Advances in Machine Learning and Data Science

Recent Achievements and Research Directives

  • Damodar Reddy Edla
  • Pawan Lingras
  • Venkatanareshbabu K.
Conference proceedings

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Dharavath Ramesh, Neeraj Patidar, Teja Vunnam, Gaurav Kumar
    Pages 9-20
  3. Dharavath Ramesh, Himangshu Biswas, Vijay Kumar Vallamdas
    Pages 21-32
  4. Satyanarayana Nimmala, Y. Ramadevi, Srinivas Naik Nenavath, Ramalingaswamy Cheruku
    Pages 53-60
  5. Choudhary Shyam Prakash, Sushila Maheshkar, Vikas Maheshkar
    Pages 81-93
  6. G. Kiran Kumar, Ilaiah Kavati, Koppula Srinivas Rao, Ramalingaswamy Cheruku
    Pages 95-102
  7. Ramalingaswamy Cheruku, Diwakar Tripathi, Y. Narasimha Reddy, Sathya Prakash Racharla
    Pages 103-112
  8. R. C. Anju, Sree Harsha Ramesh, P. C. Rafeeque
    Pages 113-120
  9. S. Manigandan, K. Vijayaraja, G. Durga Revanth, A. V. S. C. Anudeep
    Pages 121-128
  10. Shraddha Naik, Ravi Prasad K. Jagannath
    Pages 129-138
  11. Monica Ravishankar, D. Vijay Rao, C. R. S. Kumar
    Pages 149-152
  12. Sumaiya Pathan, Preetham Kumar, Radhika M. Pai
    Pages 163-173

About these proceedings

Introduction

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions.

These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc.

The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms. 

Keywords

Data Science Conference Proceedings Machine Learning LAMDA 2017 Big Data Open Platforms Cloud Computing Techniques Feature Learning

Editors and affiliations

  • Damodar Reddy Edla
    • 1
  • Pawan Lingras
    • 2
  • Venkatanareshbabu K.
    • 3
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology GoaGoaIndia
  2. 2.Department of Mathematics and Computing ScienceSaint Mary’s UniversityHalifaxCanada
  3. 3.Department of Computer Science and EngineeringNational Institute of Technology GoaGoaIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-8569-7
  • Copyright Information Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-10-8568-0
  • Online ISBN 978-981-10-8569-7
  • Series Print ISSN 2194-5357
  • Series Online ISSN 2194-5365
  • About this book