Cognitive Informatics, Computer Modelling, and Cognitive Science: Application to Neural Engineering, Robotics, and Stem: Volume 2: Application to Neural Engineering, Robotics, and STEM

Cognitive Informatics, Computer Modelling, and Cognitive Science: Volume Two, Application to Neural Engineering, Robotics, and STEM presents the practical, real-world applications of Cognitive Science to help readers understand how it can help them in their research, engineering and academic pursuits. The book is presented in two volumes, covering Introduction and Theoretical Background, Philosophical and Psychological Theory, and Cognitive Informatics and Computing. Volume Two includes Statistics for Cognitive Science, Cognitive Applications and STEM Case Studies. Other sections cover Cognitive Informatics, Computer Modeling and Cognitive Science: Application to Neural Engineering, Robotics, and STEM. The book's authors discuss the current status of research in the field of Cognitive Science, including cognitive language processing that paves the ways for developing numerous tools for helping physically challenged persons, and more. Identifies how foundational theories and concepts in cognitive science are applicable in other fields Includes a comprehensive review of cognitive science applications in multiple domains, applying it to neural engineering, robotics, computer science and STEM Presents basic statistics and cognitive maps, testing strategies of hypothesis, maximum likelihood estimator, Bayesian statistics, and discrete probability models of neural computation Contains in-depth technical coverage of cognitive applications and case studies, including neuro-computing, brain modeling, cognitive ability and cognitive robots Cognitive Informatics, Computer Modeling, and Cognitive Science Copyright Dedication Contents List of contributors Editors’ biographies Authors’ biography Preface Acknowledgments 1 Approaches from cognitive neuroscience and comparative cognition 1.1 Introduction 1.2 Cognitive science 1.3 Neuroscience 1.4 Python 1.5 Review of literature 1.6 Cognitive neuroscience/physiology 1.7 Cognitive psychology 1.8 Conclusion References Further reading 2 Functional neuroanatomy and disorders of cognition Abbreviations 2.1 Introduction 2.2 Neuroanatomy of memory encoding 2.2.1 Medial temporal lobe 2.2.2 Diencephalon 2.2.3 Basal forebrain 2.3 Mechanisms underlying memory formation 2.4 Neurotransmitters involved in cognition 2.4.1 Classical neurotransmitters 2.4.1.1 Acetylcholine 2.4.1.2 Glutamate 2.4.1.3 γ-Aminobutyric acid 2.4.1.4 Dopamine 2.4.1.5 Serotonin (5-hydroxytryptamine) 2.4.1.6 Agmatine 2.4.2 Neuropeptides 2.4.2.1 Cocaine- and amphetamine-regulated transcript 2.4.2.2 Neuropeptide Y 2.4.2.3 α-Melanocyte stimulating hormone 2.4.3 Neurosteroids 2.5 Cognition-related diseases 2.5.1 Alzheimer’s disease 2.5.1.1 Extraneuronal plaque deposition of β-amyloid 2.5.1.2 Intraneuronal accumulation of neurofibrillary tangles 2.5.2 Lewy body diseases 2.6 Conclusion 2.7 Acknowledgment References Further reading 3 A cognitive system of elderly exercise evaluation with sensors and robots 3.1 Introduction 3.2 System overview 3.3 Elderly exercise measurement 3.4 Exercise evaluation 3.5 Feedback by robot interface 3.6 Multiple Kinect application for occlusion problem 3.6.1 Frame synchronization 3.6.2 Sensing data integration without calibration 3.7 Conclusion Acknowledgment References 4 Models of making choice and control over thought for action 4.1 Outline of review 4.2 Introduction 4.3 Models of perceptual decision 4.3.1 Fast decision-making 4.3.2 Intuitive decision-making 4.4 Models of economic decision 4.5 Models of movement inhibition 4.5.1 Proactive control 4.5.2 Estimation of stopping efficacy 4.5.3 Trigger failures 4.5.4 Bayesian rational decision-making 4.5.5 Optimal Bayesian statistical inference 4.5.6 Decision process as optimal stochastic control 4.5.7 Linear approach to threshold explaining space and time model for decisions in space and time 4.6 Discussion Conflict of interest Acknowledgments References Further reading 5 Speech recognition technique for identification of raga 5.1 Introduction 5.2 Speech recognition 5.3 Applications of speech recognition 5.4 Speech analyses in music information retrieval 5.5 A brief history of Indian music 5.6 Mathematical structure of Carnatic music 5.7 Digital speech processing 5.8 Proposed methodology for classification of raga 5.9 A practical example using Praat 5.10 Conclusion Reference Further reading 6 Future of cognitive science 6.1 Introduction 6.2 Role of cognitive science in varied domains 6.2.1 Cognitive science for big data 6.2.2 Cognitive science for philosophy 6.2.3 Brain–machine interface 6.2.4 Cognition science for psychology 6.2.5 Cognition social science 6.2.6 Role of cognitive science in linguistics 6.2.7 Cognitive control 6.2.8 Cognitive image processing 6.3 Future of cognitive neuroscience and cognitive enhancement 6.3.1 Scope for neuroscience research and challenges 6.3.2 Cognitive enhancement 6.3.3 Ethical issues and concerns of cognitive enhancement 6.4 Conclusion References 7 Application of virtual reality systems to psychology and cognitive neuroscience research 7.1 Introduction 7.1.1 Cognitive science 7.1.2 Virtual reality 7.2 Literary survey review 7.2.1 Cognitive neuroscience/physiology 7.2.2 Cognitive psychology 7.3 Conclusion References Further reading 8 Electrodermal activity and its effectiveness in cognitive research field 8.1 Introduction 8.2 History of electrodermal activity signal, psychophysiological, and physiological mechanism behind electrodermal activity 8.2.1 Application of electrodermal activity 8.2.2 Electrodermal activity as an indicator of general arousal 8.2.3 Electrodermal activity in different sleep stages 8.2.4 Electrodermal indices of emotion and stress 8.3 Experiment design—a good experiment design 8.3.1 Experimental design 8.3.1.1 Experiment design 8.3.1.2 Types of experiments 8.3.1.3 Hypothesis 8.3.1.4 Stimulus 8.3.1.5 Measure of performance 8.3.2 External and internal influences 8.3.3 Climatic conditions 8.3.4 Internal or physiological influences 8.3.5 Demographic characteristics 8.4 Electrodermal activity signal collection sites and pretreatment of sites 8.4.1 Electrodermal activity signal collection sites 8.4.2 Pretreatment of sites 8.5 Artifacts removal from the electrodermal activity signal 8.6 Analysis of electrodermal activity signal 8.6.1 Phasic electrodermal activity 8.6.1.1 Latency 8.6.1.2 Amplitude 8.6.1.3 Shape of electrodermal responses 8.6.2 Area measurements 8.6.3 Tonic electrodermal activity 8.7 End remarks References Further reading 9 Study of modern brain-imaging and -signaling techniques for brain–computer interface 9.1 Introduction 9.2 Brain-imagining techniques 9.2.1 Computer tomography 9.2.1.1 Computer tomography head 9.2.1.1.1 Benefits 9.2.1.1.2 Risk and limitation 9.2.2 Near-infrared spectroscopy–based imaging equipment 9.2.2.1 Functional near-infrared spectroscopy 9.2.2.2 Diffuse optical imaging or diffuse optical tomography 9.2.2.3 High-density diffuse optical tomography 9.2.2.3.1 Advantages and disadvantages of optical imaging 9.2.3 Magnetic resonance imaging 9.2.3.1 Magnetic resonance imaging head 9.2.3.2 Functional magnetic resonance imaging 9.2.3.2.1 Advantages of magnetic resonance imaging 9.2.3.2.2 Disadvantages of magnetic resonance imaging 9.2.4 Single-photon emission computed tomography 9.2.4.1 Advantages of single-photon emission computed tomography 9.2.4.2 Disadvantage of single-photon emission computed tomography 9.2.5 Cranial ultrasound 9.2.5.1 Advantages of cranial ultrasound 9.2.5.2 Limitations of cranial ultrasound 9.3 Brain-signaling techniques 9.3.1 Electroencephalography 9.3.1.1 Application of electroencephalography [28] 9.3.1.2 Advantages of electroencephalography 9.3.1.3 Disadvantages of electroencephalography 9.3.2 Magnetoencephalography 9.3.2.1 Advantages of magnetoencephalography 9.3.2.2 Limitations of magnetoencephalography 9.3.3 Electromyography 9.3.3.1 Applications of electromyography 9.3.3.2 Advantages of electromyography 9.3.3.3 Limitations of electromyography 9.4 Sleep-based disorder analysis using neurodiagnosis techniques 9.4.1 Polysomnography 9.4.1.1 Advantages of polysomnograhy 9.4.1.2 Limitation of polysomnograhy 9.5 Summary References Further reading 10 Reading an extremist mind through literary language: approaching cognitive literary hermeneutics to R.N. Tagore’s play T... 10.1 Introduction 10.1.1 Why transdisciplinary? 10.1.2 Tagore’s The Post Office: a cognitive neurology 10.2 Affecting factors to activate mirror neuron in R.N. Tagore 10.3 Hypothesis 10.4 Colonialism/nationalism or national extremism: symptoms psychoneurological disorders 10.5 The mind of extremist: a neurological observation 10.6 “Nation is the greatest evil for the Nation”? 10.7 Amal as a religion under control References Further Reading Recommended Reading 11 REAH: Resolution Engine for Anaphora in Hindi dialogue 11.1 Introduction 11.1.1 Categorization of Hindi anaphora 11.1.2 Boundaries in anaphora resolution 11.1.2.1 Nonavailability of freeware Hindi discourse 11.1.2.2 Efficiency of linguistic preprocessor 11.1.2.3 No benchmark for POS tagging 11.1.2.4 Lack of efficient named entity recognizer 11.2 The state-of-the-art 11.2.1 Background of the authors 11.3 The resolution engine 11.3.1 The preprocessing phase 11.3.1.1 Data annotation 11.3.1.2 Defining the term patterns 11.3.1.3 Removal of irrelevant chunks and nonanaphoric 11.3.1.4 Identification of intermediate clause 11.3.1.5 Extraction of relevant noun phrases 11.3.1.6 Distance factors 11.3.1.7 Identifying inanimate entity 11.3.2 Anaphora resolution phase 11.3.2.1 Constraints 11.3.2.2 Identifying the equivalence class 11.3.2.2.1 Algorithm for resolving first-person pronouns 11.3.2.2.2 Algorithm for resolving second-person pronouns 11.3.2.2.3 Algorithm for resolving third-person pronouns 11.3.2.2.4 Algorithm for resolving reflexive pronouns 11.3.2.2.5 Algorithm for resolving locative pronouns 11.3.2.2.6 Algorithm for resolving demonstrative pronouns 11.4 Test datasets 11.5 Experiments and evaluations 11.6 Conclusion References 12 Surveying various effective modes and research trends on cognitive Internet of Things over wireless sensor network 12.1 Introduction 12.2 Objects with computing devices and AI 12.2.1 Internet of Things 12.2.2 Objects with computing devices and computerized ones 12.2.3 Objects with computing devices is not AI 12.2.4 Need for AI in Internet of Things 12.3 Intellectual AI and Intellectual compute 12.3.1 Intellectual AI and cognition, AI 12.3.2 Intellectual computing 12.3.3 Further than mechanization 12.4 Objects with computing devices and Intellectual computing 12.4.1 The Intellectual Internet of Things 12.4.2 Ownership of Intellectual Internet of Things 12.4.3 The pillars of Intellectual Internet of Things 12.4.4 Challenge of Intellectual Internet of Things 12.5 Value of Intellectual Internet of Things 12.6 Areas where we used 12.6.1 Well turned-out livelihood 12.6.2 Elegant health 12.6.3 Household appliances 12.6.4 Smart cities 12.6.5 Wiki City 12.6.6 Synchronized analytics 12.7 Usecase 12.8 Conclusion References Further reading 13 Time and feature specific sentiment analysis of product reviews 13.1 Introduction 13.2 Related work 13.3 Proposed model 13.4 Need of feature specificity 13.5 The aging factor 13.6 Experimental setup 13.6.1 Collection and preparing of dataset 13.6.2 Define feature dictionary for product 13.6.3 Preprocess, tokenize, and vectorize the dataset 13.6.4 Classify the review tokens under the features in the feature dictionary 13.6.5 Find the sentiments of the review tokens for each feature 13.6.6 Multiply the polarity with the aging factor to get the sentiment score of the review term 13.6.7 Sum up the results for each feature 13.6.8 Visualize the results 13.7 Result and discussion 13.8 Conclusion and future work References 14 Language learnability analysis of Hindi: a comparison with ideal and constrained learning approaches Glossary 14.1 Introduction 14.2 Language acquisition theories 14.3 Evaluation models 14.3.1 Bayesian segmentation 14.3.2 Bayesian inference 14.4 Data preparation for learnability analysis 14.4.1 Transliteration 14.4.2 Syllabification 14.4.3 Phonemization 14.5 Results and discussions 14.6 Conclusion and future work Acknowledgments References Further reading 15 A special report on changing trends in preventive stroke/cardiovascular risk assessment via B-mode ultrasonography 15.1 Introduction 15.1.1 Article search strategy 15.2 Risk assessment using traditional methods 15.3 Fundamentals of machine learning 15.3.1 Types of machine learning techniques 15.3.2 General framework of machine learning 15.3.2.1 Feature engineering: extraction and selection 15.3.2.2 Data partitioning 15.3.2.3 Training model design 15.3.2.4 Prediction or testing model 15.3.2.5 Performance evaluation of machine learning systems 15.3.3 Machine learning–based algorithms 15.4 Risk assessment in machine learning framework 15.4.1 Image-based stroke risk assessment using machine learning 15.4.2 Cardiovascular diseases risk assessment using machine learning 15.4.3 Cardiovscular disease/stroke risk assessment indices 15.5 Medical implications of machine learning–based risk assessment 15.6 Deep learning–based cardiovascular risk stratification 15.7 Challenges in machine learning design 15.8 Conclusion Acknowledgments Funding Disclosure References Appendix: performance evaluation parameters 16 A healthcare text classification system and its performance evaluation: a source of better intelligence by characterizin... 16.1 Introduction 16.2 Brief literature survey and our proposed model 16.2.1 Our model 16.3 Data types 16.3.1 Data type 1: TwitterA dataset 16.3.2 Data type 2: WebKB4 dataset 16.3.3 Data type 3: Disease dataset 16.3.4 Data type 4: Reuters (R8) dataset 16.3.5 Data type 5: SMS dataset 16.4 Methodology 16.4.1 Brief discussion on classifiers 16.4.1.1 Support vector machine 16.4.1.2 Multilayer perceptron 16.4.1.3 AdaBoost 16.4.1.4 Stochastic gradient descent 16.4.1.5 Decision tree 16.5 Experiment protocol 16.5.1 Experimental protocol 1: system classifier accuracy computation over all parameters 16.5.2 Experimental protocol 2: effect of training data size on classification accuracy 16.5.3 Experimental protocol 3: overall mean performance using all parameters: D, C, K, and T Sensitivity Specificity Positive predictive value Accuracy 16.6 Results 16.6.1 Results of protocol #1: system accuracy computation over all parameters 16.6.2 Results of protocol #2: effect of the training data size on classification accuracy 16.6.3 Results for the protocol #3: overall mean performance over all D, C, K, and T 16.7 Hypothesis validation and performance evaluation 16.7.1 Hypothesis validation 16.7.1.1 System performance linking misrepresentation ratio with area under the curve of machine learning system 16.7.1.2 Effect of misrepresentation ratio on machine learning classification accuracy 16.7.1.3 Effect of misrepresentation ratio on mean area under the curve for all classifiers and all data types 16.7.2 Individual receiver operating characteristic plots for all K protocols, D data types, and C classifiers 16.7.3 Reliability and stability analysis 16.7.3.1 Reliability index 16.7.3.2 Stability index 16.8 Discussion 16.8.1 Benchmarking 16.8.2 A special note on classifier, ground truth labels and misrepresentation ratio 16.8.3 Strength weakness and extensions 16.9 Conclusion Acknowledgment Funding Conflict of interest References Appendix A Types of dataset used in the study A.1 TwitterA dataset A.2 WebKB4 dataset A.3 Disease dataset A.4 Reuters (R8) dataset A.5 SMS dataset Appendix B Labels used in different text data types Appendix C Receiver operating characteristic curves C1 Receiver operating characteristic curves for K2 protocol using five classifiers C2 Receiver operating characteristic curves for K4 protocol using five classifiers C3 Receiver operating characteristic curves for K5 protocol using five classifiers C4 Receiver operating characteristic curves for K10 protocol using five classifiers C5 Receiver operating characteristic curves for JK protocol using five classifiers Appendix D Area under the curve tables Appendix E Postive predictive value tables Appendix F Sensitivity tables Appendix G Specificity tables Appendix H List of abbreviations/symbols Index "Cognitive Informatics, Computer Modelling, and Cognitive Science: Application to Neural Engineering, Robotics, and STEM presents the practical real-world applications of Cognitive Science to help readers understand how it can help them in their research, engineering, and academic pursuits. Cognitive Informatics, Computer Modelling, and Cognitive Science is presented in two volumes. Volume 1 includes three sections: Introduction and Theoretical Background, Philosophical and Psychological Theory, and Cognitive Informatics and Computing. Volume 2 includes three sections: Statistics for Cognitive Science, Cognitive Applications, and STEM Case Studies. Cognitive Informatics, Computer Modelling, and Cognitive Science: Application to Neural Engineering, Robotics, and STEM, highlights practice strategies and applications. Evaluation of various Cognitive Science tasks and computation results has to be done using statistics. The first section of this volume presents statistics for dealing with uncertainty as complex processes. Probability theory is discussed to address uncertainty and interpret the cognitive scenario, which includes probability distribution concepts, maximum likelihood estimators and other similar distribution functions. Bayesian Statistics is presented that play an important role in establishing hypothesis from evidence. This section also gives emphasis to cognitive maps and some testing and validation strategies for hypothesis. The second section of this Volume presents cognitive applications and future trends that can drive innovations for some time to come. This includes cognitive robots; internet of cognitive things; neuro-computing and human brain modelling. The modeling of the human brain and assessment of cognitive ability will greatly help researchers, neuroscientists and psychologists working in the field of understanding the human brain and its functions. Mind is typically considered different from the brain; brain as physical and mind as mental entities and therefore consciousness of human brain and mind control are emerging applications in the field of cognitive psychology and philosophical understanding of the brain. The authors discuss the current status of research in the field of Cognitive Science, including cognitive language processing that paves the ways for developing numerous tools for helping physically challenged persons. We are now in the age of self-driving cars and autonomous driver assistance system (ADAS) and therefore the insight, theory and applications of Cognitive Science in these areas are also explained with cases studies. Cognitive systems are employed in many tools that operate on wireless networks using smart sensors, so security becomes an important issue in the field of Cognitive Science. To deal with the challenges related to cyber-attack and hacking, cyber cognitive systems are also presented. The final section of Volume 2 provides several prominent case studies based on real-world implementations and studies of Cognitive Science highlighting various aspects of the science such as philosophical, psychological and STEM (Science, Technology, Engineering and Mathematics) based approaches"-- Provided by publisher "Cognitive Informatics, Computer Modelling, and Cognitive Science: Theory, Case Studies, and Applications presents the theoretical background and history of cognitive science to help readers understand its foundations, followed by its philosophical and psychological aspects, and culminating with the application of cognitive science for a wide range of engineering and computer science case studies. Representation of cognitive science, cognitive model of the brain, knowledge representation, and information processing in the human brain are discussed. Though cognitive science includes modelling and imitation of the brains of all living beings, the scope of the book encompasses only the human brain and its processing. The theory of consciousness, neuroscience, intelligence, decision making, mind and behavior analysis are described with cases studies supported by research and application. Computing applications are another important factor in cognitive science, and the authors present the various ways cognitive computing is used for information manipulation, processing and finally decision making. Mathematical and computational models, structures and processes of the human brain are also covered. Artificial intelligence is considered as core part of cognitive science. Advances in the role of machine learning, artificial intelligence, cognitive knowledge base, deep learning, cognitive image processing and suitable data analytics useful for cognitive science are covered, including relevant case studies. Cognitive Informatics, Computer Modelling, and Cognitive Science is presented in two volumes. Volume 1 includes three sections: Introduction and Theoretical Background, Phiosophical and Psychological Theory, and Cognitive Informatics and Computing. Volume 2 includes three sections: Statistics for Cognitive Science, Cognitive Applications, and STEM Case Studies"-- Provided by publisher __Cognitive Informatics, Computer Modelling, and Cognitive Science: Volume Two, Application to Neural Engineering, Robotics, and STEM__ presents the practical, real-world applications of Cognitive Science to help readers understand how it can help them in their research, engineering and academic pursuits. The book is presented in two volumes, covering Introduction and Theoretical Background, Philosophical and Psychological Theory, and Cognitive Informatics and Computing. Volume Two includes Statistics for Cognitive Science, Cognitive Applications and STEM Case Studies. Other sections cover Cognitive Informatics, Computer Modeling and Cognitive Science: Application to Neural Engineering, Robotics, and STEM. The book's authors discuss the current status of research in the field of Cognitive Science, including cognitive language processing that paves the ways for developing numerous tools for helping physically challenged persons, and more.