Machine Learning and Information Processing (eBook, PDF)
Proceedings of ICMLIP 2020
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Machine Learning and Information Processing (eBook, PDF)
Proceedings of ICMLIP 2020
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This book includes selected papers from the 2nd International Conference on Machine Learning and Information Processing (ICMLIP 2020), held at Vardhaman College of Engineering, Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, from November 28 to 29, 2020. It presents the latest developments and technical solutions in the areas of advanced computing and data sciences, covering machine learning, artificial intelligence, human–computer interaction, IoT, deep learning, image processing and pattern recognition, and signal and speech processing.
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This book includes selected papers from the 2nd International Conference on Machine Learning and Information Processing (ICMLIP 2020), held at Vardhaman College of Engineering, Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, from November 28 to 29, 2020. It presents the latest developments and technical solutions in the areas of advanced computing and data sciences, covering machine learning, artificial intelligence, human–computer interaction, IoT, deep learning, image processing and pattern recognition, and signal and speech processing.
Produktdetails
- Produktdetails
- Verlag: Springer Singapore
- Erscheinungstermin: 2. April 2021
- Englisch
- ISBN-13: 9789813348592
- Artikelnr.: 61391178
- Verlag: Springer Singapore
- Erscheinungstermin: 2. April 2021
- Englisch
- ISBN-13: 9789813348592
- Artikelnr.: 61391178
Debabala Swain is working as Associate Professor in the Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India. She has more than a decade of teaching and research experience. Dr. Swain has published number of research papers in peer-reviewed international journals, conferences, and book chapters. She has edited books of Springer, IEEE. Her area of research interest includes high-performance computing, information security, machine learning, and IoT.
Prasant Kumar Pattnaik, Ph.D. (Computer Science), Fellow of IETE, Senior Member of IEEE, is Professor at the School of Computer Engineering, KIIT Deemed University, Bhubaneswar. He has more than a decade of teaching and research experience. Dr. Pattnaik has published numbers of research papers in peer-reviewed international journals and conferences. He also published many edited book volumes in Springer and IGI Global Publication. His areas of interest include mobile computing, cloud computing, cyber security, intelligent systems, and brain–computer interface. He is one of the Associate Editors of Journal of Intelligent & Fuzzy Systems, IOS Press, and Intelligent Systems Book Series Editor of CRC Press, Taylor Francis Group.
Tushar Athawale is currently working as Computer Scientist at Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. He is in the domain of scientific visualization for analysis of large-scale data using tools, such as VisIt and ParaView and software development for multi-threaded visualization toolkit (VTK-m). He was Postdoctoral Fellow at the University of Utah’s Scientific Computing & Imaging (SCI) Institute with Prof. Chris R. Johnson as his advisor since October 2016. He received Ph.D. in Computer Science from the University of Florida in May 2015, and he worked with Prof. Alireza Entezari while pursuing his Ph.D. After his graduation, he worked as an application support engineer under the supervision of Robijn Hage in MathWorks, Inc., the developer of the leading computing software MATLAB. His primary research interests are in uncertainty quantification and statistical analysis.
Prasant Kumar Pattnaik, Ph.D. (Computer Science), Fellow of IETE, Senior Member of IEEE, is Professor at the School of Computer Engineering, KIIT Deemed University, Bhubaneswar. He has more than a decade of teaching and research experience. Dr. Pattnaik has published numbers of research papers in peer-reviewed international journals and conferences. He also published many edited book volumes in Springer and IGI Global Publication. His areas of interest include mobile computing, cloud computing, cyber security, intelligent systems, and brain–computer interface. He is one of the Associate Editors of Journal of Intelligent & Fuzzy Systems, IOS Press, and Intelligent Systems Book Series Editor of CRC Press, Taylor Francis Group.
Tushar Athawale is currently working as Computer Scientist at Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. He is in the domain of scientific visualization for analysis of large-scale data using tools, such as VisIt and ParaView and software development for multi-threaded visualization toolkit (VTK-m). He was Postdoctoral Fellow at the University of Utah’s Scientific Computing & Imaging (SCI) Institute with Prof. Chris R. Johnson as his advisor since October 2016. He received Ph.D. in Computer Science from the University of Florida in May 2015, and he worked with Prof. Alireza Entezari while pursuing his Ph.D. After his graduation, he worked as an application support engineer under the supervision of Robijn Hage in MathWorks, Inc., the developer of the leading computing software MATLAB. His primary research interests are in uncertainty quantification and statistical analysis.
Smart Queue Shopping using Rfid System.- Prediction and Classification of Biased and Fake News using NLP and Machine Learning models.- Smart Leaf Disease Detection Using Image Processing.- Unsupervised Image Generation & Manipulation using Deep Convolutional Adversarial Networks.- A Sentiment Analysis and Suicide Ideation Method using Various Machine Learning Algorithms on Twitter Tweets.- Video Categorization based on Sentiment Analysis of YouTube comments.- Credit Score Prediction using Machine Learning.- Stock Market Prediction Using Long Short-Term Memory Model.- Efficient Management of Web Personalization through Entropy and Similarity Analysis.- Artistic Media Stylization & Identification using Convolution Neural Networks.
Smart Queue Shopping using Rfid System.- Prediction and Classification of Biased and Fake News using NLP and Machine Learning models.- Smart Leaf Disease Detection Using Image Processing.- Unsupervised Image Generation & Manipulation using Deep Convolutional Adversarial Networks.- A Sentiment Analysis and Suicide Ideation Method using Various Machine Learning Algorithms on Twitter Tweets.- Video Categorization based on Sentiment Analysis of YouTube comments.- Credit Score Prediction using Machine Learning.- Stock Market Prediction Using Long Short-Term Memory Model.- Efficient Management of Web Personalization through Entropy and Similarity Analysis.- Artistic Media Stylization & Identification using Convolution Neural Networks.
Smart Queue Shopping using Rfid System.- Prediction and Classification of Biased and Fake News using NLP and Machine Learning models.- Smart Leaf Disease Detection Using Image Processing.- Unsupervised Image Generation & Manipulation using Deep Convolutional Adversarial Networks.- A Sentiment Analysis and Suicide Ideation Method using Various Machine Learning Algorithms on Twitter Tweets.- Video Categorization based on Sentiment Analysis of YouTube comments.- Credit Score Prediction using Machine Learning.- Stock Market Prediction Using Long Short-Term Memory Model.- Efficient Management of Web Personalization through Entropy and Similarity Analysis.- Artistic Media Stylization & Identification using Convolution Neural Networks.
Smart Queue Shopping using Rfid System.- Prediction and Classification of Biased and Fake News using NLP and Machine Learning models.- Smart Leaf Disease Detection Using Image Processing.- Unsupervised Image Generation & Manipulation using Deep Convolutional Adversarial Networks.- A Sentiment Analysis and Suicide Ideation Method using Various Machine Learning Algorithms on Twitter Tweets.- Video Categorization based on Sentiment Analysis of YouTube comments.- Credit Score Prediction using Machine Learning.- Stock Market Prediction Using Long Short-Term Memory Model.- Efficient Management of Web Personalization through Entropy and Similarity Analysis.- Artistic Media Stylization & Identification using Convolution Neural Networks.