東華大學圖書館 |

Microbial data intelligence and computational techniques for sustainable computing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Microbial data intelligence and computational techniques for sustainable computing/ edited by Aditya Khamparia ... [et al.].
other author:	Khamparia, Aditya.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xvi, 391 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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
Subject:	Artificial intelligence - Biological applications. -
Online resource:	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

Microbial data intelligence and computational techniques for sustainable computing
LDR:04496nmm a2200337 a 4500 001 2368428
003 DE-He213
005 20240229131747.0
006 m d
007 cr nn 008maaau
008 241127s2024 si s 0 eng d
020 $a 9789819996216 $q (electronic bk.)
020 $a 9789819996209 $q (paper)
024 7 $a 10.1007/978-981-99-9621-6 $2 doi
035 $a 978-981-99-9621-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QR69.D35
072 7 $a PSG $2 bicssc
072 7 $a SCI045000 $2 bisacsh
072 7 $a PSG $2 thema
082 0 4 $a 579.028563 $2 23
090 $a QR69.D35 $b M626 2024
245 0 0 $a Microbial data intelligence and computational techniques for sustainable computing $h [electronic resource] / $c edited by Aditya Khamparia ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xvi, 391 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Microorganisms for sustainability, $x 2512-1898 ; $v v. 47
505 0 $a 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.
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.
650 0 $a Artificial intelligence $x Biological applications. $3 590657
650 0 $a Microbiology $x Data processing. $3 662675
650 1 4 $a Microbiology. $3 536250
650 2 4 $a Industrial Microbiology. $3 910373
650 2 4 $a Microbiology Techniques. $3 3597395
650 2 4 $a Bioinformatics. $3 553671
700 1 $a Khamparia, Aditya. $3 3599026
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Microorganisms for sustainability ; $v v. 47. $3 3712352
856 4 0 $u https://doi.org/10.1007/978-981-99-9621-6
950 $a Biomedical and Life Sciences (SpringerNature-11642)