Cognitive Engineering for Next Generation Computing

Kolla Bhanu Prakash is Professor and Research Group Head for Artificial Intelligence and Data Science Research Group in CSE Department, K L University, Andhra Pradesh, India. He received his MSc and MPhil in Physics from Acharya Nagarjuna University and his ME and PhD in Computer Science & Engineering from Sathyabama University, Chennai, India. Dr. Prakash has 14+ years of experience working in academia, research, and teaching. He has published multiple SCI journal articles as well as been granted 5 patents.G. R. Kanagachidambaresan received his BE degree in Electrical and Electronics Engineering from Anna University in 2010; ME in Pervasive Computing Technologies in Anna University in 2012, and his PhD in Anna University Chennai in 2017. He is currently an associate professor, Department of CSE, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology.Srikanth Vemuru is a professor in the Department of Computer Science and Engineering, K L University. He received his PhD degree from Acharya Nagarjuna University (ANU) in 2011. He has more than 17 years of academic experience and in the software industry, and has published more than over 60 research papers in SCI journals and flagship conferences.Vamsidhar Enireddy is an associate professor in CSE Department, K L University, Andhra Pradesh, India. He received his PhD from JNTU Kakinada, India. Dr. Enireddy has 17+years of experience working in academia, research, and teaching. He has authored over 28 research papers in various national and international journals and conferences as well as been granted 3 patents and 1 patent filed.

• Preface xviiAcknowledgments xix1 Introduction to Cognitive Computing 1Vamsidhar Enireddy, Sagar Imambi and C. Karthikeyan1.1 Introduction: Definition of Cognition, Cognitive Computing 11.2 Defining and Understanding Cognitive Computing 21.3 Cognitive Computing Evolution and Importance 61.4 Difference Between Cognitive Computing and Artificial Intelligence 81.5 The Elements of a Cognitive System 111.5.1 Infrastructure and Deployment Modalities 111.5.2 Data Access, Metadata, and Management Services 121.5.3 The Corpus, Taxonomies, and Data Catalogs 121.5.4 Data Analytics Services 121.5.5 Constant Machine Learning 131.5.6 Components of a Cognitive System 131.5.7 Building the Corpus 141.5.8 Corpus Administration Governing and Protection Factors 161.6 Ingesting Data Into Cognitive System 171.6.1 Leveraging Interior and Exterior Data Sources 171.6.2 Data Access and Feature Extraction 181.7 Analytics Services 191.8 Machine Learning 221.9 Machine Learning Process 241.9.1 Data Collection 241.9.2 Data Preparation 241.9.3 Choosing a Model 241.9.4 Training the Model 241.9.5 Evaluate the Model 251.9.6 Parameter Tuning 251.9.7 Make Predictions 251.10 Machine Learning Techniques 251.10.1 Supervised Learning 251.10.2 Unsupervised Learning 271.10.3 Reinforcement Learning 271.10.4 The Significant Challenges in Machine Learning 281.11 Hypothesis Space 301.11.1 Hypothesis Generation 311.11.2 Hypotheses Score 321.12 Developing a Cognitive Computing Application 321.13 Building a Health Care Application 351.13.1 Healthcare Ecosystem Constituents 351.13.2 Beginning With a Cognitive Healthcare Application 371.13.3 Characterize the Questions Asked by the Clients 371.13.4 Creating a Corpus and Ingesting the Content 381.13.5 Training the System 381.13.6 Applying Cognition to Develop Health and Wellness 391.13.7 Welltok 391.13.8 CaféWell Concierge in Action 411.14 Advantages of Cognitive Computing 421.15 Features of Cognitive Computing 431.16 Limitations of Cognitive Computing 441.17 Conclusion 47References 472 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges 49Janmenjoy Nayak, P. Suresh Kumar, Dukka Karun Kumar Reddy, Bighnaraj Naik and Danilo Pelusi2.1 Introduction 502.2 Cyber-Physical System Architecture 522.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS) 532.4 Machine Learning Applications in CPS 552.4.1 K-Nearest Neighbors (K-NN) in CPS 552.4.2 Support Vector Machine (SVM) in CPS 582.4.3 Random Forest (RF) in CPS 612.4.4 Decision Trees (DT) in CPS 632.4.5 Linear Regression (LR) in CPS 652.4.6 Multi-Layer Perceptron (MLP) in CPS 662.4.7 Naive Bayes (NB) in CPS 702.5 Use of IoT in CPS 702.6 Use of Big Data in CPS 722.7 Critical Analysis 772.8 Conclusion 83References 843 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection 93J.A.D.C.A. Jayakody, E.A.G.A. Edirisinghe and S.Lokuliyana3.1 Introduction 943.1.1 Background 943.1.2 Research Objectives 963.1.3 Research Approach 973.1.4 Limitations 983.2 Literature Review 983.3 Methodology 1013.3.1 Methodological Approach 1013.3.1.1 Select an Appropriate Camera 1023.3.1.2 Design the Lighting System 1023.3.1.3 Design the Electronic Circuit 1043.3.1.4 Design the Prototype 1043.3.1.5 Collect Data and Develop the Algorithm 1043.3.1.6 Develop the Prototype 1063.3.1.7 Mobile Application Development 1063.3.1.8 Completed Device 1073.3.1.9 Methods of Data Collection 1093.3.2 Methods of Analysis 1093.4 Results 1103.4.1 Impact of Project Outcomes 1103.4.2 Results Obtained During the Methodology 1113.4.2.1 Select an Appropriate Camera 1113.4.2.2 Design the Lighting System 1123.5 Discussion 1123.6 Originality and Innovativeness of the Research 1163.6.1 Validation and Quality Control of Methods 1173.6.2 Cost-Effectiveness of the Research 1173.7 Conclusion 117References 1174 Advanced Cognitive Models and Algorithms 121J. Ramkumar, M. Baskar and B. Amutha4.1 Introduction 1224.2 Microsoft Azure Cognitive Model 1224.2.1 AI Services Broaden in Microsoft Azure 1254.3 IBM Watson Cognitive Analytics 1264.3.1 Cognitive Computing 1264.3.2 Defining Cognitive Computing via IBM Watson Interface 1274.3.2.1 Evolution of Systems Towards Cognitive Computing 1284.3.2.2 Main Aspects of IBM Watson 1294.3.2.3 Key Areas of IBM Watson 1304.3.3 IBM Watson Analytics 1304.3.3.1 IBM Watson Features 1314.3.3.2 IBM Watson DashDB 1314.4 Natural Language Modeling 1324.4.1 NLP Mainstream 1324.4.2 Natural Language Based on Cognitive Computation 1344.5 Representation of Knowledge Models 1344.6 Conclusion 137References 1385 iParking—Smart Way to Automate the Management of the Parking System for a Smart City 141J.A.D.C.A. Jayakody, E.A.G.A. Edirisinghe, S.A.H.M. Karunanayaka, E.M.C.S. Ekanayake, H.K.T.M. Dikkumbura and L.A.I.M. Bandara5.1 Introduction 1425.2 Background & Literature Review 1445.2.1 Background 1445.2.2 Review of Literature 1455.3 Research Gap 1515.4 Research Problem 1515.5 Objectives 1535.6 Methodology 1545.6.1 Lot Availability and Occupancy Detection 1545.6.2 Error Analysis for GPS (Global Positioning System) 1555.6.3 Vehicle License Plate Detection System 1565.6.4 Analyze Differential Parking Behaviors and Pricing 1565.6.5 Targeted Digital Advertising 1575.6.6 Used Technologies 1575.6.7 Specific Tools and Libraries 1585.7 Testing and Evaluation 1595.8 Results 1615.9 Discussion 1625.10 Conclusion 164References 1656 Cognitive Cyber-Physical System Applications 167John A., Senthilkumar Mohan and D. Maria Manuel Vianny6.1 Introduction 1686.2 Properties of Cognitive Cyber-Physical System 1696.3 Components of Cognitive Cyber-Physical System 1706.4 Relationship Between Cyber-Physical System for Human–Robot 1716.5 Applications of Cognitive Cyber-Physical System 1726.5.1 Transportation 1726.5.2 Industrial Automation 1736.5.3 Healthcare and Biomedical 1766.5.4 Clinical Infrastructure 1786.5.5 Agriculture 1806.6 Case Study: Road Management System Using CPS 1816.6.1 Smart Accident Response System for Indian City 1826.7 Conclusion 184References 1857 Cognitive Computing 189T Gunasekhar and Marella Surya Teja7.1 Introduction 1897.2 Evolution of Cognitive System 1917.3 Cognitive Computing Architecture 1937.3.1 Cognitive Computing and Internet of Things 1947.3.2 Cognitive Computing and Big Data Analysis 1977.3.3 Cognitive Computing and Cloud Computing 2007.4 Enabling Technologies in Cognitive Computing 2027.4.1 Cognitive Computing and Reinforcement Learning 2027.4.2 Cognitive Computive and Deep Learning 2047.4.2.1 Rational Method and Perceptual Method 2057.4.2.2 Cognitive Computing and Image Understanding 2077.5 Applications of Cognitive Computing 2097.5.1 Chatbots 2097.5.2 Sentiment Analysis 2107.5.3 Face Detection 2117.5.4 Risk Assessment 2117.6 Future of Cognitive Computing 2127.7 Conclusion 214References 2158 Tools Used for Research in Cognitive Engineering and Cyber Physical Systems 219Ajita Seth8.1 Cyber Physical Systems 2198.2 Introduction: The Four Phases of Industrial Revolution 2208.3 System 2218.4 Autonomous Automobile System 2218.4.1 The Timeline 2228.5 Robotic System 2238.6 Mechatronics 225References 2289 Role of Recent Technologies in Cognitive Systems 231V. Pradeep Kumar, L. Pallavi and Kolla Bhanu Prakash9.1 Introduction 2329.1.1 Definition and Scope of Cognitive Computing 2329.1.2 Architecture of Cognitive Computing 2339.1.3 Features and Limitations of Cognitive Systems 2349.2 Natural Language Processing for Cognitive Systems 2369.2.1 Role of NLP in Cognitive Systems 2369.2.2 Linguistic Analysis 2389.2.3 Example Applications Using NLP With Cognitive Systems 2409.3 Taxonomies and Ontologies of Knowledge Representation for Cognitive Systems 2419.3.1 Taxonomies and Ontologies and Their Importance in Knowledge Representation 2429.3.2 How to Represent Knowledge in Cognitive Systems? 2439.3.3 Methodologies Used for Knowledge Representation in Cognitive Systems 2479.4 Support of Cloud Computing for Cognitive Systems 2489.4.1 Importance of Shared Resources of Distributed Computing in Developing Cognitive Systems 2489.4.2 Fundamental Concepts of Cloud Used in Building Cognitive Systems 2499.5 Cognitive Analytics for Automatic Fraud Detection Using Machine Learning and Fuzzy Systems 2549.5.1 Role of Machine Learning Concepts in Building Cognitive Analytics 2559.5.2 Building Automated Patterns for Cognitive Analytics Using Fuzzy Systems 2559.6 Design of Cognitive System for Healthcare Monitoring in Detecting Diseases 2569.6.1 Role of Cognitive System in Building Clinical Decision System 2579.7 Advanced High Standard Applications Using Cognitive Computing 2599.8 Conclusion 262References 26310 Quantum Meta-Heuristics and Applications 265Kolla Bhanu Prakash10.1 Introduction 26510.2 What is Quantum Computing? 26710.3 Quantum Computing Challenges 26810.4 Meta-Heuristics and Quantum Meta-Heuristics Solution Approaches 27110.5 Quantum Meta-Heuristics Algorithms With Application Areas 27310.5.1 Quantum Meta-Heuristics Applications for Power Systems 27710.5.2 Quantum Meta-Heuristics Applications for Image Analysis 28110.5.3 Quantum Meta-Heuristics Applications for Big Data or Data Mining 28210.5.4 Quantum Meta-Heuristics Applications for Vehicular Trafficking 28510.5.5 Quantum Meta-Heuristics Applications for Cloud Computing 28610.5.6 Quantum Meta-Heuristics Applications for Bioenergy or Biomedical Systems 28710.5.7 Quantum Meta-Heuristics Applications for Cryptography or Cyber Security 28710.5.8 Quantum Meta-Heuristics Applications for Miscellaneous Domain 288References 29111 Ensuring Security and Privacy in IoT for Healthcare Applications 299Anjali Yeole and D.R. Kalbande11.1 Introduction 29911.2 Need of IoT in Healthcare 30011.2.1 Available Internet of Things Devices for Healthcare 30111.3 Literature Survey on an IoT-Aware Architecture for Smart Healthcare Systems 30311.3.1 Cyber-Physical System (CPS) for e-Healthcare 30311.3.2 IoT-Enabled Healthcare With REST-Based Services 30411.3.3 Smart Hospital System 30411.3.4 Freescale Home Health Hub Reference Platform 30511.3.5 A Smart System Connecting e-Health Sensors and Cloud 30511.3.6 Customizing 6LoWPAN Networks Towards IoT-Based Ubiquitous Healthcare Systems 30511.4 IoT in Healthcare: Challenges and Issues 30611.4.1 Challenges of the Internet of Things for Healthcare 30611.4.2 IoT Interoperability Issues 30811.4.3 IoT Security Issues 30811.4.3.1 Security of IoT Sensors 30911.4.3.2 Security of Data Generated by Sensors 30911.4.3.3 LoWPAN Networks Healthcare Systems and its Attacks 30911.5 Proposed System: 6LoWPAN and COAP Protocol-Based IoT System for Medical Data Transfer by Preserving Privacy of Patient 31011.6 Conclusion 312References 31212 Empowering Secured Outsourcing in Cloud Storage Through Data Integrity Verification 315C. Saranya Jothi, Carmel Mary Belinda and N. Rajkumar12.1 Introduction 31512.1.1 Confidentiality 31612.1.2 Availability 31612.1.3 Information Uprightness 31612.2 Literature Survey 31612.2.1 PDP 31612.2.1.1 Privacy-Preserving PDP Schemes 31712.2.1.2 Efficient PDP 31712.2.2 POR 31712.2.3 HAIL 31812.2.4 RACS 31812.2.5 FMSR 31812.3 System Design 31912.3.1 Design Considerations 31912.3.2 System Overview 32012.3.3 Workflow 32012.3.4 System Description 32112.3.4.1 System Encoding 32112.3.4.2 Decoding 32212.3.4.3 Repair and Check 32312.4 Implementation and Result Discussion 32412.4.1 Creating Containers 32412.4.2 File Chunking 32412.4.3 XORing Partitions 32612.4.4 Regeneration of File 32612.4.5 Reconstructing a Node 32712.4.6 Cloud Storage 32712.4.6.1 NC-Cloud 32712.4.6.2 Open Swift 32912.5 Performance 33012.6 Conclusion 332References 333Index 335