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- Job Title:
- Faculty
- Seniority:
- Professor
- Short Bio:
- I am a researcher and faculty at VIT University.My area of research is machine learning and deep learning.
Higest Qualification(Ph.D in CSE,Specialization : machine learning)
- LinkedIn Profile:
- http://https://www.linkedin.com/in/sanjiban-sekhar-roy-b1747b58
- Interests:
- Finding a new position
Handbook of Neural Computation,Elsevier
Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world.
Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.
Key Features
- Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more
- Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing
- Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods
Readership
Professional engineers, research engineers, graduate students, PhD students
Sanjiban Sekhar Roy's Blog
[New] Handbook of Deep Learning Applications (Springer)
Posted on March 1, 2019 at 11:30pm 0 Comments 1 Like
This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artifacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique…
ContinueNew Book : Big Data in Engineering Applications
Posted on June 9, 2018 at 8:30pm 0 Comments 2 Likes
BOOK : Big Data in Engineering Applications, SPRINGER. Editors: Roy, S.S., Samui, P., Deo, R., Ntalampiras, S. (Eds.).
Dear All,
The most anticipated book titled "Big Data in Engineering Applications" has been published by Springer.
This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from…
ContinueHandbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering
Posted on February 3, 2018 at 9:30am 0 Comments 1 Like
Description
The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of…
ContinueBook: Predictive Modeling and Optimization Methods in Science and Engineering
Posted on December 21, 2017 at 9:00pm 0 Comments 0 Likes
Our edited book entitled as "Predictive Modeling and Optimization Methods in Science and Engineering ", IGI Global (Editors: Dookie Kim- South Korea, Sanjiban Sekhar Roy -India,Tim Länsivaara -Finland, Ravinesh Deo-Australia,Pijush Samui-India) will be available online very soon.Hope this book will help research scholar and machine learning enthusiasts working on predictive models.
To contribute a chapter, click …
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