AI in the Social and Business World: A Comprehensive Approach offers an in-depth exploration of the transformative impact of Artificial Intelligence (AI) across a wide range of sectors. This edited collection features 13 chapters, each penned by field experts, providing a comprehensive understanding of AI's theoretical foundations, practical applications, and societal implications.
Each chapter offers strategic insights, case studies, and discussions on ethical considerations and future trends.
Beginning with an overview of AI's historical evolution, the book navigates through its diverse applications in healthcare, social welfare, business intelligence, and more. Chapters systematically explore AI's role in enhancing healthcare delivery, optimizing business operations, and fostering social inclusion through innovative technologies like AI-based sign recognition and IoT in agriculture.
With strategic insights, case studies, and discussions on ethical considerations and future trends, this book is a valuable resource for researchers, practitioners, and anyone interested in understanding AI's multifaceted influence. It is designed to foster informed discussions and strategic decisions in navigating the evolving landscape of AI in today's dynamic world.
This book is an essential resource for researchers, practitioners, and anyone interested in understanding AI’s multifaceted influence across the social and business landscapes.
Readership
Undergraduate/Graduate Students, Professionals.
Table of Contents
PREFACE
LIST OF CONTRIBUTORS
CHAPTER 1 THE STATE OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT IN THE PRESENT-DAY SCENARIO
- Krupali Dhawale, Shraddha Jha, Mishri Gube and Khwaish Asati
INTRODUCTION
THE AI REVOLUTION
- Evolution of Artificial Intelligence
- Beginning of AI (1940s-1950s)
- Enhancement of Artificial Intelligence (1950s-1970s)
- Artificial Intelligence (1970s-1980s)
- Deep Blue (1997)
- Aggressive Growth of AI in the 21st Century
- Impact of Artificial Intelligence
- Automation and Efficiency
- Healthcare
- Natural Language Processing and Communication
- Transportation and Autonomous Driving
- Finance and Fraud Detection
- Cybersecurity
- Data Analytics
- Ethical and Social Implications
INTRODUCTION TO MACHINE LEARNING
- Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machine (SVM)
- Unsupervised Learning
- K-means Clustering
- Hierarchical Sets
- Principal Component Analysis (PCA)
- Collaboration Studies
- Reinforcement Learning
- Q-learning
- Deep Q-networks (DQN)
INTRODUCTION TO DEEP LEARNING
- Applications
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-term Memory (LSTM) Networks
- Generative anti-generation networks (GAN)
INTRODUCTION OF NATURAL LANGUAGE PROCESSING IN AI
- Tokenization
- Stop Word Expulsion
- Stemming and Lemmatization
- Part-of-speech Labeling (POS)
- Named Substance Acknowledgment (NER)
- Text Classification
- Information Extraction
- Machine Interpretation
REINFORCEMENT LEARNING
- Types of Reinforcement Learning Algorithms
- Q-learning
- State-Action-Reward-State-Action (SARSA)
- Deep Q-network (DQN)
PYTHON LANGUAGE USED IN AI DEVELOPMENT
- Scikit-learn
- TensorFlow
- PyTorch
- Keras
- XGBoost
- LightGBM
- CatBoost
- H2O
- Caffe
- Theano
- Microsoft Cognitive Toolkit (CNTK)
CLOUD-BASED AI SERVICES
- Amazon Web Services (AWS) AI Services
- Google Cloud AI Platform
- Microsoft Azure AI
- IBM Watson
CASE STUDIES AND REAL-WORLD APPLICATIONS
- Case Study
- Problem Statement
- Objective
- Role of Machine Learning in Predicting Sonar vs. Mine
- Predicting Underwater Rock vs. Mine Using Sonar Signals
- Applications of AI
- Natural Language Processing (NLP)
- Computer Vision
- Medical Diagnosis
- Self-Driving Cars
- Robotics
- Financial Services
- Virtual Assistants
- Cybersecurity
- AI Used In Smart Practices
- Advanced Surveillance System
- Smart Traffic Management System
- Smart Energy Management System
- Smart Health Management System
- The Societal Impact on the Future of AI
- Employment and Workforce
- Economic Disruption
- Ethical Considerations
- Education and Skills Development
- Healthcare and Well-being
- Privacy and Security
- Social and Cultural Changes
- Predictions and Future Possibilities of AI
- Challenges for Cybersecurity and Fraud Detection
- Advanced Cyber Attacks
- Insider Threats
- Cloud Security
- Internet of Things (IOT) Vulnerabilities
- Data Breach and Privacy Issues
- Machine Learning and AI-Based Attacks
- Lack of Cybersecurity Skills and Workforce
- Regulatory Compliance
- Ethical Challenges for AI
- Bias and Fairness
- Privacy and Data Protection
- Responsibility and Accountability
- Job Displacement and Economic Impact
- Autonomous Systems and Decision-making
- Manipulation and Misuse of AI
- Uninformed Consent and User Empowerment
CONCLUSION
REFERENCES
CHAPTER 2 SOCIAL WELFARE AND ARTIFICIAL INTELLIGENCE'S ROLE: A COMPREHENSIVE SUMMARY OF THE STUDY
- Manjushree Nayak and Jagannath Tiyadi
INTRODUCTION
- Background on Social Welfare Programmes
- Traditional Approaches' Limitations and Obstacles
- The Potential of AI in Social Welfare
- The Importance of Social Welfare Programs
- Improving Efficiency and Effectiveness
- Personalization and Targeting
EMERGENCE OF ARTIFICIAL INTELLIGENCE (AI) AND ITS POTENTIAL IMPACT ON SOCIAL WELFARE
- The Rise of Artificial Intelligence
- Impact on Social Welfare
- Enhancing Service Delivery
- Optimizing Resource Allocation
- Proactive Interventions
- Ethical Considerations and Challenges
BENEFITS OF AI IN SOCIAL WELFARE
- Employment Assistance
- Public Safety and Crime Prevention
- Targeted Intervention and Support
- Social Impact Forecasting
- Citizen Engagement and Feedback
- Remote Monitoring and Telehealth
- Environmental Impact and Sustainability
- Improved Accessibility
- Efficient Resource Allocation
- Improved Fraud Detection
- Personalized Service
- Early Intervention and Risk Assessment
- Optimized Application Process
- Decision Support for Social Workers
- Mental Health Support
- Language Translation and Accessibility
- Disaster Response and Relief
- Adoption of AI in Smart Cities
- Elderly Care and Support
- Social Impact Measurement
- Mathematical Equations and Algorithms for AI in Social Welfare
- Machine Learning Algorithms
- Optimization Algorithms
- Fairness and Bias Mitigation
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- Neural Networks
- Neurons (Nodes)
- Weights
- Activation Function
- Naive Bayes
- K-Nearest Neighbors (KNN)
- Training Phase
- Prediction Phase
- Clustering Algorithms (e.g., K-means, DBSCAN)
- K-Means
- Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Gaussian Mixture Models (GMM)
CHALLENGES AND LIMITATIONS IN THE INTEGRATION OF AI IN SOCIAL WELFARE
- Challenges May Include
- Legal and Ethical Issues
- Data Availability and Quality
- Lack of Domain Expertise
- Interpretability and Explainability
- Human-Centric Approach
- Implementation and Adoption Challenges
- Socio-Economic Impacts
- Limitations May Include
- Lack of Human Interaction
- Data Availability and Quality
- Dynamic and Evolving Challenges
- Inequality and Access Disparities
- Unexpected repercussions
- Ethical Decision-Making
Author
- Parul Dubey
- Mangala Madankar
- Pushkar Dubey
- Kailash Kumar Sahu
