Artificial Intelligence : a Multidisciplinary Approach Towards Teaching and Learning.

Format:

Book

Author/Creator:

Khan, Tahmeena.

Contributor:

Singh, Manisha.

Raza, Saman.

Language:

English

Physical Description:

1 online resource (0 pages)

Edition:

1st ed.

Place of Publication:

Sharjah : Bentham Science Publishers, 2024.

Summary:

Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning explores the evolving role of AI in education, covering applications in fields such as bioinformatics, environmental science, physics, chemistry, economics, and language learning. Written by experts, this book provides a comprehensive overview of AI's integration into diverse subjects, offering insights into the future of AI in education and its potential to enhance academic research and pedagogy.Targeted at faculty, students, and professionals, the book addresses AI's role in blended learning environments and offers practical tools for educators seeking to incorporate AI into their teaching practices. Key Features:- Multidisciplinary exploration of AI in teaching and learning.- Practical tools and methodologies for educators.- Insights into AI-driven innovations in research.- Relevant to a broad audience, from students to professionals. Readership:Undergraduate/Graduate students, academics, and professionals interested in AI applications in education.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword I

Foreword II

Preface

List of Contributors

The Evolution of Artificial Intelligence from Philosophy to New Frontier

Manisha Singh1,*, Arbind K. Jha2, Tahmeena Khan3 and Saman Raza4

INTRODUCTION

THE HISTORY OF ARTIFICIAL INTELLIGENCE (AI)

PHILOSOPHY AND AI: A PHILOSOPHICAL JOURNEY

PHILOSOPHICAL CONSIDERATION OF AI

Metaphysics and AI

Epistemology and AI

Axiology and AI

Framework of AI

HUMAN-MACHINE TEAMING FRAMEWORK

FORMS OF AI

Based on Capabilities

Artificial Narrow Intelligence

Artificial General Intelligence

Artificial Super Intelligence

Generative AI

Based on Functionality Artificial Intelligence

Reactive Machines

Limited AI

Theory of Mind AI

Self-aware AI

Some other forms of AI

AI AND NEW FRONTIERS

AI and Medical Science

AI and Life Science

AI and Mathematics

AI and Architecture

AI and Environmental Science

AI in Education

AI in Research

ChatGPT/Perplexity/GoogleBard

PDFgear

Wordvice AI

Consensus

Trinka

QuillBot AI

Page.AI

Zotero, EndNote Online, Mendeley, RefWorks, etc

AI, HUMAN INTELLIGENCE AND HUMAN WISDOM

CONCLUDING REMARKS

REFERENCES

Artificial Intelligence and Bioinformatics: A Powerful Synergy for Drug Design and Discovery

Chanda Hemantha Manikumar Chakravarthi1, Viswajit Mulpuru1 and Nidhi Mishra2,*

Overview of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Importance of Drug Design

Challenges in Traditional Drug Discovery

DATA ANALYSIS AND PREPROCESSING

Utilizing Biological Databases

Omics Data Integration

Data Cleaning and Feature Extraction

Data Cleaning and Pre-processing

Feature Extraction Techniques.

Handling Imbalanced Datasets

Oversampling and Undersampling

Advanced Algorithms for Imbalanced Data

Addressing Batch Effects

Definition of Batch Effects

Ensuring Consistency

PREDICTIVE MODELLING

Classification Algorithms

Support Vector Machines (SVM)

Random Forests

Neural Networks

Regression Analysis

Quantitative Structure-Activity Relationship (QSAR)

Predicting Molecular Properties

VIRTUAL SCREENING

Target Identification and Validation

Disease Gene Prediction

Expression Profiling and Differential Analysis

Pharmacogenomics

Text Mining and Literature Analysis

Validation through High-Throughput Screening (HTS)

Integration of Structural Biology Data

Ligand-Based Virtual Screening Techniques

Molecular Descriptors and Fingerprints

Machine Learning Classifiers

Pharmacophore Modeling

Chemical Similarity Networks

Ensemble Methods

Structure-Based Virtual Screening

Protein-Ligand Docking

Scoring Functions

Deep Learning in Binding Affinity Prediction

Machine Learning Filters

Consensus Scoring

Machine Learning for Binding Site Prediction

Fragment-Based Virtual Screening

DE NOVO DRUG DESIGN

Generative Models in Drug Design

Generative AI in bioinformatics

Generative AI in Drug Design

Generative AI revolutionizes Drug Discovery Processes

Variational Autoencoders (VAEs)

Generative Adversarial Networks (GANs)

Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks

Transformer-Based Models

Graph Generative Models

Conditional Generative Models

Transfer Learning in Generative Models

Reinforcement Learning for Molecule Generation

Objective Function Definition

Policy Networks

Action Space Representation.

Monte Carlo Tree Search (MCTS)

Actor-Critic Models

Exploration Strategies

Transfer Learning and Pre-training

DRUG REPURPOSING

Identifying New Indications for Existing Drugs

Biological Data Integration

Drug Similarity and Similarity Networks

Disease Similarity and Phenotype Matching

Predictive Modeling for Drug-Disease Associations

Network Propagation Algorithms

Electronic Health Records (EHR) Analysis

Multi-Omics Data Integration

Utilizing Machine Learning for Drug Repositioning

Data Integration and Representation

Feature Extraction and Engineering

Predictive Modelling for Drug-Disease Associations

Network-Based Approaches

Deep Learning Models

Clinical Data Mining

Ensemble Learning

PHARMACOPHORE MODELLING

Molecular Interaction Understanding

Drug Design and Optimization

Virtual Screening

Lead Identification and Optimization

Polypharmacology Analysis

Structure-Activity Relationship (SAR) Analysis

Fragment-Based Drug Design

Target Druggability Assessment

Pharmacokinetic and Toxicity Prediction

Adverse Effects Mitigation

Feature Selection and Descriptor Generation

Training Data Generation

Enhanced Pharmacophore Screening

Predictive Pharmacophore Modeling

Polypharmacology Prediction

Druggability Assessment

Hybrid Approaches

Pharmacophore Optimization

Data-Driven Drug Design

PERSONALIZED MEDICINE

Tailoring Treatments Based on Individual Genetic Profiles

Importance and Benefits

Application of Machine Learning

Examples of Personalized Medicine Applications

Ethical and Regulatory Considerations

Future Directions

Machine Learning in Patient Stratification

Key Components of Patient Stratification

Importance and Benefits.

Applications of Machine Learning

Examples of Patient Stratification

Challenges and Considerations

CHALLENGES AND FUTURE DIRECTIONS

Data Quality and Availability

Data Quality Issues

Data Standardization and Integration

Limited Accessibility

Small Sample Sizes

Biological Variability

Ethical Considerations

Advancements in Personalized Medicine

Patient Privacy and Informed Consent

Data Ownership and Sharing

Bias and Fairness in Models

Regulatory Compliance

Inclusivity in Research

Transparency in AI Decision-Making

Emerging Technologies and Trends in Drug Design

Artificial Intelligence (AI) and Machine Learning

Quantum Computing

Structural Biology Advancements

Immunotherapy and Personalized Medicine

CRISPR and Gene Editing

Nanotechnology in Drug Delivery

Data Integration and Systems Biology

3D Printing in Drug Manufacturing

Blockchain for Data Security

Immunoinformatics

CRISPR-Cas9 and Gene Editing

3D Bioprinting

Nanotechnology

RNA Therapeutics

Virtual Reality (VR) and Augmented Reality (AR)

Blockchain in Drug Development

Metabolomics and Systems Biology

Synthetic Biology

Potential Impact on the Pharmaceutical Industry

Acceleration of Drug Discovery

Revolutionizing Vaccine Development

Precision Medicine and Personalized Therapies

Efficient Drug Testing and Development

Targeted Drug Delivery and Formulation

Innovations in RNA Therapeutics

Optimizing Drug Responses

Immersive Research Environments

Ensuring Data Integrity and Compliance

Comprehensive Understanding of Drug Impact.

Biosynthesis and Customized Biological Systems

Artificial Intelligence Assisted Teaching and Learning and Research of Environmental Sciences

Tahmeena Khan1,*, Priya Mishra2, Kulsum Hashmi2, Saman Raza2, Manisha Singh3, Seema Joshi2 and Abdul Rahman Khan1

Generative AI in Education

AI In Teaching, Learning and Academic Achievement

AI-Based Tools and Methodologies in Environmental/Geoscience Teaching

Different AI Techniques Used in Environment and Geosciences-Based Research

Hazard Identification

Risk Assessment

Risk Evaluation

Decision Making

Earthquakes

Volcano

Landslide

Rainfall

Cyclones

Meteorological Drought

Wildfire

Dust storm

Anthropogenic Air Pollutants

AI in Biosphere

Chat GP and Environmental Science

CHALLENGES IN AI IN ENVIRONMENTAL SCIENCE BASED RESEARCH

Choosing a Suitable Model

Training Optimization

Data Preparation

Ethical Issues

Integrating AI Approaches in Teaching-Learning Associated with the Mitigation of Air Pollution: A Comprehensive Analysis

Rahila Rahman Khan1,*, Ahmad Faiz Minai2 and Rushda Sharf1

OVERVIEW OF THE CURRENT STATE OF AIR POLLUTION AND ITS IMPACT

APPLICATIONS OF AI IN ENVIRONMENTAL CHALLENGES

Environmental Monitoring

Climate Modeling

Biodiversity Conservation

Renewable Energy

POTENTIAL OF AI IN ADDRESSING AIR POLLUTION

Data Analysis and Prediction

Source Identification

Early Warning Systems

Policy Formulation

PROBLEMS WITH CONVENTIONAL AIR QUALITY MONITORING TECHNIQUES

Restricted Coverage

Temporal Limitations

High Installation and Maintenance Costs

Data Timeliness

AI-BASED AIR QUALITY MONITORING

Remote Sensing and Satellite Technology

Integration of Satellite Data.

AI Algorithms for Data Analysis and Interpretation.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789815305180

9815305182

OCLC:

1477225409