Data Science and Cases in Sustainability: Pattern Recognition and Machine Learning
This book discusses the fascinating world of data science and cases in sustainability focusing on topics related to pattern recognition and machine learning, emphasizing applications that directly address topics related to SDG 9 (Industry, Innovation and Infrastructure). Recognizing the sustainable applications of big data, this text emphasizes the shift from traditional statistical analyses to more sophisticated methods. Each of these techniques—pattern recognition and machine learning—plays a crucial role in extracting hidden knowledge from vast amount of data. Targeted to students, researchers and professionals, it highlights the multidisciplinary and sustainable nature of the field and showcasing real-world applications and equips the readers to navigate the data-driven future.

The first of the two volumes, the book highlights the multidisciplinary nature of data science in the fields of computer science, statistics, physics and economics. It meticulously guides its readers through the data science workflow, covering data collection, preparation, storage, analysis, management and visualization. It highlights specific techniques and algorithms used in each of the above-mentioned stages and offers explanations of major learning mechanisms: dimensionality reduction, classification, clustering and outlier analysis. Additionally, it sheds light on the modern field of deep learning and unfolds the complexity of its mechanism with explanation. Case studies showcase the practical applications and successes of data science across various domains.

1147418569
Data Science and Cases in Sustainability: Pattern Recognition and Machine Learning
This book discusses the fascinating world of data science and cases in sustainability focusing on topics related to pattern recognition and machine learning, emphasizing applications that directly address topics related to SDG 9 (Industry, Innovation and Infrastructure). Recognizing the sustainable applications of big data, this text emphasizes the shift from traditional statistical analyses to more sophisticated methods. Each of these techniques—pattern recognition and machine learning—plays a crucial role in extracting hidden knowledge from vast amount of data. Targeted to students, researchers and professionals, it highlights the multidisciplinary and sustainable nature of the field and showcasing real-world applications and equips the readers to navigate the data-driven future.

The first of the two volumes, the book highlights the multidisciplinary nature of data science in the fields of computer science, statistics, physics and economics. It meticulously guides its readers through the data science workflow, covering data collection, preparation, storage, analysis, management and visualization. It highlights specific techniques and algorithms used in each of the above-mentioned stages and offers explanations of major learning mechanisms: dimensionality reduction, classification, clustering and outlier analysis. Additionally, it sheds light on the modern field of deep learning and unfolds the complexity of its mechanism with explanation. Case studies showcase the practical applications and successes of data science across various domains.

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Data Science and Cases in Sustainability: Pattern Recognition and Machine Learning

Data Science and Cases in Sustainability: Pattern Recognition and Machine Learning

by Ashish Ghosh
Data Science and Cases in Sustainability: Pattern Recognition and Machine Learning

Data Science and Cases in Sustainability: Pattern Recognition and Machine Learning

by Ashish Ghosh

Overview

This book discusses the fascinating world of data science and cases in sustainability focusing on topics related to pattern recognition and machine learning, emphasizing applications that directly address topics related to SDG 9 (Industry, Innovation and Infrastructure). Recognizing the sustainable applications of big data, this text emphasizes the shift from traditional statistical analyses to more sophisticated methods. Each of these techniques—pattern recognition and machine learning—plays a crucial role in extracting hidden knowledge from vast amount of data. Targeted to students, researchers and professionals, it highlights the multidisciplinary and sustainable nature of the field and showcasing real-world applications and equips the readers to navigate the data-driven future.

The first of the two volumes, the book highlights the multidisciplinary nature of data science in the fields of computer science, statistics, physics and economics. It meticulously guides its readers through the data science workflow, covering data collection, preparation, storage, analysis, management and visualization. It highlights specific techniques and algorithms used in each of the above-mentioned stages and offers explanations of major learning mechanisms: dimensionality reduction, classification, clustering and outlier analysis. Additionally, it sheds light on the modern field of deep learning and unfolds the complexity of its mechanism with explanation. Case studies showcase the practical applications and successes of data science across various domains.

Product Details

ISBN-13: 9789819683611
Publisher: Springer Nature Singapore
Publication date: 10/21/2025
Series: Mathematics for Sustainable Developments
Pages: 400
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Ashish Ghosh is the Director of the International Institute of Information Technology Bhubaneswar, India, on deputation from the Indian Statistical Institute (ISI) Kolkata. He began as an electronic engineer, transitioned to computer science for his master’s, and focused on artificial intelligence (AI) for his PhD. After postdoctoral research in Japan, he joined ISI Kolkata as a faculty member and is now a Senior Professor there. Ghosh has made significant contributions to AI, machine learning, image/video analysis, and data science, publishing over 300 research articles, editing 12 books, and achieving an h-index of 53. He ranks among the top 2% of scientists in computer science.

Ashish Ghosh was part of the founding team for the National Center for Soft Computing Research at ISI Kolkata in 2004, funded by the Department of Science and Technology (DST), Government of India. He served as In-Charge of the Center, now recognized as an Associate Institute of ISI Kolkata, and led the Machine Intelligence Unit. Currently, he heads the Data Science Research Consortium Project under a national mission of DST and established the Technology Innovation Hub on Data Science, Big Data Analytics, and Data Curation at ISI Kolkata, funded with INR 100 Crores under the NMICPS mission.

For his contributions to computational intelligence and image analysis, Ghosh received the Young Scientist Award from the Indian Science Congress Association (ISCA, 1992), the Young Scientist Medal from the Indian National Science Academy (INSA, 1995), and the Young Associateship of the Indian Academy of Sciences (IASc, 1997). He is a Fellow of the West Bengal Academy of Science and Technology (WAST, 2015), the National Academy of Sciences (NASI, 2019), the International Association for Pattern Recognition (IAPR, 2020), the Asia-Pacific Artificial Intelligence Association (AAIA, 2022), and the International Artificial Intelligence Industry Alliance (AIIA, 2023). He received the IEEE-GRSS Regional Leader Award in 2019 for his service to IEEE and is a member of various Research Advisory Committees and the Academic Council of Banasthali Vidyapith.

Ghosh is an Associate Editor for several international journals, including the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IET Journal of Computer Vision, and Neural Networks. He has delivered lectures and conducted collaborative research in countries such as China, Germany, Japan, and the USA, and has served as a plenary/keynote speaker at numerous international conferences. As PI and Co-PI, he has completed 18 significant projects funded by the Government of India, European Commission, and Indo-Italy and Indo-US forums for science and technology.

Table of Contents

Chapter 1. Evolution of Data Science.- Chapter 2. LearningDimensionality Reduction.- Chapter 3. Types of Data.- Chapter 4. Pre-processing of Data.- Chapter 5. Dimensionality Reduction.- Chapter 6. Pattern Recognition System.- Chapter 7. Classification.- Chapter 8. Classifiers.- Chapter9. Combination of Classifiers.- Chapter10. Clustering.- Chapter 11. Clustering Algorithms.- Chapter 12. Outliers.- Chapter 13. Fuzzy Set Theoretic Approach to Pattern Recognition.- Chapter 14. Rule of Thumb.- Chapter 15. Artificial Neural Networks.- Chapter 16. Multilayer Perceptron.- Chapter 17. Evolutionary Computing for Machine Learning.- Chapter 18. Support Vector Machine.- Chapter 19. Kernel Machines.- Chapter 20. Extreme Learning Machines.- Chapter 21. Deep Learning.

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