Rhythmic Advantages in Big Data and Machine Learning
1st Edition
Published on 10. January 2022
XI, 262 pages
978-981-16-5723-8 (ISBN)
Description
The book discusses various aspects of biophysics. It starts from the popular article on neurobiology to quantum biology and ends up with the consciousness of a human being and in the universe. The authors have covered eight nine different aspects of natural intelligence, starting from time crystal found in the chemical biology to the vibrations and the resonance of proteins. They have covered a wide spectrum of hierarchical communication among different biological systems. Most importantly, authors have taken an utmost care that even school-level students fall in love with biophysics; it is simple and more of a textbook and definitely bring the readers to a world of biology and physics like never before. Most authors are experienced academicians, and they have used lucid and simple language to make the content interesting for the readers.
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Anirban Bandyopadhyay | Kanad Ray
Rhythmic Advantages in Big Data and Machine Learning
Book
01/2022
Springer
€192.59
Shipment within 3-4 weeks
Persons
Anirban Bandyopadhyay is Senior Principal Scientist at the National Institute for Materials Science (NIMS), Tsukuba, Japan. He received Ph.D. in Supramolecular Electronics at the Indian Association for the Cultivation of Science (IACS), Kolkata, on 2005. From 2005 to 2008, he was ICYS Research Fellow at the ICYS, NIMS, Japan, and worked on the brain-like bio-processor. In 2008, he joined as a permanent scientist at NIMS, working on the time crystal model of human brain and design-synthesis of brain-like organic jelly, written a book "Nanobrain: The making of an artificial brain from a time crystal," on 2020. From 2013 to 2014, he was a visiting scientist at the Massachusetts Institute of Technology (MIT), USA. He has received Hitachi Science and Technology Award, 2010, Inamori Foundation Award, 2011-2012, Kurata Foundation Award, Inamori Foundation Fellow (2011), and Sewa Society International Member, Japan.
Kanad Ray (Senior Member, IEEE) received the M.Sc. degree in Physics from Calcutta University and the Ph.D. degree in Physics from Jadavpur University, West Bengal, India. He has been Professor of Physics and Electronics and Communication and is presently working as Head of the Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan (AUR), Jaipur, India. His current research areas of interest include cognition, communication, electromagnetic field theory, antenna and wave propagation, microwave, computational biology, and applied physics. He has been serving as Editor for various Springer book series. He was Associate Editor of the Journal of Integrative Neuroscience (The Netherlands: IOS Press). He has visited several countries such as Netherlands, Turkey, China, Czechoslovakia, Russia, Portugal, Finland, Belgium, South Africa, Japan, Singapore, Thailand, and Malaysia for various academic missions.
Kanad Ray (Senior Member, IEEE) received the M.Sc. degree in Physics from Calcutta University and the Ph.D. degree in Physics from Jadavpur University, West Bengal, India. He has been Professor of Physics and Electronics and Communication and is presently working as Head of the Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan (AUR), Jaipur, India. His current research areas of interest include cognition, communication, electromagnetic field theory, antenna and wave propagation, microwave, computational biology, and applied physics. He has been serving as Editor for various Springer book series. He was Associate Editor of the Journal of Integrative Neuroscience (The Netherlands: IOS Press). He has visited several countries such as Netherlands, Turkey, China, Czechoslovakia, Russia, Portugal, Finland, Belgium, South Africa, Japan, Singapore, Thailand, and Malaysia for various academic missions.
Content
Data: periodicity and ways to unlock it's full potential.- Modulating Neural Oscillations with Transcranial Focused Ultrasound.- Minimization of thermal conductivity in nanostructures and geometric self-similar structures for thermoelectric applications.
