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Data Mining and Information Security : Proceedings of ICDMIS 2024, Volume 5 / edited by Soumi Dutta, Abhishek Bhattacharya, Vung Pham, Zdzislaw Polkowski.

Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2025 Available online

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Format:
Book
Contributor:
Dutta, Soumi., Editor.
Bhattacharya, Abhishek, Editor.
Pham, Vung., Editor.
Polkowski, Zdzislaw, Editor.
Series:
Lecture Notes in Networks and Systems, 2367-3389 ; 1389
Language:
English
Subjects (All):
Computational intelligence.
Artificial intelligence.
Data mining.
Data protection.
Computational Intelligence.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Data and Information Security.
Local Subjects:
Computational Intelligence.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Data and Information Security.
Physical Description:
1 online resource (XVIII, 612 p. 325 illus., 279 illus. in color.)
Edition:
1st ed. 2025.
Place of Publication:
Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Summary:
This book features research papers presented at the International Conference on Data Mining and Information Security (ICDMIS 2024) held at Eminent College of Management and Technology (ECMT), West Bengal, India, during October 7–8, 2024. The book is organized in five volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT) and information security.
Contents:
Forecasting Future Stock Market Values Using Long-Term Memory Models and Sentiment Analysis
Early Detection and Classification of Blight Diseases in Tomato and Potato Leaf Using Deep Convolution Neural Networks
A Smartphone based Robust Model for Intelligent Prediction of Thyroid disease
Analysis of leveraging machine learning and data analytics to predict Cardiac Disease
Comparative Analysis of Machine Learning Algorithms for Early Sepsis Detection.
Notes:
Description based on publisher supplied metadata and other sources.
ISBN:
981-9660-66-1
OCLC:
1535341540

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