Cognitive computing - theory and applications:
Gespeichert in:
| Weitere beteiligte Personen: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier, North Holland
[2016]
|
| Schriftenreihe: | Handbook of statistics
volume 35 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029199562&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XX, 384 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780444637444 |
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| 245 | 1 | 0 | |a Cognitive computing - theory and applications |c edited by Venkat N. Gudivada, East Carolina University, Greenville, NC, United States [und 3 weiteren] |
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| 264 | 4 | |c © 2016 | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Contributors xiii
Preface xv
Section A
Fundamentals and Principles
1. Cognitive Computing: Concepts, Architectures,
Systems, and Applications 3
V.N. Gudivada
1 Introduction 3
1.1 Chapter Organization 4
2 Interdisciplinary Nature of Cognitive Science 5
3 Cognitive Computing Systems 6
4 Representations for Information and Knowledge 7
5 Principal Technology Enablers of Cognitive Computing 9
5.1 Big Data and Data Science 9
5.2 Performance, Scalability, and Elasticity 11
5.3 Distributed Computing Architectures 11
5.4 Massive Parallel Processing Through MapReduce 13
5.5 Cloud Computing 15
5.6 Al and Machine Learning 15
5.7 Neuromorphic Computing 16
6 Cognitive Computing Architectures and Approaches 1 7
6.1 Cognitivist Architectures and Approaches 18
6.2 Connectionist Architectures and Approaches 20
6.3 Hybrid Architectures and Approaches 22
7 Cognitive Computing Systems and Applications 24
7.1 Intelligent Tutoring Systems 24
7.2 Problem Solving Systems 25
7.3 Question Answering 25
7.4 Health Care 25
7.5 Cognitive Businesses 26
7.6 Human—Robot Interaction 26
7.7 Cognitive Robots 26
7.8 Deep Learning and Image Search 26
7.9 Cross-media Retrieval 27
v
VI
Contents
7.10 Brain—Computer Interfaces 27
7.11 Autonomous Vehicle Navigation 28
8 Trends and Future Research Directions 28
9 Cognitive Computing Resources 30
9.1 Open Source Frameworks, Tools, and Digital Libraries 30
9.2 Nonopen Source Infrastructures and Cognitive Computing
Systems 32
References 33
2. Cognitive Computing and Neural Networks:
Reverse Engineering the Brain 39
A.S. Maida
1 Introduction 39
1.1 Chapter Outline 40
2 Brain Scalability 40
3 Neocortical Brain Organization 42
3.1 Ventral Visual Pathway 43
4 The Concept of a Basic Circuit 44
5 Abstractions of Cortical Basic Circuits 47
5.1 Abstract Neurons 47
5.2 Excitatory and Inhibitory Neurons 48
5.3 Receptive Fields and Visual Feature Maps 48
5.4 Optimal Representations with Respect to
Natural Image Statistics 52
5.5 Softmax and Winner-Take-All Network 54
5.6 Learning in the spike domain 55
5.7 A Spiking EM Classifier 56
5.8 The Liquid Computing Model 57
5.9 Unsupervised Feature Discovery 59
6 Large-Scale Cortical Simulations 64
6.1 The Blue Brain Digital Reconstruction 64
7 Hardware Support for Brain Simulation 65
8 Deep Learning Networks 65
8.1 Stochastic Deep Belief Networks 66
8.2 Deep ConvNets and Machine Learning 70
8.3 Convolutional Sparse DBNs 72
9 Summary and Conclusion 74
References 75
Section B
Complex Analytics and Machine Learning
3. Visual Analytic Decision-Making Environments
for Large-Scale Time-Evolving Graphs 81
S.R. Venna, R.N. Gottumukkala, and VV Raghavan
1 Introduction 82
2 Visual Analytics as an Approach to Cognitive Computing 85
2.1 Visual Analytics: A Definition 87
Contents
vii
3 Time-Evolving Graphs 87
3.1 Definition 87
3.2 Properties of Time-Evolving Graphs 88
4 Visual Analytics as a Framework for Time-Evolving Graphs 91
4.1 Visual Analytics Frameworks 91
4.2 Data Management 93
4.3 Graph Analytics 97
4.4 Visualization 99
4.5 Visual Representation and Interaction 100
5 Visual Analytics Sandbox: An Implementation Architecture 101
5.1 Implementation Architecture 101
5.2 Case Study: Emerging Event Detection for Social Media 102
5.3 Case Study: Real-Time Information-Based Evacuation
Decision Support System for Emergency Management 105
5.4 Case Study: Forecasting Influenza 107
6 Conclusion and Future Research 109
Acknowledgments 110
References 110
4. CyGraph: Graph-Based Analytics and
Visualization for Cybersecurity 11 7
5. Noel, E. Harley, K.H. Tam, M. Limiero, and M. Share
1 Introduction 118
2 Related Work 120
3 Description of CyGraph 122
3.1 CyGraph Architecture 122
3.2 CyGraph Data Sources 123
3.3 Big Data Analytics in CyGraph 125
3.4 CyGraph Client—Server 127
3.5 CyQL: CyGraph Domain-Specific Query Language 129
3.6 CyGraph Interactive Visualization 136
4 Example Applications 137
4.1 Cyber Analytics 139
4.2 Cyber Modeling and Simulation 156
5 Summary 162
Acknowledgments 164
References 164
5. Cognitive Analytics: Going Beyond Big Data
Analytics and Machine Learning 169
V.N. Gudivada, M.T. Irfan, E. Fathi, and D.L Rao
1 Introduction 169
1.1 Chapter Organization 172
2 Evolution of Analytics and Core Themes 1 72
2.1 Multiple Perspectives 173
2.2 Analytics Evolution 173
2.3 Data Warehouses and Data Marts 174
2.4 ROLAP, MOLAP, and HOLAP 1 75
Contents
2.5 Data Mining/Knowledge Discovery 175
2.6 Visual Analytics 176
2.7 Cognitive Analytics 176
3 Types of Learning 178
3.1 Active Learning 179
3.2 Reinforcement Learning 179
3.3 Ensemble Learning 180
4 Machine Learning Algorithms 181
4.1 Logistic Regression 183
4.2 Decision Trees 184
4.3 Support Vector Machine 185
4.4 Artificial Neural Networks and Deep Learning 186
4.5 Bayesian Networks 187
4.6 Libraries and Frameworks 188
5 Cognitive Analytics: A Coveted Goal 190
5.1 Cognalytics: A Reference Architecture for
Cognitive Analytics 1 90
5.2 Implementing Cognalytics 192
6 Cognitive Analytics Applications 197
6.1 Learning Analytics 197
6.2 Personalized Learning 198
6.3 Cognitive Businesses 198
6.4 BCI and Assistive Technologies 198
7 Current Trends and Research Issues 199
8 Conclusions 200
References 201
6. A Cognitive Random Forest: An Intra- and
Intercognitive Computing for Big Data
Classification Under Cune Condition 207
5. Suthaharan
1 Introduction 207
2 Terminologies 210
2.1 Cognitive Computing Models 211
2.2 Cognitive Computing Architectures 212
2.3 Cognitive Computing System 212
3 Random Forest Classifiers 213
4 The STE-M Model 214
4.1 Modeling of Computing as Senses 215
4.2 Modeling of Computing as Thoughts 215
4.3 Modeling of Computing as Experiences 216
4.4 Modeling of Computing as Memory 216
5 Cognitive Random Forest 217
5.1 Integration of Domain Divisions 217
5.2 Random Forest for Continuous Learning 218
Contents ix
6 Cognitive Computing System 218
7 Experimental Validation 219
7.1 Selected Cune Conditions 220
7.2 Standard Random Forest 221
7.3 Cognitive Random Forest 223
8 Conclusions 226
References 226
7. Bayesian Additive Regression Tree for Seemingly
Unrelated Regression with Automatic Tree Selection 229
S. Chakraborty
^ Introduction 229
2 BART for SUR with Automatic Tree Selection 232
2.1 BART-SUR Model 232
2.2 Prior Distribution 235
3 Fitting BART-SUR Model Through MCMC 236
4 Simulation Studies 238
5 Data Analysis 243
5.1 Biscuit Dough Data Set 243
5.2 Rohwer Data Set 244
5.3 Grunfeld s Investment Data 246
6 Conclusion 247
Appendices 248
References 250
Section C
Applications
8. Cognitive Systems for the Food-Water-Energy
Nexus 255
V.P.A. Lonij and J.-B. Fiot
1 Introduction 256
1.1 Constrained Resources 256
1.2 The Value of Cognitive Systems in Managing Constrained
Resources 259
2 Invariance, Correlation, and Data 261
2.1 Invariance 261
2.2 Correlation 262
2.3 Sparse Data, Big Data, and Data Types 264
3 Time-Series Data 264
3.1 Physics-Based Methods 265
3.2 Data-Driven Methods 266
3.3 Hybrid Methods 267
3.4 Hierarchical Time-Series and Missing Data 268
X
Contents
4 Images, Video, and Spatio-Temporal Data 268
4.1 Making the Most of Sensor Data 268
4.2 Forecasting and Deep Learning 271
4.3 Forecasting Horizon vs Accuracy 273
5 Autonomous Systems to Manage Complexity 273
6 Conclusion 276
References 276
9. Cognitive Computing Applications in
Education and Learning 283
M.T. Irfan and V.N. Gudivada
1 Introduction 283
2 EDM and LA 285
2.1 Educational Data Mining 285
2.2 Learning Analytics 288
3 Recent Research 291
3.1 Intelligent Tutoring Systems 292
3.2 Clustering and Student Modeling 293
3.3 Predicting Student Performance 294
3.4 Affect Detection and Student Engagement 295
4 Conclusion 296
References 297
10. Large Scale Data Enabled Evolution of
Spoken Language Research and Applications 301
S. Jothilakshmi and V.N. Gudivada
1 Introduction 301
1.1 Chapter Organization 302
2 Speech Signals 302
2.1 Human Speech Frequencies 303
2.2 Formants 303
2.3 Phonemes and Syllables 303
2.4 Analog to Digital Conversion of Speech Signals 304
3 Signal Preprocessing 304
3.1 Preemphasis 304
3.2 Framing 305
3.3 Windowing 306
4 Segmental Feature Extraction 306
5 Prosodic Feature Extraction 309
5.1 Non-Automatic Speech Recognizer Based
Prosodic Feature Extraction 310
5.2 Features for Voice Activity Detection 311
6 Mathematical Models 311
6.1 Acoustic and Language Models 31 1
6.2 Gaussian Mixture Models 31 3
6.3 Hidden Markov Models 313
Contents
xi
6.4 Support Vector Machines 314
6.5 Artificial Neural Network Models 315
6.6 Deep Neural Networks 315
7 Speech Processing Core Tasks and Applications 316
7.1 Speech Recognition 316
7.2 Speech Synthesis 319
7.3 Speaker Recognition 321
7.4 Spoken Language Identification 324
7.5 Speech-to-Speech Translation 326
7.6 Speaker Diarization 328
8 Resources for Speech Research 330
9 Trends and Research Directions 332
10 Conclusions 333
References 333
11. The Internet of Things and Cognitive Computing
F.D. Hudson and E.W. Nichols
1 Introduction
2 The loT—Definition and History
3 The Role of Big Data
4 Big Data Challenges and Opportunities for loT
and Cognitive Computing
4.1 Volume
4.2 Variety
4.3 Velocity
4.4 Veracity
4.5 Value
5 loT Use Cases and Opportunity to Leverage
Cognitive Computing
5.1 Smart Cities—Personal Transportation Planning
5.2 Smart Cities—City Transportation Management
5.3 Healthcare—The Internet of Medical Things
and Precision Medicine
6 Future Opportunities for loT and Cognitive Computing
References
341
341
342
346
347
348
351
352
352
354
355
355
360
365
370
371
Index
375
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| title | Cognitive computing - theory and applications |
| title_auth | Cognitive computing - theory and applications |
| title_exact_search | Cognitive computing - theory and applications |
| title_full | Cognitive computing - theory and applications edited by Venkat N. Gudivada, East Carolina University, Greenville, NC, United States [und 3 weiteren] |
| title_fullStr | Cognitive computing - theory and applications edited by Venkat N. Gudivada, East Carolina University, Greenville, NC, United States [und 3 weiteren] |
| title_full_unstemmed | Cognitive computing - theory and applications edited by Venkat N. Gudivada, East Carolina University, Greenville, NC, United States [und 3 weiteren] |
| title_short | Cognitive computing - theory and applications |
| title_sort | cognitive computing theory and applications |
| topic | Kognitionswissenschaft (DE-588)4193780-6 gnd Neurowissenschaften (DE-588)7555119-6 gnd Datenanalyse (DE-588)4123037-1 gnd Soft Computing (DE-588)4455833-8 gnd Kognitives Schema (DE-588)4164480-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Cloud Computing (DE-588)7623494-0 gnd |
| topic_facet | Kognitionswissenschaft Neurowissenschaften Datenanalyse Soft Computing Kognitives Schema Künstliche Intelligenz Cloud Computing Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029199562&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000002510 |
| work_keys_str_mv | AT gudivadavenkatn cognitivecomputingtheoryandapplications |