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Proceedings of International Conference on Big Data, Machine Learning and Applications BigDML 2019
- Available as
- Online
- Summary
-
This book covers selected high-quality research papers presented at the International Conference on Big Data, Machine Learning, and Applications (BigDML 2019). It focuses on both theory and applica...
This book covers selected high-quality research papers presented at the International Conference on Big Data, Machine Learning, and Applications (BigDML 2019). It focuses on both theory and applications in the broad areas of big data and machine learning. It brings together the academia, researchers, developers and practitioners from scientific organizations and industry to share and disseminate recent research findings. .
Details
- Creator
- edited by Ripon Patgiri, Sivaji Bandyopadhyay, Valentina Emilia Balas
- Format
- Books
- Language
- English
- Contributors
- Publication
-
- 1st ed. 2021
- Singapore : Springer Nature Singapore : Imprint: Springer, 2021
- Physical Details
-
- XII, 268 p. 93 illus., 60 illus. in color. online resource
- text file PDF
- ISBNs
- 9813347880, 9813347872, 9789813347878, 9789813347892, 9789813347885
- OCLC
- on1243262481
- BicGenesis: A method to Identify ESCC Biomarkers using Biclustering Approach -- An Improved Energy Aware Secure Clustering Technique for Wireless Sensor Network -- Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence -- U-control Chart-Based Differential Evolution Clustering for Determining the Number of Clusters in K-Means -- Privacy Preserving Association Rule Mining in Distributed Database Environment: a review -- The Goals Programming as a Tool for Measuring Sustainability of Agricultural Production Chains of Rice -- Comparative Study of Faster Region-based Convolutional Neural Networks with Inception v2 and Single Shot Detector with Inception V2 on Their Signature Detection Capabilities -- Sleep Stage Classification Based on Ensemble Decision Tree Technique Using Single-Channel EEG