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E-raamat: Machine Learning Algorithms and Applications

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  • Ilmumisaeg: 16-Aug-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119769255
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Machine Learning Algorithms and ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-Aug-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119769255
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Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms.

The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program. 

Acknowledgments xv
Preface xvii
Part 1: Machine Learning for Industrial Applications 1(140)
1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services
3(20)
Priyank Jain
Gagandeep Kaur
1.1 Introduction
4(1)
1.1.1 Open Government Data Initiative
4(1)
1.1.2 Air Quality
4(1)
1.1.3 Impact of Lockdown on Air Quality
5(1)
1.2 Literature Survey
5(1)
1.3 Implementation Details
6(5)
1.3.1 Proposed Methodology
7(1)
1.3.2 System Specifications
8(1)
1.3.3 Algorithms
8(2)
1.3.4 Control Flow
10(1)
1.4 Results and Discussions
11(10)
1.5 Conclusion
21(1)
References
21(2)
2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning
23(18)
Shreedhar Rangappa
Ajay A.
G.S. Rajanna
2.1 Introduction
23(1)
2.2 Conventional Silkworm Egg Detection Approaches
24(1)
2.3 Proposed Method
25(10)
2.3.1 Model Architecture
26(2)
2.3.2 Foreground-Background Segmentation
28(2)
2.3.3 Egg Location Predictor
30(1)
2.3.4 Predicting Egg Class
31(4)
2.4 Dataset Generation
35(1)
2.5 Results
35(2)
2.6 Conclusion
37(1)
Acknowledgment
38(1)
References
38(3)
3 A Wind Speed Prediction System Using Deep Neural Networks
41(20)
Jaseena K.U.
Binsu C. Kovoor
3.1 Introduction
42(3)
3.2 Methodology
45(7)
3.2.1 Deep Neural Networks
45(2)
3.2.2 The Proposed Method
47(1)
3.2.2.1 Data Acquisition
47(1)
3.2.2.2 Data Pre-Processing
48(1)
3.2.2.3 Model Selection and Training
50(1)
3.2.2.4 Performance Evaluation
51(1)
3.2.2.5 Visualization
51(1)
3.3 Results and Discussions
52(5)
3.3.1 Selection of Parameters
52(1)
3.3.2 Comparison of Models
53(4)
3.4 Conclusion
57(1)
References
57(4)
4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections
61(16)
Varshaneya V.
S. Balasubramanian
Darshan Gera
4.1 Introduction
61(1)
4.2 Related Work
62(1)
4.3 Preliminaries
63(3)
4.3.1 ResNet
63(1)
4.3.2 Squeeze-and-Excitation Block
64(2)
4.4 Proposed Model
66(2)
4.4.1 Effect of Bridge Connections in ResNet
66(1)
4.4.2 Res-SE-Net: Proposed Architecture
67(1)
4.5 Experiments
68(1)
4.5.1 Datasets
68(1)
4.5.2 Experimental Setup
68(1)
4.6 Results
69(4)
4.7 Conclusion
73(1)
References
74(3)
5 Hitting the Success Notes of Deep Learning
77(22)
Sakshi Aggarwal
Navjot Singh
K.K. Mishra
5.1 Genesis
78(1)
5.2 The Big Picture: Artificial Neural Network
79(1)
5.3 Delineating the Cornerstones
80(2)
5.3.1 Artificial Neural Network vs. Machine Learning
80(1)
5.3.2 Machine Learning vs. Deep Learning
81(1)
5.3.3 Artificial Neural Network vs. Deep Learning
81(1)
5.4 Deep Learning Architectures
82(3)
5.4.1 Unsupervised Pre-Trained Networks
82(1)
5.4.2 Convolutional Neural Networks
83(1)
5.4.3 Recurrent Neural Networks
84(1)
5.4.4 Recursive Neural Network
85(1)
5.5 Why is CNN Preferred for Computer Vision Applications?
85(4)
5.5.1 Convolutional Layer
86(1)
5.5.2 Nonlinear Layer
86(1)
5.5.3 Pooling Layer
87(1)
5.5.4 Fully Connected Layer
87(2)
5.6 Unravel Deep Learning in Medical Diagnostic Systems
89(5)
5.7 Challenges and Future Expectations
94(1)
5.8 Conclusion
94(1)
References
95(4)
6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks
99(18)
Diwakar Tripathi
Damodar Reddy Edla
Annushree Bablani
Venkatanareshbabu Kuppili
6.1 Introduction
100(1)
6.1.1 Motivation
100(1)
6.2 Literature Survey
101(2)
6.3 Proposed Model for Credit Scoring
103(4)
6.3.1 Stage-1: Feature Selection
104(1)
6.3.2 Proposed Criteria Function
105(1)
6.3.3 Stage-2: Ensemble Classifier
106(1)
6.4 Results and Discussion
107(5)
6.4.1 Experimental Datasets and Performance Measures
107(1)
6.4.2 Classification Results With Feature Selection
108(4)
6.5 Conclusion
112(1)
References
113(4)
7 Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization
117(24)
Sreeja M.U.
Binsu C. Kovoor
7.1 Introduction
118(1)
7.2 Related Works
119(3)
7.3 Feature Agglomeration Clustering
122(1)
7.4 Proposed Methodology
122(7)
7.4.1 Pre-Processing
123(2)
7.4.2 Modified Block Clustering Using Feature Agglomeration Technique
125(2)
7.4.3 Post-Processing and Summary Generation
127(2)
7.5 Results and Analysis
129(9)
7.5.1 Experimental Setup and Data Sets Used
129(1)
7.5.2 Evaluation Metrics
130(1)
7.5.3 Evaluation
131(7)
7.6 Conclusion
138(1)
References
138(3)
Part 2: Machine Learning for Healthcare Systems 141(34)
8 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier
143(16)
Saroj Kumar Pandeyz
Rekh Ram Janghel
Vaibhav Gupta
8.1 Introduction
143(2)
8.2 Materials and Methods
145(4)
8.2.1 MIT-BIH Arrhythmia Database
146(1)
8.2.2 Signal Pre-Processing
147(1)
8.2.3 Feature Extraction
147(1)
8.2.4 Classification
148(1)
8.2.4.1 XGBoost Classifier
148(1)
8.2.4.2 AdaBoost Classifier
149(1)
8.3 Results and Discussion
149(6)
8.4 Conclusion
155(1)
References
156(3)
9 GSA-Based Approach for Gene Selection from Microarray Gene Expression Data
159(16)
Pintu Kumar Ram
Pratyay Kuila
9.1 Introduction
159(2)
9.2 Related Works
161(1)
9.3 An Overview of Gravitational Search Algorithm
162(1)
9.4 Proposed Model
163(3)
9.4.1 Pre-Processing
163(1)
9.4.2 Proposed GSA-Based Feature Selection
164(2)
9.5 Simulation Results
166(6)
9.5.1 Biological Analysis
168(4)
9.6 Conclusion
172(1)
References
172(3)
Part 3: Machine Learning for Security Systems 175(72)
10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching
177(16)
Ritesh Vyas
Tirupathiraju Kanumuri
Gyanendra Sheoran
Pawan Dubey
10.1 Introduction
177(2)
10.1.1 Related Works
178(1)
10.2 Preliminary Details
179(3)
10.2.1 Fusion
181(1)
10.3 Experiments and Results
182(8)
10.3.1 Databases
182(1)
10.3.2 Experimental Results
182(1)
10.3.2.1 Same Spectral Matchings
183(1)
10.3.2.2 Cross Spectral Matchings
184(2)
10.3.3 Feature-Level Fusion
186(3)
10.3.4 Score-Level Fusion
189(1)
10.4 Conclusions
190(1)
References
190(3)
11 Fake Social Media Profile Detection
193(18)
Umita Deepak Joshi
Vanshika
Ajay Pratap Singh
Tushar Rajesh Pahuja
Smita Naval
Gaurav Singal
11.1 Introduction
194(1)
11.2 Related Work
195(2)
11.3 Methodology
197(7)
11.3.1 Dataset
197(1)
11.3.2 Pre-Processing
198(1)
11.3.3 Artificial Neural Network
199(3)
11.3.4 Random Forest
202(1)
11.3.5 Extreme Gradient Boost
202(2)
11.3.6 Long Short-Term Memory
204(1)
11.4 Experimental Results
204(3)
11.5 Conclusion and Future Work
207(1)
Acknowledgment
207(1)
References
207(4)
12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks
211(18)
E.M.V. Naga Karthik
Madan Gopal
12.1 Introduction
212(1)
12.2 Related Work
213(2)
12.3 Methods and Materials
215(7)
12.3.1 Feature Extraction Using SURF
215(1)
12.3.2 Feature Extraction Using Conventional Methods
216(1)
12.3.2.1 Local Orientation Estimation
216(1)
12.3.2.2 Singular Region Detection
218(1)
12.3.3 Proposed CNN Architecture
219(2)
12.3.4 Dataset
221(1)
12.3.5 Computational Environment
221(1)
12.4 Results
222(4)
12.4.1 Feature Extraction and Visualization
223(3)
12.5 Conclusion
226(1)
Acknowledgments
226(1)
References
226(3)
13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features
229(18)
M. Srinivas
Sanjeev Saurav
Akshay Nayak
Murukessan A.P.
13.1 Introduction
230(2)
13.2 Related Work
232(3)
13.3 Proposed Method
235(7)
13.3.1 Convolutional Neural Network
236(1)
13.3.1.1 Convolution Layer
236(1)
13.3.1.2 Pooling Layer
237(1)
13.3.1.3 ReLU Layer
238(1)
13.3.1.4 Fully Connected Layer
238(1)
13.3.2 Histogram of Gradient
239(1)
13.3.3 Facial Landmark Detection
240(1)
13.3.4 Support Vector Machine
241(1)
13.3.5 Model Merging and Learning
242(1)
13.4 Experimental Results
242(3)
13.4.1 Datasets
242(3)
13.5 Conclusion
245(1)
Acknowledgment
245(1)
References
245(2)
Part 4: Machine Learning for Classification and Information Retrieval Systems 247(84)
14 AnimNet: An Animal Classification Network using Deep Learning
249(18)
Kanak Manjari
Kriti Singhal
Madhushi Verma
Gaurav Singal
14.1 Introduction
249(3)
14.1.1 Feature Extraction
250(1)
14.1.2 Artificial Neural Network
250(1)
14.1.3 Transfer Learning
251(1)
14.2 Related Work
252(2)
14.3 Proposed Methodology
254(4)
14.3.1 Dataset Preparation
254(1)
14.3.2 Training the Model
254(4)
14.4 Results
258(5)
14.4.1 Using Pre-Trained Networks
259(1)
14.4.2 Using AnimNet
259(1)
14.4.3 Test Analysis
260(3)
14.5 Conclusion
263(1)
References
264(3)
15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis
267(22)
Alok Kumar
Renu Jain
15.1 Introduction
268(1)
15.2 Related Work
269(2)
15.3 The Proposed System
271(11)
15.3.1 Feedback Collector
272(1)
15.3.2 Feedback Pre-Processor
272(1)
15.3.3 Feature Selector
272(2)
15.3.4 Feature Validator
274(1)
15.3.4.1 Removal of Terms From Tentative List of Features on the Basis of Syntactic Knowledge
274(1)
15.3.4.2 Removal of Least Significant Terms on the Basis of Contextual Knowledge
276(1)
15.3.4.3 Removal of Less Significant Terms on the Basis of Association With Sentiment Words
277(1)
15.3.4.4 Removal of Terms Having Similar Sense
278(1)
15.3.4.5 Removal of Terms Having Same Root
279(1)
15.3.4.6 Identification of Multi-Term Features
279(1)
15.3.4.7 Identification of Less Frequent Feature
279(2)
15.3.5 Feature Concluder
281(1)
15.4 Result Analysis
282(4)
15.5 Conclusion
286(1)
References
286(3)
16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding
289(16)
Amol P. Bhopale
Ashish Tiwari
16.1 Introduction
290(1)
16.2 Related Work
291(1)
16.3 Proposed Approach
292(5)
16.3.1 Phrase Extraction
292(2)
16.3.2 Corpus Annotation
294(1)
16.3.3 Phrase Embedding
294(3)
16.4 Experimental Setup
297(1)
16.4.1 Dataset Preparation
297(1)
16.4.2 Parameter Setting
297(1)
16.5 Results
298(5)
16.5.1 Phrase Extraction
298(1)
16.5.2 Phrase Embedding
298(5)
16.6 Conclusion
303(1)
References
303(2)
17 Image Anonymization Using Deep Convolutional Generative Adversarial Network
305(26)
Ashish Undirwade
Sujit Das
17.1 Introduction
306(4)
17.2 Background Information
310(9)
17.2.1 Black Box and White Box Attacks
310(1)
17.2.2 Model Inversion Attack
311(1)
17.2.3 Differential Privacy
312(1)
17.2.3.1 Definition
312(1)
17.2.4 Generative Adversarial Network
313(3)
17.2.5 Earth-Mover (EM) Distance/Wasserstein Metric
316(1)
17.2.6 Wasserstein GAN
317(1)
17.2.7 Improved Wasserstein GAN (WGAN-GP)
317(1)
17.2.8 KL Divergence and JS Divergence
318(1)
17.2.9 DCGAN
319(1)
17.3 Image Anonymization to Prevent Model Inversion Attack
319(7)
17.3.1 Algorithm
321(1)
17.3.2 Training
322(1)
17.3.3 Noise Amplifier
323(1)
17.3.4 Dataset
324(1)
17.3.5 Model Architecture
324(1)
17.3.6 Working
325(1)
17.3.7 Privacy Gain
325(1)
17.4 Results and Analysis
326(2)
17.5 Conclusion
328(1)
References
329(2)
Index 331
Mettu Srinivas PhD from the Indian Institute of Technology Hyderabad, and is currently an assistant professor in the Department of Computer Science and Engineering, NIT Warangal, India.

G. Sucharitha PhD from KL University, Vijayawada and is currently an assistant professor in the Department of Electronics and Communication Engineering at ICFAI Foundation for Higher Education Hyderabad.

Anjanna Matta PhD from the Indian Institute of Technology Hyderabad and is currently an assistant professor in the Department of Mathematics at ICFAI Foundation for Higher Education Hyderabad.

Prasenjit Chatterjee PhD is an associate professor in the Mechanical Engineering Department at MCKV Institute of Engineering, India.
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