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Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Rajiv Misra

    1. Indian Institute of Technology Patna, Patna, India

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  2. Rudrapatna K. Shyamasundar

    1. Indian Institute of Technology Bombay, Mumbai, India

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  3. Amrita Chaturvedi

    1. Indian Institute of Technology (BHU), Varanasi, India

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  4. Rana Omer

    1. Cardiff University, Cardiff, UK

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  • Presents recent research on machine learning and big data analytics
  • Includes the Proceedings of ICMLBDA 2021, held on March 29–30, 2021, in Patna, India
  • Written by experts in the field

Part of the book series: Lecture Notes in Networks and Systems (LNNS, volume 256)

Included in the following conference series:

Conference proceedings info: ICMLBDA 2021.

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Table of contents (30 papers)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Analysis on Applying the Capabilities of Deep Learning Based Method for Underwater Fish Species Classification

    • Suja Cherukullapurath Mana, T. Sasipraba
    Pages 1-11

  3. Bug Assignment Through Advanced Linguistic Operations

    • Shubham Kumar, Saurabh Kumar, Rahul Agrawal, Nitesh Goyal, Shubham Kumar, Vivek Kumar
    Pages 12-21

  4. An Empirical Framework for Bangla Word Sense Disambiguation Using Statistical Approach

    • Monisha Biswas, Omar Sharif, Mohammed Moshiul Hoque
    Pages 22-33

  5. Engagement Analysis of Students in Online Learning Environments

    • Sharanya Kamath, Palak Singhal, Govind Jeevan, B. Annappa
    Pages 34-47

  6. Static and Dynamic Hand Gesture Recognition for Indian Sign Language

    • A. Susitha, N. Geetha, R. Suhirtha, A. Swetha
    Pages 48-66

  7. An Application of Transfer Learning: Fine-Tuning BERT for Spam Email Classification

    • Amol P. Bhopale, Ashish Tiwari
    Pages 67-77

  8. Concurrent Vowel Identification Using the Deep Neural Network

    • Vandana Prasad, Anantha Krishna Chintanpalli
    Pages 78-84

  9. Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2

    • Ankitha Giridhar, Niranjana Sampathila
    Pages 85-94

  10. Explainable AI for Healthcare: A Study for Interpreting Diabetes Prediction

    • Neel Gandhi, Shakti Mishra
    Pages 95-105

  11. QR Based Paperless Out-Patient Health and Consultation Records Sharing System

    • S. Thiruchadai Pandeeswari, S. Padmavathi, S. S. Srilakshmi
    Pages 106-116

  12. Searching Pattern in DNA Sequence Using ECC-Diffie-Hellman Exchange Based Hash Function: An Efficient Approach

    • M. Ravikumar, M. C. Prashanth, B. J. Shivaprasad
    Pages 117-127

  13. Integrated Micro-Video Recommender Based on Hadoop and Web-Scrapper

    • Jyoti Raj, Amirul Hoque, Ashim Saha
    Pages 128-140

  14. Comparison of Machine Learning Techniques to Predict Academic Performance of Students

    • Bhavesh Patel
    Pages 141-149

  15. Misinformation–A Challenge to Medical Sciences: A Systematic Review

    • Arpita Sharma, Yasha Hasija
    Pages 150-159

  16. Comparative Analysis Grey Wolf Optimization Technique & Its Diverse Applications in E-Commerce Market Prediction

    • Shital S. Borse, Vijayalaxmi Kadroli
    Pages 160-174

  17. Applying Extreme Gradient Boosting for Surface EMG Based Sign Language Recognition

    • Shashank Kumar Singh, Amrita Chaturvedi, Alok Prakash
    Pages 175-185

  18. Automated Sleep Staging System Based on Ensemble Learning Model Using Single-Channel EEG Signal

    • Santosh Kumar Satapathy, Hari Kishan Kondaveeti, Ravisankar Malladi
    Pages 186-202

  19. Histopathological Image Classification Using Ensemble Transfer Learning

    • Binet Rose Devassy, Jobin K. Antony
    Pages 203-212

  20. A Deep Feature Concatenation Approach for Lung Nodule Classification

    • Amrita Naik, Damodar Reddy Edla, Ramesh Dharavath
    Pages 213-226
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Other volumes

  1. Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)

Keywords

About this book

This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.

Editors and Affiliations

  • Indian Institute of Technology Patna, Patna, India

    Rajiv Misra

  • Indian Institute of Technology Bombay, Mumbai, India

    Rudrapatna K. Shyamasundar

  • Indian Institute of Technology (BHU), Varanasi, India

    Amrita Chaturvedi

  • Cardiff University, Cardiff, UK

    Rana Omer

Bibliographic Information

  • Book Title: Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)

  • Editors: Rajiv Misra, Rudrapatna K. Shyamasundar, Amrita Chaturvedi, Rana Omer

  • Series Title: Lecture Notes in Networks and Systems

  • DOI: https://doi.org/10.1007/978-3-030-82469-3

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Softcover ISBN: 978-3-030-82468-6Published: 30 September 2021

  • eBook ISBN: 978-3-030-82469-3Published: 29 September 2021

  • Series ISSN: 2367-3370

  • Series E-ISSN: 2367-3389

  • Edition Number: 1

  • Number of Pages: XI, 362

  • Number of Illustrations: 79 b/w illustrations, 147 illustrations in colour

  • Topics: Data Engineering, Computational Intelligence, Machine Learning, Big Data

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