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.
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.
1. An Introduction/ theoretical understanding to deep learning –
challenges, feasibility in domains
2. Deep learning for big data
3. Deep learning in signal processing
4. Deep learning in image processing
5. Deep learning in video processing
6. Deep learning in audio/speech processing
7. Deep learning in data mining
8. Deep learning in healthcare
9. Deep learning in biomedical research
10. Deep learning in agriculture
11. Deep learning in environmental sciences
12. Deep learning in economics/e-commerce
13. Deep learning in forensics (biometrics recognition)
14. Deep learning in cybersecurity
15. Deep learning for smart cities, smart hospitals, and smart
homes
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 IIT’s, NIT’S. She
has also edited some of the Springer, de-Gruyter edited book in the
field of AI, Health Care Informatics.
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