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E-raamat: Advanced Analytics and Deep Learning Models

Edited by (Terna Engineering College, Nerul, India), Edited by (Vellore Institute of Technology (VIT), Chennai Campus, India), Edited by (Terna Engineering College, Navi Mumbai, India)
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Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc.

Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools.

However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc.

This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence.

Audience

Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
Preface xix
Part 1: Introduction to Computer Vision 1(148)
1 Artificial Intelligence in Language Learning: Practices and Prospects
3(16)
Khushboo Kuddus
1.1 Introduction
4(1)
1.2 Evolution of CALL
5(2)
1.3 Defining Artificial Intelligence
7(1)
1.4 Historical Overview of AI in Education and Language Learning
7(1)
1.5 Implication of Artificial Intelligence in Education
8(5)
1.5.1 Machine Translation
9(1)
1.5.2 Chatbots
9(1)
1.5.3 Automatic Speech Recognition Tools
9(2)
1.5.4 Autocorrect/Automatic Text Evaluator
11(1)
1.5.5 Vocabulary Training Applications
12(1)
1.5.6 Google Docs Speech Recognition
12(1)
1.5.7 Language Muse™ Activity Palette
13(1)
1.6 Artificial Intelligence Tools Enhance the Teaching and Learning Processes
13(1)
1.6.1 Autonomous Learning
13(1)
1.6.2 Produce Smart Content
13(1)
1.6.3 Task Automation
13(1)
1.6.4 Access to Education for Students with Physical Disabilities
14(1)
1.7 Conclusion
14(1)
References
15(4)
2 Real Estate Price Prediction Using Machine Learning Algorithms
19(14)
Palak Furia
Anand Khandare
2.1 Introduction
20(1)
2.2 Literature Review
20(1)
2.3 Proposed Work
21(6)
2.3.1 Methodology
21(1)
2.3.2 Work Flow
22(1)
2.3.3 The Dataset
22(1)
2.3.4 Data Handling
23(1)
2.3.4.1 Missing Values and Data Cleaning
23(1)
2.3.4.2 Feature Engineering
24(1)
2.3.4.3 Removing Outliers
25(2)
2.4 Algorithms
27(2)
2.4.1 Linear Regression
27(1)
2.4.2 LASSO Regression
27(1)
2.4.3 Decision Tree
28(1)
2.4.4 Support Vector Machine
28(1)
2.4.5 Random Forest Regressor
28(1)
2.4.6 XGBoost
29(1)
2.5 Evaluation Metrics
29(1)
2.6 Result of Prediction
30(1)
References
31(2)
3 Multi-Criteria-Based Entertainment Recommender System Using Clustering Approach
33(32)
Chandramouli Das
Abhaya Kumar Sahoo
Chittaranjan Pradhan
3.1 Introduction
34(1)
3.2 Work Related Multi-Criteria Recommender System
35(3)
3.3 Working Principle
38(4)
3.3.1 Modeling Phase
39(1)
3.3.2 Prediction Phase
39(1)
3.3.3 Recommendation Phase
40(1)
3.3.4 Content-Based Approach
40(1)
3.3.5 Collaborative Filtering Approach
41(1)
3.3.6 Knowledge-Based Filtering Approach
41(1)
3.4 Comparison Among Different Methods
42(12)
3.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis
42(1)
3.4.1.1 Discussion and Result
43(3)
3.4.2 User Preference Learning in Multi-Criteria Recommendation Using Stacked Autoencoders by Tallapally et al.
46(1)
3.4.2.1 Dataset and Evaluation Matrix
46(1)
3.4.2.2 Training Setting
49(1)
3.4.2.3 Result
49(1)
3.4.3 Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts by Zheng
49(1)
3.4.3.1 Evaluation Setting
50(1)
3.4.3.2 Experimental Result
50(1)
3.4.4 Utility-Based Multi-Criteria Recommender Systems by Zheng
51(1)
3.4.4.1 Experimental Dataset
51(1)
3.4.4.2 Experimental Result
52(1)
3.4.5 Multi-Criteria Clustering Approach by Wasid and Ali
53(1)
3.4.5.1 Experimental Evaluation
53(1)
3.4.5.2 Result and Analysis
53(1)
3.5 Advantages of Multi-Criteria Recommender System
54(4)
3.5.1 Revenue
57(1)
3.5.2 Customer Satisfaction
57(1)
3.5.3 Personalization
57(1)
3.5.4 Discovery
58(1)
3.5.5 Provide Reports
58(1)
3.6 Challenges of Multi-Criteria Recommender System
58(2)
3.6.1 Cold Start Problem
58(1)
3.6.2 Sparsity Problem
59(1)
3.6.3 Scalability
59(1)
3.6.4 Over Specialization Problem
59(1)
3.6.5 Diversity
59(1)
3.6.6 Serendipity
59(1)
3.6.7 Privacy
60(1)
3.6.8 Shilling Attacks
60(1)
3.6.9 Gray Sheep
60(1)
3.7 Conclusion
60(1)
References
61(4)
4 Adoption of Machine/Deep Learning in Cloud With a Case Study on Discernment of Cervical Cancer
65(46)
A.P. Jyothi
S. Usha
H.R. Archana
4.1 Introduction
66(3)
4.2 Background Study
69(3)
4.3 Overview of Machine Learning/Deep Learning
72(2)
4.4 Connection Between Machine Learning/Deep Learning and Cloud Computing
74(1)
4.5 Machine Learning/Deep Learning Algorithm
74(19)
4.5.1 Supervised Learning
74(3)
4.5.2 Unsupervised Learning
77(1)
4.5.3 Reinforcement or Semi-Supervised Learning
77(1)
4.5.3.1 Outline of ML Algorithms
77(16)
4.6 A Project Implementation on Discernment of Cervical Cancer by Using Machine/Deep Learning in Cloud
93(8)
4.6.1 Proposed Work
94(1)
4.6.1.1 MRI Dataset
94(1)
4.6.1.2 Pre Processing
95(1)
4.6.1.3 Feature Extraction
96(1)
4.6.2 Design Methodology and Implementation
97(3)
4.6.3 Results
100(1)
4.7 Applications
101(3)
4.7.1 Cognitive Cloud
102(1)
4.7.2 Chatbots and Smart Personal Assistants
103(1)
4.7.3 IoT Cloud
103(1)
4.7.4 Business Intelligence
103(1)
4.7.5 AI-as-a-Service
104(1)
4.8 Advantages of Adoption of Cloud in Machine Learning/ Deep Learning
104(1)
4.9 Conclusion
105(1)
References
106(5)
5 Machine Learning and Internet of Things-Based Models for Healthcare Monitoring
111(16)
Shruti Kute
Amit Kumar Tyagi
S.U. Aswathy
Shaveta Malik
5.1 Introduction
112(1)
5.2 Literature Survey
113(1)
5.3 Interpretable Machine Learning in Healthcare
114(2)
5.4 Opportunities in Machine Learning for Healthcare
116(3)
5.5 Why Combining IoT and ML?
119(2)
5.5.1 ML-IoT Models for Healthcare Monitoring
119(2)
5.6 Applications of Machine Learning in Medical and Pharma
121(1)
5.7 Challenges and Future Research Direction
122(1)
5.8 Conclusion
123(1)
References
123(4)
6 Machine Learning-Based Disease Diagnosis and Prediction for E-Healthcare System
127(22)
Shruti Suhas Kute
A.V. Shreyas Madhav
Shabnam Kumari
S.U. Aswathy
6.1 Introduction
128(1)
6.2 Literature Survey
129(3)
6.3 Machine Learning Applications in Biomedical Imaging
132(2)
6.4 Brain Tumor Classification Using Machine Learning and IoT
134(1)
6.5 Early Detection of Dementia Disease Using Machine Learning and IoT-Based Applications
135(2)
6.6 IoT and Machine Learning-Based Diseases Prediction and Diagnosis System for EHRs
137(3)
6.7 Machine Learning Applications for a Real-Time Monitoring of Arrhythmia Patients Using IoT
140(1)
6.8 IoT and Machine Learning-Based System for Medical Data Mining
141(2)
6.9 Conclusion and Future Works
143(1)
References
144(5)
Part 2: Introduction to Deep Learning and its Models 149(134)
7 Deep Learning Methods for Data Science
151(30)
K. Indira
Kusumika Krori Dutta
S. Poornima
Sunny Arokia Swamy Bellary
7.1 Introduction
152(1)
7.2 Convolutional Neural Network
152(7)
7.2.1 Architecture
154(1)
7.2.2 Implementation of CNN
154(3)
7.2.3 Simulation Results
157(1)
7.2.4 Merits and Demerits
158(1)
7.2.5 Applications
159(1)
7.3 Recurrent Neural Network
159(9)
7.3.1 Architecture
160(1)
7.3.2 Types of Recurrent Neural Networks
161(1)
7.3.2.1 Simple Recurrent Neural Networks
161(1)
7.3.2.2 Long Short-Term Memory Networks
162(1)
7.3.2.3 Gated Recurrent Units (GRUs)
164(3)
7.3.3 Merits and Demerits
167(1)
7.3.3.1 Merits
167(1)
7.3.3.2 Demerits
167(1)
7.3.4 Applications
167(1)
7.4 Denoising Autoencoder
168(2)
7.4.1 Architecture
169(1)
7.4.2 Merits and Demerits
169(1)
7.4.3 Applications
170(1)
7.5 Recursive Neural Network (RCNN)
170(3)
7.5.1 Architecture
170(2)
7.5.2 Merits and Demerits
172(1)
7.5.3 Applications
172(1)
7.6 Deep Reinforcement Learning
173(2)
7.6.1 Architecture
174(1)
7.6.2 Merits and Demerits
174(1)
7.6.3 Applications
174(1)
7.7 Deep Belief Networks (DBNS)
175(2)
7.7.1 Architecture
176(1)
7.7.2 Merits and Demerits
176(1)
7.7.3 Applications
176(1)
7.8 Conclusion
177(1)
References
177(4)
8 A Proposed LSTM-Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG
181(26)
Rupali Gill
Jaiteg Singh
8.1 Introduction
182(1)
8.2 Background and Motivation
183(2)
8.2.1 Emotion Model
183(1)
8.2.2 Neuromarketing and BCI
184(1)
8.2.3 EEG Signal
185(1)
8.3 Related Work
185(10)
8.3.1 Machine Learning
186(5)
8.3.2 Deep Learning
191(1)
8.3.2.1 Fast Feed Neural Networks
193(1)
8.3.2.2 Recurrent Neural Networks
193(1)
8.3.2.3 Convolutional Neural Networks
194(1)
8.4 Methodology of Proposed System
195(3)
8.4.1 DEAP Dataset
196(1)
8.4.2 Analyzing the Dataset
196(1)
8.4.3 Long Short-Term Memory
197(1)
8.4.4 Experimental Setup
197(1)
8.4.5 Data Set Collection
197(1)
8.5 Results and Discussions
198(1)
8.5.1 LSTM Model Training and Accuracy
198(1)
8.6 Conclusion
199(1)
References
199(8)
9 An Extensive Survey of Applications of Advanced Deep Learning Algorithms on Detection of Neurodegenerative Diseases and the Tackling Procedure in Their Treatment Protocol
207(24)
S. Vignesh Baalaji
M. Vergin Raja Sarobin
L. Jani Anbarasi
S. Graceline Jasmine
P. Rukmani
9.1 Introduction
208(1)
9.2 Story of Alzheimer's Disease
208(2)
9.3 Datasets
210(1)
9.3.1 ADNI
210(1)
9.3.2 OASIS
210(1)
9.4 Story of Parkinson's Disease
211(1)
9.5 A Review on Learning Algorithms
212(3)
9.5.1 Convolutional Neural Network (CNN)
212(1)
9.5.2 Restricted Boltzmann Machine
213(1)
9.5.3 Siamese Neural Networks
213(1)
9.5.4 Residual Network (ResNet)
214(1)
9.5.5 U-Net
214(1)
9.5.6 LSTM
214(1)
9.5.7 Support Vector Machine
215(1)
9.6 A Review on Methodologies
215(9)
9.6.1 Prediction of Alzheimer's Disease
215(6)
9.6.2 Prediction of Parkinson's Disease
221(2)
9.6.3 Detection of Attacks on Deep Brain Stimulation
223(1)
9.7 Results and Discussion
224(1)
9.8 Conclusion
224(3)
References
227(4)
10 Emerging Innovations in the Near Future Using Deep Learning Techniques
231(24)
Akshara Pramod
Harsh Sankar Naicker
Amit Kumar Tyagi
10.1 Introduction
232(2)
10.2 Related Work
234(1)
10.3 Motivation
235(1)
10.4 Future With Deep Learning/Emerging Innovations in Near Future With Deep Learning
236(8)
10.4.1 Deep Learning for Image Classification and Processing
237(1)
10.4.2 Deep Learning for Medical Image Recognition
237(1)
10.4.3 Computational Intelligence for Facial Recognition
238(1)
10.4.4 Deep Learning for Clinical and Health Informatics
238(1)
10.4.5 Fuzzy Logic for Medical Applications
239(1)
10.4.6 Other Intelligent-Based Methods for Biomedical and Healthcare
239(1)
10.4.7 Other Applications
239(5)
10.5 Open Issues and Future Research Directions
244(5)
10.5.1 Joint Representation Learning From User and Item Content Information
244(1)
10.5.2 Explainable Recommendation With Deep Learning
245(1)
10.5.3 Going Deeper for Recommendation
245(1)
10.5.4 Machine Reasoning for Recommendation
246(1)
10.5.5 Cross Domain Recommendation With Deep Neural Networks
246(1)
10.5.6 Deep Multi-Task Learning for Recommendation
247(1)
10.5.7 Scalability of Deep Neural Networks for Recommendation
247(1)
10.5.8 Urge for a Better and Unified Evaluation
248(1)
10.6 Deep Learning: Opportunities and Challenges
249(1)
10.7 Argument with Machine Learning and Other Available Techniques
250(1)
10.8 Conclusion With Future Work
251(1)
Acknowledgement
252(1)
References
252(3)
11 Optimization Techniques in Deep Learning Scenarios: An Empirical Comparison
255(28)
Ajeet K. Jain
P.V.R.D. Prasad Rao
K. Venkatesh Sharma
11.1 Introduction
256(2)
11.1.1 Background and Related Work
256(2)
11.2 Optimization and Role of Optimizer in DL
258(7)
11.2.1 Deep Network Architecture
259(1)
11.2.2 Proper Initialization
260(1)
11.2.3 Representation, Optimization, and Generalization
261(1)
11.2.4 Optimization Issues
261(1)
11.2.5 Stochastic GD Optimization
262(1)
11.2.6 Stochastic Gradient Descent with Momentum
263(1)
11.2.7 SGD With Nesterov Momentum
264(1)
11.3 Various Optimizers in DL Practitioner Scenario
265(5)
11.3.1 AdaGrad Optimizer
265(2)
11.3.2 RMSProp
267(1)
11.3.3 Adam
267(2)
11.3.4 AdaMax
269(1)
11.3.5 AMSGrad
269(1)
11.4 Recent Optimizers in the Pipeline
270(3)
11.4.1 EVE
270(1)
11.4.2 RAdam
271(1)
11.4.3 MAS (Mixing ADAM and SGD)
271(1)
11.4.4 Lottery Ticket Hypothesis
272(1)
11.5 Experiment and Results
273(5)
11.5.1 Web Resource
273(4)
11.5.2 Resource
277(1)
11.6 Discussion and Conclusion
278(1)
References
279(4)
Part 3: Introduction to Advanced Analytics 283(108)
12 Big Data Platforms
285(26)
Sharmila Gaikwad
Jignesh Patil
12.1 Visualization in Big Data
286(19)
12.1.1 Introduction to Big Data
286(1)
12.1.2 Techniques of Visualization
287(15)
12.1.3 Case Study on Data Visualization
302(3)
12.2 Security in Big Data
305(4)
12.2.1 Introduction of Data Breach
305(1)
12.2.2 Data Security Challenges
306(1)
12.2.3 Data Breaches
307(1)
12.2.4 Data Security Achieved
307(2)
12.2.5 Findings: Case Study of Data Breach
309(1)
12.3 Conclusion
309(1)
References
309(2)
13 Smart City Governance Using Big Data Technologies
311(14)
K. Raghava Rao
D. Sateesh Kumar
13.1 Objective
312(1)
13.2 Introduction
312(2)
13.3 Literature Survey
314(1)
13.4 Smart Governance Status
314(4)
13.4.1 International
314(2)
13.4.2 National
316(2)
13.5 Methodology and Implementation Approach
318(4)
13.5.1 Data Generation
319(1)
13.5.2 Data Acquisition
319(1)
13.5.3 Data Analytics
319(3)
13.6 Outcome of the Smart Governance
322(1)
13.7 Conclusion
323(1)
References
323(2)
14 Big Data Analytics With Cloud, Fog, and Edge Computing
325(26)
Deepti Goyal
Amit Kumar Tyagi
S.U. Aswathy
14.1 Introduction to Cloud, Fog, and Edge Computing
326(4)
14.2 Evolution of Computing Terms and Its Related Works
330(2)
14.3 Motivation
332(1)
14.4 Importance of Cloud, Fog, and Edge Computing in Various Applications
333(1)
14.5 Requirement and Importance of Analytics (General) in Cloud, Fog, and Edge Computing
334(1)
14.6 Existing Tools for Making a Reliable Communication and Discussion of a Use Case (with Respect to Cloud, Fog, and Edge Computing)
335(3)
14.6.1 CloudSim
335(1)
14.6.2 SPECI
336(1)
14.6.3 Green Cloud
336(1)
14.6.4 OCT (Open Cloud Testbed)
337(1)
14.6.5 Open Cirrus
337(1)
14.6.6 GroudSim
338(1)
14.6.7 Network CloudSim
338(1)
14.7 Tools Available for Advanced Analytics (for Big Data Stored in Cloud, Fog, and Edge Computing Environment)
338(2)
14.7.1 Microsoft HDlnsight
338(1)
14.7.2 Skytree
339(1)
14.7.3 Splice Machine
339(1)
14.7.4 Spark
339(1)
14.7.5 Apache SAMOA
339(1)
14.7.6 Elastic Search
339(1)
14.7.7 R-Programming
339(1)
14.8 Importance of Big Data Analytics for Cyber-Security and Privacy for Cloud-IoT Systems
340(1)
14.8.1 Risk Management
340(1)
14.8.2 Predictive Models
340(1)
14.8.3 Secure With Penetration Testing
340(1)
14.8.4 Bottom Line
341(1)
14.8.5 Others: Internet of Things-Based Intelligent Applications
341(1)
14.9 An Use Case with Real World Applications (with Respect to Big Data Analytics) Related to Cloud, Fog, and Edge Computing
341(1)
14.10 Issues and Challenges Faced by Big Data Analytics (in Cloud, Fog, and Edge Computing Environments)
342(2)
14.10.1 Cloud Issues
343(1)
14.11 Opportunities for the Future in Cloud, Fog, and Edge Computing Environments (or Research Gaps)
344(1)
14.12 Conclusion
345(1)
References
346(5)
15 Big Data in Healthcare: Applications and Challenges
351(14)
V. Shyamala Susan
K. Juliana Gnana Selvi
Ir. Bambang Sugiyono Agus Purwono
15.1 Introduction
352(4)
15.1.1 Big Data in Healthcare
352(1)
15.1.2 The 5V's Healthcare Big Data Characteristics
353(1)
15.1.2.1 Volume
353(1)
15.1.2.2 Velocity
353(1)
15.1.2.3 Variety
353(1)
15.1.2.4 Veracity
353(1)
15.1.2.5 Value
353(1)
15.1.3 Various Varieties of Big Data Analytical (BDA) in Healthcare
353(1)
15.1.4 Application of Big Data Analytics in Healthcare
354(1)
15.1.5 Benefits of Big Data in the Health Industry
355(1)
15.2 Analytical Techniques for Big Data in Healthcare
356(4)
15.2.1 Platforms and Tools for Healthcare Data
357(1)
15.3 Challenges
357(1)
15.3.1 Storage Challenges
357(1)
15.3.2 Cleaning
358(1)
15.3.3 Data Quality
358(1)
15.3.4 Data Security
358(1)
15.3.5 Missing or Incomplete Data
358(1)
15.3.6 Information Sharing
358(1)
15.3.7 Overcoming the Big Data Talent and Cost Limitations
359(1)
15.3.8 Financial Obstructions
359(1)
15.3.9 Volume
359(1)
15.3.10 Technology Adoption
360(1)
15.4 What is the Eventual Fate of Big Data in Healthcare Services?
360(1)
15.5 Conclusion
361(1)
References
361(4)
16 The Fog/Edge Computing: Challenges, Serious Concerns, and the Road Ahead
365(26)
R. Varsha
Siddharth M. Nair
Amit Kumar Tyagi
16.1 Introduction
366(2)
16.1.1 Organization of the Work
368(1)
16.2 Motivation
368(1)
16.3 Background
369(2)
16.4 Fog and Edge Computing-Based Applications
371(3)
16.5 Machine Learning and Internet of Things-Based Cloud, Fog, and Edge Computing Applications
374(2)
16.6 Threats Mitigated in Fog and Edge Computing-Based Applications
376(2)
16.7 Critical Challenges and Serious Concerns Toward Fog/Edge Computing and Its Applications
378(3)
16.8 Possible Countermeasures
381(2)
16.9 Opportunities for 21st Century Toward Fog and Edge Computing
383(4)
16.9.1 5G and Edge Computing as Vehicles for Transformation of Mobility in Smart Cities
383(1)
16.9.2 Artificial Intelligence for Cloud Computing and Edge Computing
384(3)
16.10 Conclusion
387(1)
References
387(4)
Index 391
Archana Mire, PhD, is an assistant professor in the Computer Engineering Department, Terna Engineering College, Navi Mumbai, India. She has published many research articles in peer-reviewed journals.

Shaveta Malik, PhD, is an associate professor in the Computer Engineering Department (NBA accredited), Terna Engineering College, Nerul, India. She has published many research articles in peer-reviewed journals.

Amit Kumar Tyagi, PhD, is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber-physical systems, and computer vision.
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