Cognitive Engineering for Next Generation Computing
Description
The cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices.
This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results.
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Persons
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.
Content
Preface xvii
Acknowledgments xix
1 Introduction to Cognitive Computing 1
Vamsidhar Enireddy, Sagar Imambi and C. Karthikeyan
1.1 Introduction: Definition of Cognition, Cognitive Computing 1
1.2 Defining and Understanding Cognitive Computing 2
1.3 Cognitive Computing Evolution and Importance 6
1.4 Difference Between Cognitive Computing and Artificial Intelligence 8
1.5 The Elements of a Cognitive System 11
1.5.1 Infrastructure and Deployment Modalities 11
1.5.2 Data Access, Metadata, and Management Services 12
1.5.3 The Corpus, Taxonomies, and Data Catalogs 12
1.5.4 Data Analytics Services 12
1.5.5 Constant Machine Learning 13
1.5.6 Components of a Cognitive System 13
1.5.7 Building the Corpus 14
1.5.8 Corpus Administration Governing and Protection Factors 16
1.6 Ingesting Data Into Cognitive System 17
1.6.1 Leveraging Interior and Exterior Data Sources 17
1.6.2 Data Access and Feature Extraction 18
1.7 Analytics Services 19
1.8 Machine Learning 22
1.9 Machine Learning Process 24
1.9.1 Data Collection 24
1.9.2 Data Preparation 24
1.9.3 Choosing a Model 24
1.9.4 Training the Model 24
1.9.5 Evaluate the Model 25
1.9.6 Parameter Tuning 25
1.9.7 Make Predictions 25
1.10 Machine Learning Techniques 25
1.10.1 Supervised Learning 25
1.10.2 Unsupervised Learning 27
1.10.3 Reinforcement Learning 27
1.10.4 The Significant Challenges in Machine Learning 28
1.11 Hypothesis Space 30
1.11.1 Hypothesis Generation 31
1.11.2 Hypotheses Score 32
1.12 Developing a Cognitive Computing Application 32
1.13 Building a Health Care Application 35
1.13.1 Healthcare Ecosystem Constituents 35
1.13.2 Beginning With a Cognitive Healthcare Application 37
1.13.3 Characterize the Questions Asked by the Clients 37
1.13.4 Creating a Corpus and Ingesting the Content 38
1.13.5 Training the System 38
1.13.6 Applying Cognition to Develop Health and Wellness 39
1.13.7 Welltok 39
1.13.8 CaféWell Concierge in Action 41
1.14 Advantages of Cognitive Computing 42
1.15 Features of Cognitive Computing 43
1.16 Limitations of Cognitive Computing 44
1.17 Conclusion 47
References 47
2 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges 49
Janmenjoy Nayak, P. Suresh Kumar, Dukka Karun Kumar Reddy, Bighnaraj Naik and Danilo Pelusi
2.1 Introduction 50
2.2 Cyber-Physical System Architecture 52
2.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS) 53
2.4 Machine Learning Applications in CPS 55
2.4.1 K-Nearest Neighbors (K-NN) in CPS 55
2.4.2 Support Vector Machine (SVM) in CPS 58
2.4.3 Random Forest (RF) in CPS 61
2.4.4 Decision Trees (DT) in CPS 63
2.4.5 Linear Regression (LR) in CPS 65
2.4.6 Multi-Layer Perceptron (MLP) in CPS 66
2.4.7 Naive Bayes (NB) in CPS 70
2.5 Use of IoT in CPS 70
2.6 Use of Big Data in CPS 72
2.7 Critical Analysis 77
2.8 Conclusion 83
References 84
3 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection 93
J.A.D.C.A. Jayakody, E.A.G.A. Edirisinghe and S.Lokuliyana
3.1 Introduction 94
3.1.1 Background 94
3.1.2 Research Objectives 96
3.1.3 Research Approach 97
3.1.4 Limitations 98
3.2 Literature Review 98
3.3 Methodology 101
3.3.1 Methodological Approach 101
3.3.1.1 Select an Appropriate Camera 102
3.3.1.2 Design the Lighting System 102
3.3.1.3 Design the Electronic Circuit 104
3.3.1.4 Design the Prototype 104
3.3.1.5 Collect Data and Develop the Algorithm 104
3.3.1.6 Develop the Prototype 106
3.3.1.7 Mobile Application Development 106
3.3.1.8 Completed Device 107
3.3.1.9 Methods of Data Collection 109
3.3.2 Methods of Analysis 109
3.4 Results 110
3.4.1 Impact of Project Outcomes 110
3.4.2 Results Obtained During the Methodology 111
3.4.2.1 Select an Appropriate Camera 111
3.4.2.2 Design the Lighting System 112
3.5 Discussion 112
3.6 Originality and Innovativeness of the Research 116
3.6.1 Validation and Quality Control of Methods 117
3.6.2 Cost-Effectiveness of the Research 117
3.7 Conclusion 117
References 117
4 Advanced Cognitive Models and Algorithms 121
J. Ramkumar, M. Baskar and B. Amutha
4.1 Introduction 122
4.2 Microsoft Azure Cognitive Model 122
4.2.1 AI Services Broaden in Microsoft Azure 125
4.3 IBM Watson Cognitive Analytics 126
4.3.1 Cognitive Computing 126
4.3.2 Defining Cognitive Computing via IBM Watson Interface 127
4.3.2.1 Evolution of Systems Towards Cognitive Computing 128
4.3.2.2 Main Aspects of IBM Watson 129
4.3.2.3 Key Areas of IBM Watson 130
4.3.3 IBM Watson Analytics 130
4.3.3.1 IBM Watson Features 131
4.3.3.2 IBM Watson DashDB 131
4.4 Natural Language Modeling 132
4.4.1 NLP Mainstream 132
4.4.2 Natural Language Based on Cognitive Computation 134
4.5 Representation of Knowledge Models 134
4.6 Conclusion 137
References 138
5 iParking-Smart Way to Automate the Management of the Parking System for a Smart City 141
J.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. Bandara
5.1 Introduction 142
5.2 Background & Literature Review 144
5.2.1 Background 144
5.2.2 Review of Literature 145
5.3 Research Gap 151
5.4 Research Problem 151
5.5 Objectives 153
5.6 Methodology 154
5.6.1 Lot Availability and Occupancy Detection 154
5.6.2 Error Analysis for GPS (Global Positioning System) 155
5.6.3 Vehicle License Plate Detection System 156
5.6.4 Analyze Differential Parking Behaviors and Pricing 156
5.6.5 Targeted Digital Advertising 157
5.6.6 Used Technologies 157
5.6.7 Specific Tools and Libraries 158
5.7 Testing and Evaluation 159
5.8 Results 161
5.9 Discussion 162
5.10 Conclusion 164
References 165
6 Cognitive Cyber-Physical System Applications 167
John A., Senthilkumar Mohan and D. Maria Manuel Vianny
6.1 Introduction 168
6.2 Properties of Cognitive Cyber-Physical System 169
6.3 Components of Cognitive Cyber-Physical System 170
6.4 Relationship Between Cyber-Physical System for Human-Robot 171
6.5 Applications of Cognitive Cyber-Physical System 172
6.5.1 Transportation 172
6.5.2 Industrial Automation 173
6.5.3 Healthcare and Biomedical 176
6.5.4 Clinical Infrastructure 178
6.5.5 Agriculture 180
6.6 Case Study: Road Management System Using CPS 181
6.6.1 Smart Accident Response System for Indian City 182
6.7 Conclusion 184
References 185
7 Cognitive Computing 189
T Gunasekhar and Marella Surya Teja
7.1 Introduction 189
7.2 Evolution of Cognitive System 191
7.3 Cognitive Computing Architecture 193
7.3.1 Cognitive Computing and Internet of Things 194
7.3.2 Cognitive Computing and Big Data Analysis 197
7.3.3 Cognitive Computing and Cloud Computing 200
7.4 Enabling Technologies in Cognitive Computing 202
7.4.1 Cognitive Computing and Reinforcement Learning 202
7.4.2 Cognitive Computive and Deep Learning 204
7.4.2.1 Rational Method and Perceptual Method 205
7.4.2.2 Cognitive Computing and Image Understanding 207
7.5 Applications of Cognitive Computing 209
7.5.1 Chatbots 209
7.5.2 Sentiment Analysis 210
7.5.3 Face Detection 211
7.5.4 Risk Assessment 211
7.6 Future of Cognitive Computing 212
7.7 Conclusion 214
References 215
8 Tools Used for Research in Cognitive Engineering and Cyber Physical Systems 219
Ajita Seth
8.1 Cyber Physical Systems 219
8.2 Introduction: The Four Phases of Industrial Revolution 220
8.3 System 221
8.4 Autonomous Automobile System 221
8.4.1 The Timeline 222
8.5 Robotic System 223
8.6 Mechatronics 225
References 228
9 Role of Recent Technologies in Cognitive Systems 231
V. Pradeep Kumar, L. Pallavi and Kolla Bhanu Prakash
9.1 Introduction 232
9.1.1 Definition and Scope of Cognitive Computing 232
9.1.2 Architecture of Cognitive Computing 233
9.1.3 Features and Limitations of Cognitive Systems 234
9.2 Natural Language Processing for Cognitive Systems 236
9.2.1 Role of NLP in Cognitive Systems 236
9.2.2 Linguistic Analysis 238
9.2.3 Example Applications Using NLP With Cognitive Systems 240
9.3 Taxonomies and Ontologies of Knowledge Representation for Cognitive Systems 241
9.3.1 Taxonomies and Ontologies and Their Importance in Knowledge Representation 242
9.3.2 How to Represent Knowledge in Cognitive Systems? 243
9.3.3 Methodologies Used for Knowledge Representation in Cognitive Systems 247
9.4 Support of Cloud Computing for Cognitive Systems 248
9.4.1 Importance of Shared Resources of Distributed Computing in Developing Cognitive Systems 248
9.4.2 Fundamental Concepts of Cloud Used in Building Cognitive Systems 249
9.5 Cognitive Analytics for Automatic Fraud Detection Using Machine Learning and Fuzzy Systems 254
9.5.1 Role of Machine Learning Concepts in Building Cognitive Analytics 255
9.5.2 Building Automated Patterns for Cognitive Analytics Using Fuzzy Systems 255
9.6 Design of Cognitive System for Healthcare Monitoring in Detecting Diseases 256
9.6.1 Role of Cognitive System in Building Clinical Decision System 257
9.7 Advanced High Standard Applications Using Cognitive Computing 259
9.8 Conclusion 262
References 263
10 Quantum Meta-Heuristics and Applications 265
Kolla Bhanu Prakash
10.1 Introduction 265
10.2 What is Quantum Computing? 267
10.3 Quantum Computing Challenges 268
10.4 Meta-Heuristics and Quantum Meta-Heuristics Solution Approaches 271
10.5 Quantum Meta-Heuristics Algorithms With Application Areas 273
10.5.1 Quantum Meta-Heuristics Applications for Power Systems 277
10.5.2 Quantum Meta-Heuristics Applications for Image Analysis 281
10.5.3 Quantum Meta-Heuristics Applications for Big Data or Data Mining 282
10.5.4 Quantum Meta-Heuristics Applications for Vehicular Trafficking 285
10.5.5 Quantum Meta-Heuristics Applications for Cloud Computing 286
10.5.6 Quantum Meta-Heuristics Applications for Bioenergy or Biomedical Systems 287
10.5.7 Quantum Meta-Heuristics Applications for Cryptography or Cyber Security 287
10.5.8 Quantum Meta-Heuristics Applications for Miscellaneous Domain 288
References 291
11 Ensuring Security and Privacy in IoT for Healthcare Applications 299
Anjali Yeole and D.R. Kalbande
11.1 Introduction 299
11.2 Need of IoT in Healthcare 300
11.2.1 Available Internet of Things Devices for Healthcare 301
11.3 Literature Survey on an IoT-Aware Architecture for Smart Healthcare Systems 303
11.3.1 Cyber-Physical System (CPS) for e-Healthcare 303
11.3.2 IoT-Enabled Healthcare With REST-Based Services 304
11.3.3 Smart Hospital System 304
11.3.4 Freescale Home Health Hub Reference Platform 305
11.3.5 A Smart System Connecting e-Health Sensors and Cloud 305
11.3.6 Customizing 6LoWPAN Networks Towards IoT-Based Ubiquitous Healthcare Systems 305
11.4 IoT in Healthcare: Challenges and Issues 306
11.4.1 Challenges of the Internet of Things for Healthcare 306
11.4.2 IoT Interoperability Issues 308
11.4.3 IoT Security Issues 308
11.4.3.1 Security of IoT Sensors 309
11.4.3.2 Security of Data Generated by Sensors 309
11.4.3.3 LoWPAN Networks Healthcare Systems and its Attacks 309
11.5 Proposed System: 6LoWPAN and COAP Protocol-Based IoT System for Medical Data Transfer by Preserving Privacy of Patient 310
11.6 Conclusion 312
References 312
12 Empowering Secured Outsourcing in Cloud Storage Through Data Integrity Verification 315
C. Saranya Jothi, Carmel Mary Belinda and N. Rajkumar
12.1 Introduction 315
12.1.1 Confidentiality 316
12.1.2 Availability 316
12.1.3 Information Uprightness 316
12.2 Literature Survey 316
12.2.1 PDP 316
12.2.1.1 Privacy-Preserving PDP Schemes 317
12.2.1.2 Efficient PDP 317
12.2.2 POR 317
12.2.3 HAIL 318
12.2.4 RACS 318
12.2.5 FMSR 318
12.3 System Design 319
12.3.1 Design Considerations 319
12.3.2 System Overview 320
12.3.3 Workflow 320
12.3.4 System Description 321
12.3.4.1 System Encoding 321
12.3.4.2 Decoding 322
12.3.4.3 Repair and Check 323
12.4 Implementation and Result Discussion 324
12.4.1 Creating Containers 324
12.4.2 File Chunking 324
12.4.3 XORing Partitions 326
12.4.4 Regeneration of File 326
12.4.5 Reconstructing a Node 327
12.4.6 Cloud Storage 327
12.4.6.1 NC-Cloud 327
12.4.6.2 Open Swift 329
12.5 Performance 330
12.6 Conclusion 332
References 333
Index 335
1
Introduction to Cognitive Computing
Vamsidhar Enireddy*, Sagar Imambi┼ and C. Karthikeyan╬
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India
Abstract
Cognitive computing is an interdisciplinary subject that brings under its umbrella several techniques such as Machine learning, big data analytics, artificial intelligence, analytics, natural language processing, and probability and statistics to gather information and understand it using different senses and learning from their experience. Cognitive computing helps humans in taking the right decisions at a right time helping the people to grow in their respective fields. In this chapter, we are going to discuss cognitive computing and the elements involved in it. Further, we will learn about the components and hypothesis generation and scoring of it.
Keywords: Artificial intelligence, cognition, cognitive computing, corpus, intuitive thinking, hypothesis generation, machine learning
1.1 Introduction: Definition of Cognition, Cognitive Computing
The term Cognition is defined as "The procedure or the method of acquiring information and understanding through experience, thought and the senses" [1]. It envelops numerous parts of procedures and intellectual functions, for example, development of information, thinking, reasoning, attention, decision making, evaluating the decisions, problem-solving, computing techniques, judging and assessing, critical thinking, conception, and creation of language. This process produces new information using existing information. A large number of fields especially psychology, neuroscience, biology, philosophy, psychiatry, linguistics, logic, education, anesthesia, and computer science view and analyze the cognitive processes with a diverse perspective contained by dissimilar contexts [2].
The word cognition dates to the 15th century, derived from a Latin word where it meant "thinking and awareness" [3]. The term comes from cognitio which means "examination, learning or knowledge", derived from the verb cognosco, a compound of con ('with'), and gnosco ('know'). The latter half, gnosco, itself is a cognate of a Greek verb, gi(g)n?sko (??(?)??s??, 'I know,' or 'perceive') [4, 5].
Aristotle is probably the first person who has shown interest to study the working of the mind and its effect on his experience. Memory, mental imagery, observation, and awareness are the major areas of cognition, hence Aristotle also showed keen interest in their study. He set incredible significance on guaranteeing that his examinations depended on exact proof, that is, logical data that is assembled through perception and principled experimentation [6]. Two centuries later, the basis for current ideas of comprehension was laid during the Enlightenment by scholars, like, John Locke and Dugald Stewart who tried to build up a model of the psyche in which thoughts were obtained, recalled, and controlled [7].
As Derived from the Stanford Encyclopedia of Philosophy the Cognitive science can be defined as "Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology."
The approach for cognitive computing depends on understanding the way how the human brain can process the information. The main theme or idea of a cognitive system is that it must able to serve as an associate for the human's rather than simply imitating the capabilities of the human brain.
1.2 Defining and Understanding Cognitive Computing
Cognitive computing can be defined as hardware and software to learn so that they need not be reprogrammed and automate the cognitive tasks [11]. This technology brings under its cover many different technologies such as Artificial Intelligence, Machine Learning, Advanced Analytics, Natural Language Processing, Big Data Analytics, and Distributed Computing. The impact of this technology can be seen in health care, business, decision making, private lives, and many more.
Two disciplines are brought together with cognitive computing
- Cognitive Science
- Computer Science.
The term cognitive science refers to the science of mind and the other is a computational approach where the theory is put into practice.
The ultimate objective of cognitive computing is that it must able to replicate the human thinking ability in a computer model. Using technologies like machine learning, natural language processing, advanced analytics, data mining, and statistics had made these things possible where the working of the human brain can be mimicked [8].
From a long back, we can construct the computers which perform the calculations at a high speed, also able to develop supercomputers which can do calculations in a fraction of second, but they are not able to perform the tasks as humans do like the reasoning, understanding and recognizing the objects and images.
Cognitive researchers discover the mental capability of humans through an examination of the aspects like memory, emotion, reasoning, perception, and language [12]. Figure 1.1 shows the Human centered cognitive cycle. On analysis, the human being's cognitive process can be divided into two stages. One is the humans use their sensory organs to perceive the information about their surrounding environment and become aware of it, in this manner humans gather the input from the outside environment. The second stage is that this information is carried by the nerves to the brain for processing and the process of storing, analyzing, and learning takes place [13].
Figure 1.1 Human-centered cognitive cycle.
Many researchers and scientists from many years had tried to develop the systems that can mimic the human thoughts and process, but it is relatively complex to transform the intricacy of thinking of humans and actions into systems. Human beings have a lot of influence on them such as perception, culture, sentiment, lifestyle, and implicit beliefs about their surrounding environment. Cognition is the basic framework that not only leverages the way we imagine but also the way we behave and the way we make decisions. To understand this let us consider some examples that we see around us. Why there are different recommendations and approaches between the treatments for the same disease with different doctors? Why do people with the same background born and brought up in the same family have different views and opinions about the world?
Dr. Daniel Kahneman is a Nobel Prize winner in economic sciences in 2002 had paved a way for the cognitive computing approach. He had made a lot of research in the area of psychology of judgment and decision making [11]. The approach is divided into two systems: 1. Intuitive thinking and 2. Controlled andrulecentric thinking.
System 1: Intuitive thinking
In this system, reasoning occurs in the human brain naturally. The conclusions are drawn using our instincts. In System 1 human thinking begins the moment they are born. Humans learn to notice and recognize the things and their relationship by themselves. To illustrate this we consider some examples for better understanding. The children correlate their parent's voices with safety. People correlate strident sound with danger. At the same time, we can see that children with a harsh mother are not going to have a similar experience with the voice of the mother as the child with a good mother. Humans learn more things over time and continue assimilating their thoughts into their mode of working in the world. The chess grandmaster can play the game with their mind anticipating their opponent's move and also they can play the game entirely in their mind without any need to touch the chessboard. The surrounding environment plays a major role in a person's behavior, it affects their emotions and attitudes. A person brought up in treacherous surroundings, have a different attitude about the people compared to a person brought up in healthy surroundings. In System1 using the perception, we gather the data about the world and connect the events. In the cognitive computing point of view, this System 1 had taught the way how we gather information from the surroundings helps us to conclude. Figure 1.2 shows collaboration between the Intuitive thinking and analysis.
System 2: Controlled and rulecentric thinking.
In this process, the reasoning is based on an additional premeditated process. This conclusion is made by taking into consideration both observations and test assumptions, rather than simply what is understood. In this type of system the thinking process to get a postulation, it uses a simulation model and observes the results of that particular statement. To do this a lot of data is required and a model is built to test the perceptions made by System 1. Consider the treatment of cancer patients in which a large number of ways and drugs are available to treat the patients. The cancer drugs not only kill the cancer cells but also kill the healthy cells, making the patient feel the side effects of it. When a drug company comes with any novel drug it tests on animals, records its results, and then it is tested on humans. After a long verification of the data checking the side effects of the drug on the other parts of the body, the government permits to release the drug into the...
