Marvels of Artificial and Computational Intelligence in Life Sciences / edited by Thirunavukkarasu Sivaraman, V. Subramanian Thangarasu, and Ganesan Balakrishnan.

Format:

Book

Author/Creator:

Sivaraman, Thirunavukkarasu, Author.

Contributor:

Sivaraman, Thirunavukkarasu, editor.

Thangarasu, V. Subramanian, editor.

Balakrishnan, Ganesan, editor.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Artificial intelligence--Social aspects.

Computational intelligence.

Physical Description:

1 online resource (287 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Books, [2023]

Summary:

Marvels of Artificial and Computational Intelligence in Life Sciences is a primer for scholars and students who are interested in the applications of artificial intelligence (AI and computational intelligence (CI) in life sciences and other industries. The book consists of 16 chapters (9 of which focus on AI and 7 which showcase the benefits of CI approaches to solve specific problems). Chapters are edited by subject experts who describe the roles and applications of AI and CI in different parts of our lives in a concise and lucid manner. The book covers the following key themes: AI Revolution in Healthcare and Drug Discovery:AI's Impact on Biology and Energy ManagementAI and CI in Physical Sciences and Predictive ModelingComputational Biology The editors have compiled a good blend of topics in applied science and engineering to give readers a clear understanding of the multidisciplinary nature of the two facets of computing. Each chapter includes references for advanced readers. Audience Researchers and industry professionals in the field of electronics and nanotechnology; students taking advanced courses in electronics and technology.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword I

Foreword II

Preface

List of Contributors

Artificial Intelligence for Infectious Disease Surveillance

Sathish Sankar1,*, Pitchaipillai Sankar Ganesh1 and Rajalakshmanan Eswaramoorthy2

INTRODUCTION

CONCLUSION

ACKNOWLEDGEMENT

REFERENCES

Recent Innovations in Artificial Intelligence (AI) Algorithms in Electrical and Electronic Engineering for Future Transformations

S. P. Sureshraj1,*, Nalini Duraisamy1, Rathi Devi Palaniappan1, S. Sureshkumar2, M. Priya1, John Britto Pitchai1, Mohamed Badcha Yakoob1, S. Karthikeyan1, G. Sundarajan1 and S. Muthuveerappan1

PROS AND CONS OF AI IN ENGINEERING

ROLE OF AI IN POWER SYSTEMS

ROLE OF AI IN POWER ELECTRONICS

ROLE OF AI IN COMPUTERIZED OPTIMIZATION PROCESS OF POWER ELECTRONIC COMPONENTS

ROLE OF AI IN RENEWABLE ENERGY SYSTEMS

MACHINE LEARNING ALGORITHM IN HYBRID ENERGY RESOURCES

CREATING DATASET FOR GPR MODEL

DATA PREPROCESSING AND TRAINING

CONCLUDING REMARKS

An Introduction to Diabetes Drug Discovery in Biomedical Industry through Artificial Intelligence, Using Lichens' Secondary Metabolites

N. Rajaprabu1,* and P. Ponmurugan2

MATERIALS AND METHODS

Thallus Collection and Metabolite Identification

Collection of Lichen and Lichenicolous Fungal Metabolites

Bioinformatics Approach for Lichens Metabolites Analysis Against Type II Diabetic Receptor

STATISTICAL ANALYSIS

RESULTS

Comparative In Silico Interaction Between Lichen Thallus and Endo Lichenic Fungal Metabolites

Docking Interaction Profile Between the Lichenized Fungal Compounds and Diabetic Type II Membrane Receptor

DISCUSSION

Comparative Analysis of the Thallus and its Fungal Extract.

Molecular Docking Validations Through the In-silico Methods

ACKNOWLEDGEMENTS

Structural Bioinformatics and Artificial Intelligence Approaches in De Novo Drug Design

Dakshinamurthy Sivakumar1 and Sangwook Wu2,*

Molecular Docking - Classical vs. AI Methods

Scoring Functions

Knowledge-based Scoring Function

Force Field/Physics-based Scoring Functions

Empirical Scoring Functions

Machine Learning-based Scoring Functions

Success Stories

Artificial Intelligence (AI) Game Changer in Cancer Biology

Ashok Kamalanathan1, Babu Muthu1 and Patheri Kuniyil Kaleena2,*

AI'S APPLICATIONS IN VARIOUS TYPES OF CANCER

Liver Cancer

Breast Cancer

Brain Tumor

Skin Cancer

Lung Cancer

Prostate Cancer

Colon Cancer

Kidney Cancer

Bladder Cancer

Thyroid Cancer

AI CT RESPONSE

AI FOR CANCER RESEARCH

CUTTING-EDGE CANCER PROJECTS

APPLICATION OF AI IN DRUG DISCOVERY

CANCER TREATMENT TECHNIQUES USED IN THE PAST

AI-Based Energy Management for Domestic Appliances

Murugananth Gopal Raj1,*, S. John Alexis2, A. Manickavasagam1 and R. Reji3

Electricity Generation and Consumption

Artificial Intelligence

HOME ENERGY MANAGEMENT SYSTEM

DOMESTIC ELECTRICAL LOADS

ENERGY WASTAGE ISSUES IN DOMESTIC APPLIANCES

AI-BASED ENERGY MANAGEMENT SYSTEMS

AI for Room Comfort System

Refrigerator System

Other Electrical Loads

Miscellaneous Loads

AI-Based Domestic Load Scheduling and Power Management for Renewable Energy Exporters

C. Pradip1,*, Murugananth Gopal Raj2, S. John Alexis3 and A. Manickavasagam2

TYPES OF ROOFTOP SOLAR PV SYSTEM.

Grid-tied System

Grid-tied System with a Backup

Off-grid System

A NECESSITY FOR RESIDENTIAL PV SYSTEM

MODEL OF RESIDENTIAL PV SYSTEM

Optimal Scheduling Process

AI BASED OPTIMAL LOAD SCHEDULING AND POWER MANAGEMENT

AI Based Load Scheduling and Power Management

Implementation of AI Based System

Artificial Intelligence in Physical Science

Artificial Intelligence Based Global Solar Radiation Prediction

Meenal Rajasekaran1,* and Rajasekaran Ekambaram2

ARTIFICIAL NEURAL NETWORK METHODOLOGY

RESULTS AND DISCUSSION

In silico Approaches to Tyrosine Kinase Inhibitors' Development

S. Sugunakala1,* and S. Selvaraj2

Protein Tyrosine Kinases (Ptks)

CLASSIFICATION OF PROTEIN TYROSINE KINASE FAMILY

Architecture and Regulation of Receptor Protein Tyrosine Kinases (RPTKs)

Architecture and Regulation of Non-Receptor Protein Tyrosine Kinases (NRPTKs)

PROTEIN TYROSINE KINASES AS DRUG TARGETS

DEVELOPMENT OF NEXT GENERATION PROTEIN TYROSINE KINASE INHIBITORS (PTKI)

USE OF 2D QSAR, 3D QSAR AND PHARMACOPHORE MODELS FOR THE DEVELOPMENT OF TYROSINE KINASE INHIBITORS (TKI)

TYROSINE KINASE INHIBITORS DISCOVERED THROUGH SBVS

TYROSINE KINASE INHIBITORS DISCOVERED THROUGH LBVS

DISCOVERING TYROSINE KINASE INHIBITORS BY COMBINED EXPERIMENTAL AND CADD APPROACHES

PREDICTION OF KINASE-LIGAND INTERACTIONS BY COMPUTATIONAL INTELLIGENCE

Use of Machine Learning and Artificial Intelligence Approaches

Computer-Aided Drug Discovery Studies in Ethiopian Plant Species

Artificial Intelligence-genomic Studies in The Advancement of Agriculture

R. Ushasri1,*, Summera Rafiq1, SK. Jasmine Shahina1 and P. Priyadarshini R. Lakshmi1.

INTRODUCTION

ARTIFICIAL INTELLIGENCE IS THE CENTRAL DOGMA OF MOLECULAR BIOLOGY

ARTIFICIAL NEURAL NETWORKS IN AGRICULTURE

DEEP NEURAL NETWORKS IN AGRICULTURE

APPLICATION OF MACHINE LEARNING IN CROP GENOMICS RESEARCH

ACKNOWLEDGMENT

Computational EPR and Optical Spectral Investigation of VO(II) Ion Doped in Aqualithiumaquabis (Malonato) Zincate Lattice

S. Boobalan1,*, G. Sivasankari2 and M. Mahaveer Sree Jayan3

EXPERIMENTAL

Material and Method

Preparation of Single Crystal of VO(II)-doped [Li(H2O)]2[Zn(mal)2(H2O)]

EPR Measurements

UV-Visible, FT-IR, Powder XRD Measurements

CRYSTAL STRUCTURE

Single Crystal EPR Studies

Polycrystalline EPR Studies

Admixture Coefficients

Optical Absorption Studies

FT-IR spectral studies

Powder XRD Studies

Morphological and Structural Characterizations of Strontium in Strontium Sulphate as a Perceptive Factor in the Computational Method for the Forensic Analysis of Tool Paint by Non-destructive Analytical Studies

B. Sithi Asma1, A. Palanimurugan1, A. Cyril1 and S. Thangadurai2,*

METHODS AND MATERIALS

XRD Analysis

UV-VIS Microspectroscopy

FT-IR Microspectroscopy

RAMAN Microspectroscopy

Morphological Analysis

RESULT AND DISCUSSION

FTIR Analysis

Raman Spectra

UV Analysis

SEM Analysis

Functional Prediction of Anti-methanogenic Targets from Methanobrevibacter Ruminantium M1 Operome

M. Bharathi1, S. Saranya1, Senthil Kumar N.2 and P. Chellapandi1,*

Sequence-based Functional Annotation

Structure-based Functional Annotation

Knowledge-based Functional Annotation.

Prediction of Subcellular Localization

Prediction of Virulence Properties

Prediction of Vaccinogenic Properties

Comparative Prediction of Electrical Interplay Systems in Methanothermobacter thermautotro-phicus ΔH and Metal-loving Bacteria

R. Prathiviraj1, Sheela Berchmans2 and P. Chellapandi1,*

Identification and Functional Characterization of Pilin and Archaellin

Network Construction

Functional Annotation for Microbe-microbe Interactions

Structural and Functional Characterization of Pilin and Archaellin

Mechanism of Electrical Interplay Systems in MTH

Comparison Between MTH and Meta-loving Bacteria

Subject Index

Back Cover.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes index.

Includes bibliographical references and index.

ISBN:

981-5136-80-1

OCLC:

1402819126