This book offers information on intelligent and computational techniques for microbial data associated with plant microbes, human microbes etc. The main focus of this book is to provide an insight on building smart sustainable solutions for microbial technology using intelligent computational techniques.

Microbes are ubiquitous in nature, and their interactions among each other are important for colonizing diverse habitats. 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. Chapters in this book explore the conventional methods as well as the most recently recognized high-throughput technologies which are important for productive agroecosystems to feed the growing global population. This book is of interest to teachers, researchers, microbiologist, computer bioinformatics scientists,plant and environmental scientist, and those interested in environment stewardship around the world. The book also serves as an advanced textbook material for undergraduate and graduate students of computer science, biomedicine, agriculture, human science, forestry, ecology, soil science, and environmental sciences and policy makers.

Discusses computer-assisted tools for visualization and representation of complex microbial data Provides information on applying machine and deep learning, and sensor-based techniques for microbial analysis Provides insight on building smart sustainable solutions for microbial technology using computational methods

Autorentext
Dr. Aditya Khamparia has expertise in teaching, entrepreneurship, and research and development of a decade. He is currently working as an assistant professor and coordinator of Department of Computer Science, Babasaheb Bhimrao Ambedkar University, India. He received his Ph.D. degree from Lovely Professional University, Punjab in May 2018. He has completed his M. Tech. from VIT University and B. Tech. from RGPV, Bhopal. He has completed his PDF from UNIFOR, Brazil. He has more than 100 research papers along with book chapters including more than 20 papers in top journals with cumulative impact factor of above 100 to his credit. Additionally, he has authored and edited a cumulative of 11 books. His research interest includes machine learning, deep learning, educational technologies and computer vision.

Dr. Deepak Gupta received a B.Tech. degree in 2006 from the Guru Gobind Singh Indraprastha University, India. He received M.E. degree in 2010 from Delhi Technological University, India and Ph. D. degree in 2017 from Dr. APJ Abdul Kalam Technical University, India. He has completed his Post-Doc from Inatel, Brazil. With 13 years of rich expertise in teaching and two years in the industry, he focuses on rational and practical learning. He has contributed massive literature to the fields of intelligent data analysis, biomedical engineering, artificial intelligence, and soft computing. He has served as editor-in-chief, guest editor, associate editor in various reputed journals. He has actively been organizing various reputed international conferences. He has authored/edited 43 books. He has published 200 scientific research publications including more than 100 SCI Indexed Journals.

Dr. Babita Pandey working as an associate professor in Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India. Her research interests include biomedical engineering, e-learning, computational intelligence andsecurity systems. She has published more than 100 publications and conferences including more than 40 SCI Indexed Journals.

Dr. Devendra Kumar Pandey is currently working as a professor at Lovely Professional University, India. He obtained his Ph.D. in biochemical engineering from Indian Institute of Technology, India. His main area of interest related to pharmacology and toxicology, plant and soil sciences, and molecular sciences. His area of expertise includes plant biotechnology, plant-microbe interaction, chromatography techniques i.e., HPTLC, HPLC, LC-MS, molecular markers and bioactive compounds markers for medicinal plants, and bioactive compounds. He has published more than 100 articles in international journals with papers also in national and international conferences contributed as author/co-author.

Inhalt

1. The Contribution of Artificial Intelligence to Drug Discovery: Current Progress and Prospects for the Future.- 2. Prediction of Plant disease using Artificial Intelligence.- 3. Computer Vision Based Remote Care of Microbiological Data Analysis.- 4. A Comparative Study of Various Machine Learning (ML) Approaches for Fake News Detection in Web based Applications.- 5. Analytics and Decision-Making Model Using ML For IoT Based Greenhouse Precision Management in Agriculture.- 6. Distil-BERT based Text Classification for Automated Diagnosis of Mental Health Conditions.- 7. An optimized hybrid ARIMA-LSTM model for time series forecasting of Agriculture production in INDIA.- 8. An Exploratory Analysis of Machine Intelligence Enabled Plant Diseases Assessment.- 9. Synergizing Smart Farming and Human Bioinformatics through IoT and Sensor Devices.- 10. Deep learning assisted techniques for detection & prediction of colorectal cancer from medical images and microbial modality.- 11. IoT Enabled Smart farming and human bioinformatics.- 12. Smart farming and human bioinformatics system based on Context aware computing systems.- 13. Plant Diseases Diagnosis with Artificial Intelligence (AI).- 14. Analyzing the Frontier of AI-Based Plant Disease Detection: Insights and Perspectives.- 15. Fuzzy and Data Mining Methods for Enhancing Plant Productivity and Sustainability.- 16. Plant Disease Diagnosis with Artificial Intelligence (AI).- 17. Sustainable AI driven Applications for Plant Care and Treatment.- 18. Use Cases and Future Aspects of Intelligent Techniques in Microbe Data Analysis.- 19. Early Crop Disease Identification Using Multi-Fork Tree Networks and Microbial Data Intelligence.- 20. Guarding Maize: Vigilance Against Pathogens Early Identification, Detection and Prevention.- 21. Comprehensive Analysis of Deep Learning Models for Plant Disease Prediction.- 22. Enhancing Single-Cell Trajectory Inference and Microbial Data Intelligence.- 23. AI assisted methods for protein structure prediction and analysis.