E-Book, Englisch, Band 1274, 1402 Seiten, eBook
Kumar / Gunjan / Senatore Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 2
1. Auflage 2024
ISBN: 978-981-97-8043-3
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
ICDSMLA 2023, 15–16 December, Hyderabad, India
E-Book, Englisch, Band 1274, 1402 Seiten, eBook
Reihe: Lecture Notes in Electrical Engineering
ISBN: 978-981-97-8043-3
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book includes peer reviewed articles from the 5th International Conference on Data Science, Machine Learning and Applications, 2023, held at the G Narayanamma Institute of Technology and Sciences, Hyderabad on 15-16th December, India. ICDSMLA is one of the most prestigious conferences conceptualized in the field of Data Science & Machine Learning offering in-depth information on the latest developments in Artificial Intelligence, Machine Learning, Soft Computing, Human Computer Interaction, and various data science & machine learning applications. It provides a platform for academicians, scientists, researchers and professionals around the world to showcase broad range of perspectives, practices, and technical expertise in these fields. It offers participants the opportunity to stay informed about the latest developments in data science and machine learning.
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Weitere Infos & Material
.- Attention-Based Recurrent Neural Networks for Medical Image Classification.
.- Evolving ATM Networks for High Volume Transactions.
.- Unraveling Disease-Specific Diffusion Patterns in Human Brain Using Diffusion Tensor Image Analysis.
.- Enhancement of High Angular Resolution Diffusion-Weighted Imaging through Parallel Imaging and Motion Correction.
.- Domain Adaptation using Generative Adversarial Networks for Medical Image Synthesis.
.- Detecting Electromagnetic Vulnerabilities in Networking Topologies.
.- Measuring the Impact of Flexible Communications Soft-ware Design in Network Applications.
.- Analyzing Clinical Data for Improved Diagnosis Using AI/ML Algorithms in Smart Health Care.
.- Exploring Representations Learned via Self-Supervised Transfer Learning for Medical Image Classification.
.- A Comprehensive Study of Convolutional Neural Net-works for Medical Image Segmentation, etc.