Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more.
In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.
- Provides insights into the theory, algorithms, implementation and the application of deep learning techniques
- Covers a wide range of applications of deep learning across smart healthcare and smart engineering
- Investigates the development of new models and how they can be exploited to find appropriate solutions
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| Preface |
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xiii | |
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1 An introduction to deep learning applications in biometric recognition |
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6 | (22) |
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3 Comparative analysis among different modalities |
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28 | (1) |
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29 | (2) |
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31 | (6) |
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32 | (5) |
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2 Deep learning in big data and data mining |
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37 | (26) |
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37 | (1) |
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2 Overview of big data analysis |
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38 | (11) |
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49 | (3) |
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4 Applications of deep learning in data mining |
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52 | (7) |
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59 | (4) |
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60 | (1) |
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61 | (2) |
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3 An overview of deep learning in big data, image, and signal processing in the modern digital age |
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63 | (26) |
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Ana Carolina Borges Monteiro |
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63 | (16) |
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79 | (3) |
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82 | (1) |
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83 | (6) |
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84 | (5) |
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4 Predicting retweet class using deep learning |
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89 | (24) |
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89 | (8) |
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2 Related work and proposed work |
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97 | (3) |
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3 Data collection and preparation |
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100 | (4) |
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4 Research set-up and experimentation |
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104 | (2) |
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106 | (2) |
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108 | (1) |
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109 | (4) |
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109 | (4) |
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5 Role of the Internet of Things and deep learning for the growth of healthcare technology |
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113 | (16) |
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1 Introduction to the Internet of Things |
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113 | (2) |
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2 Role of IoT in the healthcare sector |
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115 | (3) |
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118 | (1) |
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4 Role of deep learning in IoT |
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119 | (1) |
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5 Design of IoT for a hospital |
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120 | (3) |
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6 Security features considered while designing and implementing IoT for healthcare |
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123 | (1) |
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7 Advantages and limitations of IoT for healthcare technology |
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124 | (1) |
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8 Discussions, conclusions, and future scope of IoT |
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125 | (4) |
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126 | (3) |
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6 Deep learning methodology proposal for the classification of erythrocytes and leukocytes |
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129 | (28) |
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Ana Carolina Borges Monteiro |
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129 | (3) |
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132 | (3) |
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135 | (4) |
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4 Convolutional neural network |
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139 | (2) |
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141 | (4) |
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145 | (3) |
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148 | (3) |
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151 | (1) |
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9 Future research directions |
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152 | (5) |
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152 | (5) |
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7 Dementia detection using the deep convolution neural network method |
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157 | (26) |
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157 | (3) |
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160 | (2) |
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3 Basics of a convolution neural network |
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162 | (8) |
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170 | (4) |
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174 | (4) |
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178 | (5) |
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178 | (5) |
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8 Deep similarity learning for disease prediction |
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183 | (24) |
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183 | (6) |
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189 | (4) |
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193 | (8) |
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201 | (2) |
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5 Conclusions and future work |
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203 | (4) |
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204 | (3) |
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9 Changing the outlook of security and privacy with approaches to deep learning |
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207 | (20) |
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207 | (3) |
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2 Birth and history of deep learning |
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210 | (1) |
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3 Frameworks of deep learning |
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211 | (2) |
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4 Statistics behind deep learning algorithms and neural networks |
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213 | (1) |
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5 Deep learning algorithms for securing networks |
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214 | (4) |
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6 Performance measures for intrusion detection systems |
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218 | (1) |
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7 Security aspects changing with deep learning |
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219 | (4) |
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8 Conclusion and future work |
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223 | (4) |
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224 | (3) |
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10 E-CART: an improved data stream mining approach |
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227 | (14) |
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227 | (2) |
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229 | (4) |
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3 E-CART: proposed approach |
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233 | (2) |
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235 | (3) |
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238 | (3) |
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238 | (3) |
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11 Deep learning-based detection and classification of adenocarcinoma cell nuclei |
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241 | (24) |
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241 | (2) |
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2 Basics of a convolution neural network |
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243 | (7) |
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250 | (3) |
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4 Proposed system architecture and methodology |
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253 | (4) |
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257 | (5) |
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262 | (3) |
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263 | (2) |
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12 Segmentation and classification of hand symbol images using classifiers |
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265 | (12) |
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265 | (1) |
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266 | (1) |
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3 Hand symbol classification mechanism |
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267 | (2) |
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269 | (1) |
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270 | (4) |
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274 | (3) |
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274 | (3) |
| Index |
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277 | |
Vincenzo Piuri received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He is a Full Professor in computer engineering at the University of Milan, Italy (since 2000), where he was also Department Chair (2007-2012). He was previously Associate Professor at Politecnico di Milano, Italy and Visiting Professor at the University of Texas at Austin and at George Mason University, USA. His main research interests include: artificial intelligence, computational intelligence, intelligent systems, machine learning, pattern analysis and recognition, signal and image processing, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, fault tolerance, dependability, and cloud computing infrastructures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters. He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He has been IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-19), and Associate Editor of the IEEE Transactions on Cloud Computing and IEEE Access, and has been Associate Editor of the IEEE Transactions on Computers, the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement. Sandeep Raj has been an Assistant Professor with the Department of Electronics and Communication Engineering, Indian Institute of Information Technology Bhagalpur, Sabour, India since 2018. Prior to this position (2012 - 20180, he was a was a Visiting Faculty with the National Institute of Technology Patna, India. His current research interests include digital signal processing, biomedical engineering, machine learning, internet-of-things (IoT), embedded systems design, and fabrication. He received the B. Tech. degree in Electrical and Electronics engineering from Allahabad Agricultural Institute Deemed University, Allahabad, India, in 2009, the M. Tech. degree in electrical engineering (Gold-Medalist) and received DST INSPIRE Fellowship for pursuing the Ph.D. degree in Electrical Engineering from Indian Institute of Technology Patna, Bihta, India, 2018. He is a member of IEEE and has published more than 11 SCI/Scopus journal articles, 5 conference papers and 1 book chapter. He is serving as a reviewer for several journals including IEEE Transactions on Industrial Electronics, IEEE Journal of Biomedical and Health Informatics, IEEE Signal Processing letters, IEEE Transactions on Instrumentation and Measurement, IEEE Access, Computer Methods and Programs in Biomedicine (Elsevier), Computers in Biology and Medicine (Elsevier), Australasian Physical & Engineering Sciences in Medicine (Springer), Journal of King Saud University - Computer and Information Sciences - (Elsevier), Biomedical Engineering: Applications, Basis and Communications (BME), IETE Journal of Research. Angelo Genovese received B.Sc., M.Sc., and Ph.D. degrees in Computer Science in 2007, 2010, and 2014 respectively, from Università degli Studi di Milano, Italy. From 2014 to 2019, he was a Postdoctoral Research Fellow and since 2015 he is a member of the Industrial, Environmental, and Biometric Informatics Laboratory (IEBIL) at the Università degli Studi di Milano, Italy. From June to August 2017, he was a Visiting Researcher at the University of Toronto, ON, Canada. Since 2019, he is Assistant Professor at Università degli Studi di Milano, Italy, Department of Computer Science. His research interests include signal and image processing, three-dimensional reconstruction, computational intelligence technologies, and design methodologies and algorithms for self-adapting systems, applied to industrial and environmental monitoring systems and biometric recognition. In the biometrics field, his focuses are on highly usable touch-based and touchless fingerprint and palmprint recognition, as well as recognition based on soft biometric traits. He is an Associate Editor of the Journal of Ambient Intelligence and Humanized Computing and Array. He has served as Program Chair/Co-Chair for the 2019 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2019), the 2018 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS 2018), the 2018 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018), and the 2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS 2017). He is a Member of the IEEE, the IEEE Biometrics Council, the IEEE Computational Intelligence Society, the IEEE Italy Section Systems Council Chapter, and the GRIN (Gruppo di Informatica). Rajshree Srivastava is an Assistant Professor at DIT University Dehradun in the department of Computer Science and Engineering. She has completed her M. Tech. from JIIT Noida in CSE-IS, B. Tech. from RTU in Computer Science and Engineering. She is a life time member of (IEAE), a member of IEEE, CSI, ACM, ACM-W, IAENG, Internet of Things. Her area of research is in machine learning, big data, biomedical, privacy security. She has published book chapters; Scopus indexed papers and many in IEEE/Springer Conferences. Currently she is also session chair holder of PDGC 2018, ICETIT 2019. Reviewer of the Journal entitled International Journal of Handheld Computing Research (IJHCR), IGI Global Publisher. She has guided many undergraduate students projects. She has attended various FDP, Short term courses, Workshops from IITs, NITS. She has also edited some of the Springer, de-Gruyter edited book in the field of AI, Health Care Informatics.
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