Advanced Computational Techniques for Sustainable Computing

1. Auflage 2024
ISBN: 978-0-367-49528-2
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 338 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 590 g

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.

The book offers a comprehensive coverage of some of the most essential topics:

- It provides an insight on building smart sustainable solutions.

- Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing.

- Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research.

- Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains.

- Encompasses comparison and analysis of recent technologies and trends.

- Includes development of smart models for information gain and effective decision making with visualization.

The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

Zielgruppe

Academic, Postgraduate, Professional, and Undergraduate Advanced

Autoren/Hrsg.

Rathi, Megha

Sinha, Adwitiya

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Geowissenschaften Umweltwissenschaften Umweltschutz, Umwelterhaltung
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Geowissenschaften Umweltwissenschaften Umweltwissenschaften
• Mathematik | Informatik Mathematik Mathematik Allgemein
• Naturwissenschaften Biowissenschaften Biowissenschaften Biowissenschaften, Biologie: Sachbuch, Naturführer
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

Preface. Editors. Contributors. 1 Sustainable Computing—An Overview. 2 Ambient Air Quality Analysis and Prediction Using Air Quality Index and Machine Learning Models—The Case Study of Delhi. 3 Assessing Land Cover and Drought Prediction for Sustainable Agriculture. 4 Electronic Health Record for Sustainable eHealth. 5 Team Member Selection in Global Software Development—A Blockchain-Oriented Approach. 6 Machine Learning in Sustainable Healthcare. 7 Multimedia Audio Signal Analysis for Sustainable Education. 8 Smart Health Analytics for Sustainable Energy Monitoring Using IoT Data Analytics. 9 Customer Analytics for Purchasing Behavior Prediction. 10 Discernment of Malaria-Infected Cells in the Blood Streak Images Using Advanced Learning Techniques. 11 Handwritten Text Recognition with IoT Devices. 12 Circadian Rhythm and Lifestyle Diseases. 13 Deep Learning for Automated Disease Detection. 14 Time Series Analysis and Trend Exploration of Stock Market. 15 Medical Search Engine. 16 Assessing Impact of Global Terrorism Using Time Series Analysis. 17 Sustainable Statistics for Death Cognizance Analysis. 18 Modeling the Immune Response of B-Cell Receptor Using Petri Net for Tuberculosis. 19 Crop Prediction and the Sustainability of Farming. 20 Personalized Heart Disease Framework for Health Sustainability. 21 Sports Analytics for Classifying Player Actions in Basketball Games. Index.

Über Autor(innen)

Dr. Megha Rathi has obtained Ph.D. in Computer Science from Banasthali University and is presently working as an Assistant Professor (Senior Grade) in the Department of Computer Science, Jaypee Institute of Information Technology (JIIT), Noida, Sector 62, Uttar Pradesh, India. She has ten years of teaching experience and worked on a research project at the National Informatics Centre (NIC), Delhi. She has experience in software development and worked as a Project Associate at the Indian Institute of Technology (IIT), Delhi. She has organized several special sessions at international conferences and also delivered invited talk. Her research areas include sustainable computing, data mining, data science, health analytics, and machine learning.

Dr. Adwitiya Sinha has completed Ph.D. from the School of Computer & Systems Science (SCSS), Jawaharlal Nehru University (JNU) New Delhi, India. She was also awarded Senior Research Fellowship (SRF) from the Council of Scientific & Industrial Research (CSIR), New Delhi. She is presently working as Assistant Professor (Senior Grade) in the Department of Computer Science, Jaypee Institute of Information Technology (JIIT), Noida, Sector 62, Uttar Pradesh, India. She has been organizing & conducting special sessions in several international conferences as chairperson and session chair for invited sessions. She has also delivered lectures series in The Consortium for Educational Communication (CEC), University Grants Commission (UGC) on sensor networks and social media sciences. She has been promoted to IEEE Senior Member in 2019 by The Institute of Electrical and Electronics Engineers (IEEE), New York, USA. Her major research interest lies in Sustainable Computing, Social Networking, Large-scale Graph Algorithms and Wireless Sensor Networks.