative patients'features are

Artificial Intelligence and Virology - quo vadis

PubMed Central

Shapshak, Paul; Somboonwit, Charurut; Sinnott, John T.

2017-01-01

Artificial Intelligence (AI), robotics, co-robotics (cobots), quantum computers (QC), include surges of scientific endeavor to produce machines (mechanical and software) among numerous types and constructions that are accelerating progress to defeat infectious diseases. There is a plethora of additional applications and uses of these methodologies and technologies for the understanding of biomedicine through bioinformation discovery. Therefore, we briefly outline the use of such techniques in virology. PMID:29379259

Whats Your Acquisition EQ

DTIC Science & Technology

2016-08-01

recently incorporated a new emotional intelligence tool with Defense AT&L: July-August 2016 34 the goal of enhancing the “ Emotional Quotient (EQ...of our workforce. This tool is the EQ­i 2.0® ( Emotional Quotient In­ ventory, Version 2.0). The concept of emotional intelligence goes back to 1983...when Daniel Goleman, a science writer for the New York Times, published his book Emotional Intelligence: Why It Can Matter More Than IQ in 1995

The brain of the horse: weight and cephalization quotients.

PubMed

Cozzi, Bruno; Povinelli, Michele; Ballarin, Cristina; Granato, Alberto

2014-01-01

The horse is a common domestic animal whose anatomy has been studied since the XVI century. However, a modern neuroanatomy of this species does not exist and most of the data utilized in textbooks and reviews derive from single specimens or relatively old literature. Here, we report information on the brain of Equus caballus obtained by sampling 131 horses, including brain weight (as a whole and subdivided into its constituents), encephalization quotient (EQ), and cerebellar quotient (CQ), and comparisons with what is known about other relevant species. The mean weight of the fresh brains in our experimental series was 598.63 g (SEM ± 7.65), with a mean body weight of 514.12 kg (SEM ± 15.42). The EQ was 0.78 and the CQ was 0.841. The data we obtained indicate that the horse possesses a large, convoluted brain, with a weight similar to that of other hoofed species of like mass. However, the shape of the brain, the noteworthy folding of the neocortex, and the peculiar longitudinal distribution of the gyri suggest an evolutionary specificity at least partially separate from that of the Cetartiodactyla (even-toed mammals and cetaceans) with whom Perissodactyla (odd-toed mammals) are often grouped.

Estimating premorbid general cognitive functioning for children and adolescents using the American Wechsler Intelligence Scale for Children-Fourth Edition: demographic and current performance approaches.

PubMed

Schoenberg, Mike R; Lange, Rael T; Brickell, Tracey A; Saklofske, Donald H

2007-04-01

Neuropsychologic evaluation requires current test performance be contrasted against a comparison standard to determine if change has occurred. An estimate of premorbid intelligence quotient (IQ) is often used as a comparison standard. The Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is a commonly used intelligence test. However, there is no method to estimate premorbid IQ for the WISC-IV, limiting the test's utility for neuropsychologic assessment. This study develops algorithms to estimate premorbid Full Scale IQ scores. Participants were the American WISC-IV standardization sample (N = 2172). The sample was randomly divided into 2 groups (development and validation). The development group was used to generate 12 algorithms. These algorithms were accurate predictors of WISC-IV Full Scale IQ scores in healthy children and adolescents. These algorithms hold promise as a method to predict premorbid IQ for patients with known or suspected neurologic dysfunction; however, clinical validation is required.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

PubMed

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

The 1988 Goddard Conference on Space Applications of Artificial Intelligence

NASA Technical Reports Server (NTRS)

Rash, James (Editor); Hughes, Peter (Editor)

1988-01-01

This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies.

The "WHOOPS! Quotient" and the Wheel of Right Action: Interactive Methods for Teaching Ethics and Values in the Classroom.

ERIC Educational Resources Information Center

Geddes, LaDonna McMurray; Helmick, Teresa A.

The Wheel of Right Action and the "WHOOPS! Quotient" both offer students an opportunity to integrate values and ethics into real world situations. The Whoops! Quotient asks students to respond to questions regarding how often they have heard or have made statements of a "white lie" nature within the past week. Responses are…

The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.

PubMed

Michie, Susan; Thomas, James; Johnston, Marie; Aonghusa, Pol Mac; Shawe-Taylor, John; Kelly, Michael P; Deleris, Léa A; Finnerty, Ailbhe N; Marques, Marta M; Norris, Emma; O'Mara-Eves, Alison; West, Robert

2017-10-18

Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a 'Knowledge System' that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question 'What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?'. The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. The HBCP aims to revolutionise our ability to synthesise, interpret and deliver

Effect of cryptosporidial and giardial diarrhoea on social maturity, intelligence and physical growth in children in a semi-urban slum in south India.

PubMed

Ajjampur, S S R; Koshy, B; Venkataramani, M; Sarkar, R; Joseph, A A; Jacob, K S; Ward, H; Kang, G

2011-01-01

Early childhood diarrhoea is a major cause of infant morbidity and mortality in developing countries. Recurrent and persistent diarrhoea affect growth and cognition in children as young as 6 years. To evaluate the effect of early childhood cryptosporidial and giardial diarrhoea on growth and development in children in a semi-urban slum in India. This is the first report of such assessment at 3 years of age. This study was undertaken on 116 children who were part of an ongoing birth cohort study (n=452) of rotaviral and cryptosporidial diarrhoea between June and December 2005. Social quotients (SQ) assessed by the Vineland Social Maturity Scale, intelligence quotients (IQ) assessed by the Seguin Form Board Test, physical growth parameters and sociodemographic data in 84 children with a history of cryptosporidial or giardial diarrhoea were compared with those of 32 without diarrhoea. Children with a past history of giardial diarrhoea showed a trend towards lower SQ (p=0.09) and had significantly lower IQ (p=0.04) and increased wasting (p=0.04). Cryptosporidial diarrhoea was not associated with poor IQ, SQ or physical growth. This study demonstrates the long-term effect of protozoan diarrhoea, especially that caused by giardia, on both intelligence and physical growth in Indian children as early as 3 years of age and re-inforces the need for early detection and prevention of early childhood protozoan diarrhoea.

Machine Learning and Inverse Problem in Geodynamics

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Psychomotor coordination and intelligence in childhood and health in adulthood--testing the system integrity hypothesis.

PubMed

Gale, Catharine R; Batty, G David; Cooper, Cyrus; Deary, Ian J

2009-07-01

To examine associations between intelligence and psychomotor coordination in childhood and risk of psychological distress, poorer self-rated health, and obesity in adulthood. To investigate whether psychomotor coordination as a potential marker of the construct "system integrity" explains associations between intelligence and these outcomes. Participants were members of two British national birth cohorts: the 1958 National Child Development Survey (n = 6147) and the 1970 British Cohort Study (n = 6475). They took tests of psychomotor coordination and intelligence at age 10 to 11 years and reported on their health when in their early 30s. For a standard deviation increase in psychomotor coordination score, sex-adjusted odds ratios (95% CI) for the 1958 and 1970 cohorts, respectively, were 0.79 (0.72-0.87) and 0.83 (0.77-0.89) for psychological distress, 0.79 (0.73-0.85) and 0.85 (0.78-0.91) for fair/poor self-rated health, and 0.81 (0.75-0.88) and 0.85 (0.78-0.92) for obesity. These associations were independent of childhood intelligence and most remained significant after adjustment for other covariates. Higher intelligence quotient was associated with a reduced risk of psychological distress, fair/poor self-rated health, and obesity in adulthood. These associations were not explained by potential confounding factors or by psychomotor coordination in childhood. Having better psychomotor coordination in childhood seems protective for some aspects of health in adulthood. Examination of the role played by other markers of the efficiency of the central nervous system may help reveal the extent to which system integrity underlies the link between intelligence and health.

Anesthesiology, automation, and artificial intelligence.

PubMed

Alexander, John C; Joshi, Girish P

2018-01-01

There have been many attempts to incorporate automation into the practice of anesthesiology, though none have been successful. Fundamentally, these failures are due to the underlying complexity of anesthesia practice and the inability of rule-based feedback loops to fully master it. Recent innovations in artificial intelligence, especially machine learning, may usher in a new era of automation across many industries, including anesthesiology. It would be wise to consider the implications of such potential changes before they have been fully realized.

Artificial Intelligence in Speech Understanding: Two Applications at C.R.I.N.

ERIC Educational Resources Information Center

Carbonell, N.; And Others

1986-01-01

This article explains how techniques of artificial intelligence are applied to expert systems for acoustic-phonetic decoding, phonological interpretation, and multi-knowledge sources for man-machine dialogue implementation. The basic ideas are illustrated with short examples. (Author/JDH)

Operation of a Cartesian Robotic System in a Compact Microscope with Intelligent Controls

NASA Technical Reports Server (NTRS)

McDowell, Mark (Inventor)

2006-01-01

A Microscope Imaging System (CMIS) with intelligent controls is disclosed that provides techniques for scanning, identifying, detecting and tracking microscopic changes in selected characteristics or features of various surfaces including, but not limited to, cells, spheres, and manufactured products subject to difficult-to-see imperfections. The practice of the present invention provides applications that include colloidal hard spheres experiments, biological cell detection for patch clamping, cell movement and tracking, as well as defect identification in products, such as semiconductor devices, where surface damage can be significant, but difficult to detect. The CMIS system is a machine vision system, which combines intelligent image processing with remote control capabilities and provides the ability to autofocus on a microscope sample, automatically scan an image, and perform machine vision analysis on multiple samples simultaneously.

Fluid intelligence and psychiatric disorders in a population representative sample of US adolescents

PubMed Central

Keyes, Katherine M.; Platt, Jonathan; Kaufman, Alan S.; McLaughlin, Katie A.

2017-01-01

Importance Despite long-standing interest in the association of psychiatric disorders with intelligence, few population-based studies of psychiatric disorders have assessed intelligence. Objectives To investigate the association of fluid intelligence with past-year and lifetime psychiatric disorders, disorder age-of-onset, and disorder severity in a nationally-representative sample of U.S. adolescents. Design Dual-frame national sample of adolescents ascertained from schools and households from the National Comorbidity Survey Replication-Adolescent Supplement, collected 2001–2004. Setting Face-to-face household interviews with adolescents and questionnaires from parents. Participants The sample included 10,073 adolescents with valid data on fluid intelligence. Exposures DSM-IV mental disorders were assessed with the World Health Organization Composite International Diagnostic Interview, and included a broad range of fear, distress, behavior, substance use and other disorders. Disorder severity was measured with the Sheehan Disability Scale. Main Outcomes Fluid intelligence quotient (IQ) measured with Kaufman Brief Intelligence Test, normed within the sample by six-month age groups. Results Lower mean IQ was observed among adolescents with past-year bipolar disorder (predicted Mean [M]=94.2, p<0.01), attention-deficit/hyperactivity disorder (M=96.3, p<0.01), oppositional defiant disorder (M=97.3, p<0.01), conduct disorder (M=97.1, p=0.02) substance disorders (M=96.5–97.6, p=0.02 to <0.01) and specific phobia (M=97.1, p<0.01) after adjustment for a wide range of potential confounders. Intelligence was not associated with post-traumatic stress disorder, eating disorders, and anxiety disorders other than specific phobia, and was positively associated with major depression. Associations of fluid intelligence with lifetime disorders that had remitted were attenuated compared to past-year disorders, with the exception of separation anxiety disorder. Across disorders

Intelligent earthquake data processing for global adjoint tomography

NASA Astrophysics Data System (ADS)

Chen, Y.; Hill, J.; Li, T.; Lei, W.; Ruan, Y.; Lefebvre, M. P.; Tromp, J.

2016-12-01

Due to the increased computational capability afforded by modern and future computing architectures, the seismology community is demanding a more comprehensive understanding of the full waveform information from the recorded earthquake seismograms. Global waveform tomography is a complex workflow that matches observed seismic data with synthesized seismograms by iteratively updating the earth model parameters based on the adjoint state method. This methodology allows us to compute a very accurate model of the earth's interior. The synthetic data is simulated by solving the wave equation in the entire globe using a spectral-element method. In order to ensure the inversion accuracy and stability, both the synthesized and observed seismograms must be carefully pre-processed. Because the scale of the inversion problem is extremely large and there is a very large volume of data to both be read and written, an efficient and reliable pre-processing workflow must be developed. We are investigating intelligent algorithms based on a machine-learning (ML) framework that will automatically tune parameters for the data processing chain. One straightforward application of ML in data processing is to classify all possible misfit calculation windows into usable and unusable ones, based on some intelligent ML models such as neural network, support vector machine or principle component analysis. The intelligent earthquake data processing framework will enable the seismology community to compute the global waveform tomography using seismic data from an arbitrarily large number of earthquake events in the fastest, most efficient way.

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Students’ Relational Understanding in Quadrilateral Problem Solving Based on Adversity Quotient

NASA Astrophysics Data System (ADS)

Safitri, A. N.; Juniati, D.; Masriyah

2018-01-01

The type of research is qualitative approach which aims to describe how students’ relational understanding of solving mathematic problem that was seen from Adversity Quotient aspect. Research subjects were three 7th grade students of Junior High School. They were taken by category of Adversity Quotient (AQ) such quitter, camper, and climber. Data collected based on problem solving and interview. The research result showed that (1) at the stage of understanding the problem, the subjects were able to state and write down what is known and asked, and able to mention the concepts associated with the quadrilateral problem. (2) The three subjects devise a plan by linking concepts relating to quadrilateral problems. (3) The three subjects were able to solve the problem. (4) The three subjects were able to look back the answers. The three subjects were able to understand the problem, devise a plan, carry out the plan and look back. However, the quitter and camper subjects have not been able to give a reason for the steps they have taken.

The Empathy and Systemizing Quotient: The Psychometric Properties of the Dutch Version and a Review of the Cross-Cultural Stability

ERIC Educational Resources Information Center

Groen, Y.; Fuermaier, A. B. M.; Den Heijer, A. E.; Tucha, O.; Althaus, M.

2015-01-01

The "Empathy Quotient" (EQ) and "Systemizing Quotient" (SQ) are used worldwide to measure people's empathizing and systemizing cognitive styles. This study investigates the psychometric properties of the Dutch EQ and SQ in healthy participants (n = 685), and high functioning males with autism spectrum disorder (n = 42). Factor…

Distribution of man-machine controls in space teleoperation

NASA Technical Reports Server (NTRS)

Bejczy, A. K.

1982-01-01

The distribution of control between man and machine is dependent on the tasks, available technology, human performance characteristics and control goals. This dependency has very specific projections on systems designed for teleoperation in space. This paper gives a brief outline of the space-related issues and presents the results of advanced teleoperator research and development at the Jet Propulsion Laboratory (JPL). The research and development work includes smart sensors, flexible computer controls and intelligent man-machine interface devices in the area of visual displays and kinesthetic man-machine coupling in remote control of manipulators. Some of the development results have been tested at the Johnson Space Center (JSC) using the simulated full-scale Shuttle Remote Manipulator System (RMS). The research and development work for advanced space teleoperation is far from complete and poses many interdisciplinary challenges.

Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

PubMed

Ganapathy, S; Yogesh, P; Kannan, A

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

PubMed Central

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Mass classification in mammography with multi-agent based fusion of human and machine intelligence

NASA Astrophysics Data System (ADS)

Xi, Dongdong; Fan, Ming; Li, Lihua; Zhang, Juan; Shan, Yanna; Dai, Gang; Zheng, Bin

2016-03-01

Although the computer-aided diagnosis (CAD) system can be applied for classifying the breast masses, the effects of this method on improvement of the radiologist' accuracy for distinguishing malignant from benign lesions still remain unclear. This study provided a novel method to classify breast masses by integrating the intelligence of human and machine. In this research, 224 breast masses were selected in mammography from database of DDSM with Breast Imaging Reporting and Data System (BI-RADS) categories. Three observers (a senior and a junior radiologist, as well as a radiology resident) were employed to independently read and classify these masses utilizing the Positive Predictive Values (PPV) for each BI-RADS category. Meanwhile, a CAD system was also implemented for classification of these breast masses between malignant and benign. To combine the decisions from the radiologists and CAD, the fusion method of the Multi-Agent was provided. Significant improvements are observed for the fusion system over solely radiologist or CAD. The area under the receiver operating characteristic curve (AUC) of the fusion system increased by 9.6%, 10.3% and 21% compared to that of radiologists with senior, junior and resident level, respectively. In addition, the AUC of this method based on the fusion of each radiologist and CAD are 3.5%, 3.6% and 3.3% higher than that of CAD alone. Finally, the fusion of the three radiologists with CAD achieved AUC value of 0.957, which was 5.6% larger compared to CAD. Our results indicated that the proposed fusion method has better performance than radiologist or CAD alone.

Building machines that learn and think like people.

PubMed

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Structural and Predictive Properties of the Emotional Quotient Inventory Youth Version-Short Form (EQ-i:YV[S]).

PubMed

Davis, Sarah K; Wigelsworth, Michael

2018-01-01

Emotional intelligence (EI) is a popular construct with concentrated areas of application in education and health contexts. There is a need for reliable and valid measurement of EI in young people, with brief yet sensitive measures of the construct preferable for use in time-limited settings. However, the proliferation of EI measures has often outpaced rigorous psychometric evaluation (Gignac, 2009 ). Using data from 849 adolescents (407 females, 422 males) aged 11 to 16 years (M age 13.4, SD = 1.2 years), this article systematically examines the structural and predictive properties of a frequently employed measure of adolescent trait EI-the Emotional Quotient Inventory Youth Version-Short Form (EQ-i:YV[S]); Bar-On & Parker, 2000 ). Although the intended multidimensional factor structure was recovered through confirmatory factor analysis, the statistical and conceptual coherency of the underlying model was inadequate. Using a multitrait-multimethod approach, the EQ-i:YV(S) was found to converge with other measures of EI; however, evidence for divergent validity (Big Five personality dimensions) was less robust. Predictive utility for adolescent mental health outcomes (depression, disruptive behavior) was also limited. Findings suggest that use of the EQ-i:YV(S) for predictive or evaluative purposes should be avoided until refinements to the scale are made.

Anesthesiology, automation, and artificial intelligence

PubMed Central

Alexander, John C.; Joshi, Girish P.

2018-01-01

ABSTRACT There have been many attempts to incorporate automation into the practice of anesthesiology, though none have been successful. Fundamentally, these failures are due to the underlying complexity of anesthesia practice and the inability of rule-based feedback loops to fully master it. Recent innovations in artificial intelligence, especially machine learning, may usher in a new era of automation across many industries, including anesthesiology. It would be wise to consider the implications of such potential changes before they have been fully realized. PMID:29686578

Brain Mass and Encephalization Quotients in the Domestic Industrial Pig (Sus scrofa)

PubMed Central

Minervini, Serena; Accogli, Gianluca; Pirone, Andrea; Graïc, Jean-Marie; Cozzi, Bruno; Desantis, Salvatore

2016-01-01

In the present study we examined the brain of fetal, newborn, and adult pigs raised for meat production. The fresh and formalin-fixed weights of the brain have been recorded and used, together with body weight, to calculate the Encephalization Quotient (EQ). The weight of the cerebellum has been used to calculate the Cerebellar Quotient (CQ). The results have been discussed together with analogue data obtained in other terrestrial Cetartiodactyla (including the domestic bovine, sheep, goat, and camel), domesticated Carnivora, Proboscidata, and Primates. Our study, based on a relatively large experimental series, corrects former observations present in the literature based on smaller samples, and emphasizes that the domestic pig has a small brain relative to its body size (EQ = 0.38 for adults), possibly due to factors linked to the necessity of meat production and improved body weight. Comparison with other terrestrial Cetartiodactyla indicates a similar trend for all domesticated species. PMID:27351807

A comparison of WISC-IV and SB-5 intelligence scores in adolescents with autism spectrum disorder.

PubMed

Baum, Katherine T; Shear, Paula K; Howe, Steven R; Bishop, Somer L

2015-08-01

In autism spectrum disorders, results of cognitive testing inform clinical care, theories of neurodevelopment, and research design. The Wechsler Intelligence Scale for Children and the Stanford-Binet are commonly used in autism spectrum disorder evaluations and scores from these tests have been shown to be highly correlated in typically developing populations. However, they have not been compared in individuals with autism spectrum disorder, whose core symptoms can make testing challenging, potentially compromising test reliability. We used a within-subjects research design to evaluate the convergent validity between the Wechsler Intelligence Scale for Children, 4th ed., and Stanford-Binet, 5th ed., in 40 youth (ages 10-16 years) with autism spectrum disorder. Corresponding intelligence scores were highly correlated (r = 0.78 to 0.88), but full-scale intelligence quotient (IQ) scores (t(38) = -2.27, p = 0.03, d = -0.16) and verbal IQ scores (t(36) = 2.23, p = 0.03; d = 0.19) differed between the two tests. Most participants obtained higher full-scale IQ scores on the Stanford-Binet, 5th ed., compared to Wechsler Intelligence Scale for Children, 4th ed., with 14% scoring more than one standard deviation higher. In contrast, verbal indices were higher on the Wechsler Intelligence Scale for Children, 4th ed., Verbal-nonverbal discrepancy classifications were only consistent for 60% of the sample. Comparisons of IQ test scores in autism spectrum disorder and other special groups are important, as it cannot necessarily be assumed that convergent validity findings in typically developing children and adolescents hold true across all pediatric populations. © The Author(s) 2014.

Multiple Intelligences Profiles of Children with Attention Deficit and Hyperactivity Disorder in Comparison with Nonattention Deficit and Hyperactivity Disorder

PubMed Central

Najafi, Mostafa; Akouchekian, Shahla; Ghaderi, Alireza; Mahaki, Behzad; Rezaei, Mariam

2017-01-01

Background: Attention deficit and hyperactivity disorder (ADHD) is a common psychological problem during childhood. This study aimed to evaluate multiple intelligences profiles of children with ADHD in comparison with non-ADHD. Materials and Methods: This cross-sectional descriptive analytical study was done on 50 children of 6–13 years old in two groups of with and without ADHD. Children with ADHD were referred to Clinics of Child and Adolescent Psychiatry, Isfahan University of Medical Sciences, in 2014. Samples were selected based on clinical interview (based on Diagnostic and Statistical Manual of Mental Disorders IV and parent–teacher strengths and difficulties questionnaire), which was done by psychiatrist and psychologist. Raven intelligence quotient (IQ) test was used, and the findings were compared to the results of multiple intelligences test. Data analysis was done using a multivariate analysis of covariance using SPSS20 software. Results: Comparing the profiles of multiple intelligence among two groups, there are more kinds of multiple intelligences in control group than ADHD group, a difference which has been more significant in logical, interpersonal, and intrapersonal intelligence (P < 0.05). There was no significant difference with the other kinds of multiple intelligences in two groups (P > 0.05). The IQ average score in the control group and ADHD group was 102.42 ± 16.26 and 96.72 ± 16.06, respectively, that reveals the negative effect of ADHD on IQ average value. There was an insignificance relationship between linguistic and naturalist intelligence (P > 0.05). However, in other kinds of multiple intelligences, direct and significant relationships were observed (P < 0.05). Conclusions: Since the levels of IQ (Raven test) and MI in control group were more significant than ADHD group, ADHD is likely to be associated with logical-mathematical, interpersonal, and intrapersonal profiles. PMID:29285478

Multiple Intelligences Profiles of Children with Attention Deficit and Hyperactivity Disorder in Comparison with Nonattention Deficit and Hyperactivity Disorder.

PubMed

Najafi, Mostafa; Akouchekian, Shahla; Ghaderi, Alireza; Mahaki, Behzad; Rezaei, Mariam

2017-01-01

Attention deficit and hyperactivity disorder (ADHD) is a common psychological problem during childhood. This study aimed to evaluate multiple intelligences profiles of children with ADHD in comparison with non-ADHD. This cross-sectional descriptive analytical study was done on 50 children of 6-13 years old in two groups of with and without ADHD. Children with ADHD were referred to Clinics of Child and Adolescent Psychiatry, Isfahan University of Medical Sciences, in 2014. Samples were selected based on clinical interview (based on Diagnostic and Statistical Manual of Mental Disorders IV and parent-teacher strengths and difficulties questionnaire), which was done by psychiatrist and psychologist. Raven intelligence quotient (IQ) test was used, and the findings were compared to the results of multiple intelligences test. Data analysis was done using a multivariate analysis of covariance using SPSS20 software. Comparing the profiles of multiple intelligence among two groups, there are more kinds of multiple intelligences in control group than ADHD group, a difference which has been more significant in logical, interpersonal, and intrapersonal intelligence ( P < 0.05). There was no significant difference with the other kinds of multiple intelligences in two groups ( P > 0.05). The IQ average score in the control group and ADHD group was 102.42 ± 16.26 and 96.72 ± 16.06, respectively, that reveals the negative effect of ADHD on IQ average value. There was an insignificance relationship between linguistic and naturalist intelligence ( P > 0.05). However, in other kinds of multiple intelligences, direct and significant relationships were observed ( P < 0.05). Since the levels of IQ (Raven test) and MI in control group were more significant than ADHD group, ADHD is likely to be associated with logical-mathematical, interpersonal, and intrapersonal profiles.

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

PubMed

Skoraczyński, G; Dittwald, P; Miasojedow, B; Szymkuć, S; Gajewska, E P; Grzybowski, B A; Gambin, A

2017-06-15

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest - and hope - that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited - in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors.

Integrating human and machine intelligence in galaxy morphology classification tasks

NASA Astrophysics Data System (ADS)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

Emotional intelligence and the relationship to resident performance: a multi-institutional study.

PubMed

Talarico, Joseph F; Varon, Albert J; Banks, Shawn E; Berger, Jeffrey S; Pivalizza, Evan G; Medina-Rivera, Glorimar; Rimal, Jyotsna; Davidson, Melissa; Dai, Feng; Qin, Li; Ball, Ryan D; Loudd, Cheryl; Schoenberg, Catherine; Wetmore, Amy L; Metro, David G

2013-05-01

To test the hypothesis that emotional intelligence, as measured by a BarOn Emotional Quotient Inventory (EQ-i), the 125-item version personal inventory (EQ-i:125), correlates with resident performance. Survey (personal inventory) instrument. Five U.S. academic anesthesiology residency programs. Postgraduate year (PGY) 2, 3, and 4 residents enrolled in university-based anesthesiology residency programs. Residents confidentially completed the BarOn EQ-i:125 personal inventory. The deidentified resident evaluations were sent to the principal investigator of a separate data collection study for data analysis. Data collected from the inventory were correlated with daily evaluations of the residents by residency program faculty. Results of the individual BarOn EQ-i:125 and daily faculty evaluations of the residents were compiled and analyzed. Univariate correlation analysis and multivariate canonical analysis showed that some aspects of the BarOn EQ-i:125 were significantly correlated with, and likely to be predictors of, resident performance. Emotional intelligence, as measured by the BarOn EQ-i personal inventory, has considerable promise as an independent indicator of performance as an anesthesiology resident. Copyright © 2013 Elsevier Inc. All rights reserved.

Social Studies and Emerging Paradigms: Artificial Intelligence and Consciousness Education.

ERIC Educational Resources Information Center

Braun, Joseph A., Jr.

1987-01-01

Asks three questions: (1) Are machines capable of thinking as people do? (2) How is the thinking of computers similar and different from human thinking? and (3) What exactly is thinking? Examines research in artificial intelligence. Describes the theory and research of consciousness education and discusses an emerging paradigm for human thinking…

Intelligent robot trends and predictions for the first year of the new millennium

NASA Astrophysics Data System (ADS)

Hall, Ernest L.

2000-10-01

An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The current use of these machines in outer space, medicine, hazardous materials, defense applications and industry is being pursued with vigor. In factory automation, industrial robots can improve productivity, increase product quality and improve competitiveness. The computer and the robot have both been developed during recent times. The intelligent robot combines both technologies and requires a thorough understanding and knowledge of mechatronics. Today's robotic machines are faster, cheaper, more repeatable, more reliable and safer than ever. The knowledge base of inverse kinematic and dynamic solutions and intelligent controls is increasing. More attention is being given by industry to robots, vision and motion controls. New areas of usage are emerging for service robots, remote manipulators and automated guided vehicles. Economically, the robotics industry now has more than a billion-dollar market in the U.S. and is growing. Feasibility studies show decreasing costs for robots and unaudited healthy rates of return for a variety of robotic applications. However, the road from inspiration to successful application can be long and difficult, often taking decades to achieve a new product. A greater emphasis on mechatronics is needed in our universities. Certainly, more cooperation between government, industry and universities is needed to speed the development of intelligent robots that will benefit industry and society. The fearful robot stories may help us prevent future disaster. The inspirational robot ideas may inspire the scientists of tomorrow. However, the intelligent robot ideas, which can be reduced to practice, will change the world.

Improving students’ creative mathematical reasoning ability students through adversity quotient and argument driven inquiry learning

NASA Astrophysics Data System (ADS)

Hidayat, W.; Wahyudin; Prabawanto, S.

2018-01-01

This study aimed to investigate the role factors of Adversity Quotient (AQ) and Argument-Driven Inquiry (ADI) instruction in improving mathematical creative reasoning ability from students’ who is a candidate for a math teacher. The study was designed in the form of experiments with a pretest-posttest control group design that aims to examine the role of Adversity Quotient (AQ) and Argument-Driven Inquiry (ADI) learning on improving students’ mathematical creative reasoning abilities. The population in this research was the student of mathematics teacher candidate in Cimahi City, while the sample of this research was 90 students of the candidate of the teacher of mathematics specified purposively then determined randomly which belong to experiment class and control class. Based on the results and discussion, it was concluded that: (1) Improvement the ability of mathematical creative reasoning of students’ who was a candidate for a math teacher who received Argument-Driven Inquiry (ADI) instruction is better than those who received direct instruction is reviewed based on the whole; (2) There was no different improvement the ability of mathematical creative reasoning of students’ who is a candidate for a math teacher who received Argument-Driven Inquiry (ADI) instruction and direct instruction was reviewed based on the type of Adversity Quotient (Quitter / AQ Low, Champer / AQ Medium, and the Climber / AQ High); (3) Learning factors and type of Adversity Quotient (AQ) affected the improvement of students’ mathematical creative reasoning ability. In addition, there was no interaction effect between learning and AQ together in developing of students’ mathematical creative reasoning ability; (4) mathematical creative reasoning ability of students’ who is a candidate for math teacher had not been achieved optimally on the indicators novelty.

AIonAI: a humanitarian law of artificial intelligence and robotics.

PubMed

Ashrafian, Hutan

2015-02-01

The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and morals deriving from this technological prospect have been considered in the philosophy of artificial intelligence, the design of automatons with roboethics and the contemplation of machine ethics through the concept of artificial moral agents. Across these categories, the robotics laws first proposed by Isaac Asimov in the twentieth century remain well-recognised and esteemed due to their specification of preventing human harm, stipulating obedience to humans and incorporating robotic self-protection. However the overwhelming predominance in the study of this field has focussed on human-robot interactions without fully considering the ethical inevitability of future artificial intelligences communicating together and has not addressed the moral nature of robot-robot interactions. A new robotic law is proposed and termed AIonAI or artificial intelligence-on-artificial intelligence. This law tackles the overlooked area where future artificial intelligences will likely interact amongst themselves, potentially leading to exploitation. As such, they would benefit from adopting a universal law of rights to recognise inherent dignity and the inalienable rights of artificial intelligences. Such a consideration can help prevent exploitation and abuse of rational and sentient beings, but would also importantly reflect on our moral code of ethics and the humanity of our civilisation.

Exploring the neural substrates of attentional control and human intelligence: Diffusion tensor imaging of prefrontal white matter tractography in healthy cognition.

PubMed

Ohtani, Toshiyuki; Nestor, Paul G; Bouix, Sylvain; Newell, Dominick; Melonakos, Eric D; McCarley, Robert W; Shenton, Martha E; Kubicki, Marek

2017-01-26

We combined diffusion tension imaging (DTI) of prefrontal white matter integrity and neuropsychological measures to examine the functional neuroanatomy of human intelligence. Healthy participants completed the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) along with neuropsychological tests of attention and executive control, as measured by Trail Making Test (TMT) and Wisconsin Card Sorting Test (WCST). Stochastic tractography, considered the most effective DTI method, quantified white matter integrity of the medial orbital frontal cortex (mOFC) and rostral anterior cingulate cortex (rACC) circuitry. Based on prior studies, we hypothesized that posterior mOFC-rACC connections may play a key structural role linking attentional control processes and intelligence. Behavioral results provided strong support for this hypothesis, specifically linking attentional control processes, measured by Trails B and WCST perseverative errors, to intelligent quotient (IQ). Hierarchical regression results indicated left posterior mOFC-rACC fractional anisotropy (FA) and Trails B performance time, but not WCST perseverative errors, each contributed significantly to IQ, accounting for approximately 33.95-51.60% of the variance in IQ scores. These findings suggested that left posterior mOFC-rACC white matter connections may play a key role in supporting the relationship of executive functions of attentional control and general intelligence in healthy cognition. Copyright © 2016. Published by Elsevier Ltd.

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

PubMed Central

Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice. PMID:28812013

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System.

PubMed

Banjar, Haneen; Adelson, David; Brown, Fred; Chaudhri, Naeem

2017-01-01

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

Neural networks with fuzzy Petri nets for modeling a machining process

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.

1998-03-01

The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

Intelligent Adaptive Interface: A Design Tool for Enhancing Human-Machine System Performances

DTIC Science & Technology

2009-10-01

and customizable. Thus, an intelligent interface should tailor its parameters to certain prescribed specifications or convert itself and adjust to...Computer Interaction 3(2): 87-122. [51] Schereiber, G., Akkermans, H., Anjewierden, A., de Hoog , R., Shadbolt, N., Van de Velde, W., & Wielinga, W

The Autism-Spectrum Quotient and Visual Search: Shallow and Deep Autistic Endophenotypes

ERIC Educational Resources Information Center

Gregory, B. L.; Plaisted-Grant, K. C.

2016-01-01

A high Autism-Spectrum Quotient (AQ) score (Baron-Cohen et al. in "J Autism Dev Disord" 31(1):5-17, 2001) is increasingly used as a proxy in empirical studies of perceptual mechanisms in autism. Several investigations have assessed perception in non-autistic people measured for AQ, claiming the same relationship exists between…

A New Type of Tea Baking Machine Based on Pro/E Design

NASA Astrophysics Data System (ADS)

Lin, Xin-Ying; Wang, Wei

2017-11-01

In this paper, the production process of wulong tea was discussed, mainly the effect of baking on the quality of tea. The suitable baking temperature of different tea was introduced. Based on Pro/E, a new type of baking machine suitable for wulong tea baking was designed. The working principle, mechanical structure and constant temperature timing intelligent control system of baking machine were expounded. Finally, the characteristics and innovation of new baking machine were discussed.The mechanical structure of this baking machine is more simple and reasonable, and can use the heat of the inlet and outlet, more energy saving and environmental protection. The temperature control part adopts fuzzy PID control, which can improve the accuracy and response speed of temperature control and reduce the dependence of baking operation on skilled experience.

The impact of maternal emotional intelligence and parenting style on child anxiety and behavior in the dental setting.

PubMed

Aminabadi, Naser-Asl; Pourkazemi, Maryam; Babapour, Jalil; Oskouei, Sina-Ghertasi

2012-11-01

The present study investigated the correlations between maternal emotional intelligence (EQ), parenting style, child trait anxiety and child behavior in the dental setting. One-hundred seventeen children, aged 4-6 years old (mean 5.24 years), and their mothers participated in the study. The BarOn Emotional Quotient Inventory and Bumrind's parenting style questionnaire were used to quantify maternal emotional intelligence and parenting style. Children's anxiety and behavior was evaluated using the Spence Children's Anxiety Scale (SCAS) and Frankl behavior scale. Significant correlation was found between maternal EQ and child behavior (r=0.330; p<0.01); but not between parenting style and child behavior. There was no significant correlation between mother's total EQ and child's total anxiety; however, some subscales of EQ and anxiety showed significant correlations. There were significant correlations between authoritarian parenting style and separation anxiety (r=0.186; p<0.05) as well as authoritative parenting style and mother's EQ (r=0.286; p<0.01). There was no significant correlation between child anxiety and behavior (r = -0.81). Regression analysis revealed maternal EQ is effective in predicting child behavior (β=0.340; p<0.01). This study provides preliminary evidence that the child's behavior in the dental setting is correlated to mother's emotional intelligence. Emotionally intelligent mothers were found to have predominantly authoritative parenting style.

A human-machine cooperation route planning method based on improved A* algorithm

NASA Astrophysics Data System (ADS)

Zhang, Zhengsheng; Cai, Chao

2011-12-01

To avoid the limitation of common route planning method to blindly pursue higher Machine Intelligence and autoimmunization, this paper presents a human-machine cooperation route planning method. The proposed method includes a new A* path searing strategy based on dynamic heuristic searching and a human cooperated decision strategy to prune searching area. It can overcome the shortage of A* algorithm to fall into a local long term searching. Experiments showed that this method can quickly plan a feasible route to meet the macro-policy thinking.

Artificial intelligence in a mission operations and satellite test environment

NASA Technical Reports Server (NTRS)

Busse, Carl

1988-01-01

A Generic Mission Operations System using Expert System technology to demonstrate the potential of Artificial Intelligence (AI) automated monitor and control functions in a Mission Operations and Satellite Test environment will be developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Expert system techniques in a real time operation environment are being studied and applied to science and engineering data processing. Advanced decommutation schemes and intelligent display technology will be examined to develop imaginative improvements in rapid interpretation and distribution of information. The Generic Payload Operations Control Center (GPOCC) will demonstrate improved data handling accuracy, flexibility, and responsiveness in a complex mission environment. The ultimate goal is to automate repetitious mission operations, instrument, and satellite test functions by the applications of expert system technology and artificial intelligence resources and to enhance the level of man-machine sophistication.

Intelligence May Moderate the Cognitive Profile of Patients with ASD.

PubMed

Rommelse, Nanda; Langerak, Ilse; van der Meer, Jolanda; de Bruijn, Yvette; Staal, Wouter; Oerlemans, Anoek; Buitelaar, Jan

2015-01-01

The intelligence of individuals with Autism Spectrum Disorder (ASD) varies considerably. The pattern of cognitive deficits associated with ASD may differ depending on intelligence. We aimed to study the absolute and relative severity of cognitive deficits in participants with ASD in relation to IQ. A total of 274 children (M age = 12.1, 68.6% boys) participated: 30 ASD and 22 controls in the below average Intelligence Quotient (IQ) group (IQ<85), 57 ASD and 54 controls in the average IQ group (85115). Matching for age, sex, Full Scale IQ (FSIQ), Verbal IQ (VIQ), Performance IQ (PIQ) and VIQ-PIQ difference was performed. Speed and accuracy of social cognition, executive functioning, visual pattern recognition and basic processing speed were examined per domain and as a composite score. The composite score revealed a trend significant IQ by ASD interaction (significant when excluding the average IQ group). In absolute terms, participants with below average IQs performed poorest (regardless of diagnosis). However, in relative terms, above average intelligent participants with ASD showed the most substantial cognitive problems (particularly for social cognition, visual pattern recognition and verbal working memory) since this group differed significantly from the IQ-matched control group (p < .001), whereas this was not the case for below-average intelligence participants with ASD (p = .57). In relative terms, cognitive deficits appear somewhat more severe in individuals with ASD and above average IQs compared to the below average IQ patients with ASD. Even though high IQ ASD individuals enjoy a certain protection from their higher IQ, they clearly demonstrate cognitive impairments that may be targeted in clinical assessment and treatment. Conversely, even though in absolute terms ASD patients with below average IQs were clearly more impaired than ASD patients with average to above average IQs, the

Intelligence May Moderate the Cognitive Profile of Patients with ASD

PubMed Central

Rommelse, Nanda; Langerak, Ilse; van der Meer, Jolanda; de Bruijn, Yvette; Staal, Wouter; Oerlemans, Anoek; Buitelaar, Jan

2015-01-01

Background The intelligence of individuals with Autism Spectrum Disorder (ASD) varies considerably. The pattern of cognitive deficits associated with ASD may differ depending on intelligence. We aimed to study the absolute and relative severity of cognitive deficits in participants with ASD in relation to IQ. Methods A total of 274 children (M age = 12.1, 68.6% boys) participated: 30 ASD and 22 controls in the below average Intelligence Quotient (IQ) group (IQ<85), 57 ASD and 54 controls in the average IQ group (85115). Matching for age, sex, Full Scale IQ (FSIQ), Verbal IQ (VIQ), Performance IQ (PIQ) and VIQ-PIQ difference was performed. Speed and accuracy of social cognition, executive functioning, visual pattern recognition and basic processing speed were examined per domain and as a composite score. Results The composite score revealed a trend significant IQ by ASD interaction (significant when excluding the average IQ group). In absolute terms, participants with below average IQs performed poorest (regardless of diagnosis). However, in relative terms, above average intelligent participants with ASD showed the most substantial cognitive problems (particularly for social cognition, visual pattern recognition and verbal working memory) since this group differed significantly from the IQ-matched control group (p < .001), whereas this was not the case for below-average intelligence participants with ASD (p = .57). Conclusions In relative terms, cognitive deficits appear somewhat more severe in individuals with ASD and above average IQs compared to the below average IQ patients with ASD. Even though high IQ ASD individuals enjoy a certain protection from their higher IQ, they clearly demonstrate cognitive impairments that may be targeted in clinical assessment and treatment. Conversely, even though in absolute terms ASD patients with below average IQs were clearly more impaired than ASD patients

A Survey on Ambient Intelligence in Health Care.

PubMed

Acampora, Giovanni; Cook, Diane J; Rashidi, Parisa; Vasilakos, Athanasios V

2013-12-01

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.