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Microbial data intelligence and computational techniques for sustainable computing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Microbial data intelligence and computational techniques for sustainable computing/ edited by Aditya Khamparia ... [et al.].
其他作者:	Khamparia, Aditya.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xvi, 391 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Biological applications. -
電子資源:	https://doi.org/10.1007/978-981-99-9621-6
ISBN:	9789819996216

Microbial data intelligence and computational techniques for sustainable computing
Microbial data intelligence and computational techniques for sustainable computing[electronic resource] /edited by Aditya Khamparia ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xvi, 391 p. :ill. (some col.), digital ;24 cm. - Microorganisms for sustainability,v. 472512-1898 ;. - Microorganisms for sustainability ;v. 47..

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.

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.

ISBN: 9789819996216

Standard No.: 10.1007/978-981-99-9621-6doiSubjects--Topical Terms:

590657
Artificial intelligence
--Biological applications.

LC Class. No.: QR69.D35

Dewey Class. No.: 579.028563

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520 $a 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.
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