Data analytics and generative AI (Gen AI) are transformative technologies that play a critical role in modern decision-making and innovation. Data analytics enables organizations to extract actionable insights from vast amounts of structured and unstructured data, driving efficiency, improving customer experiences, and identifying trends. Generative AI, on the other hand, enhances creativity and problem-solving by producing new content, such as text, images, and designs, based on learned patterns. Together, they empower people and organizations to make data-driven decisions, automate complex processes, and unlock new opportunities for growth and innovation.
Generative AI 2.0 and Data Analytics explores the intersection between Gen AI and data analytics and addresses its profound efects on industries and organizations across the globe. Highlights of the book include:
- Deep learning architectures for generative models in business data management
- Optimizing human-AI collaboration for strategic decision-making in business practises
- Benchmarking practices and evaluation metrics for Generative AI in business data analytics
Not only covering the fundamental concepts, and techniques of generative AI and their practical application, the book also investigates how these techniques foster innovation and improve quality of data in various business domains. It examines a broad range of topics from artificial data generation, security analytics, anomaly detection, reinforcement management, ethical consideration, challenges and future scenarios. The book also features expert opinions and case studies to provide practical direction and valuable insight.
The book examines how generative AI can transform and revolutionize data analytics and management. It not only covers fundamental concepts, generative AI techniques, and their practical application but also investigates how these techniques can bring innovate data analytics in various domains.
1. Exploring Inventive Potential of Generative AI and the Next
Generation: Theory and Techniques
2. AI In Education
3. Integrating
Artificial Intelligence in K-12 Education: A Systematic Review of Strategies,
Outcomes, and Applications (20212024)
4. Precise and Computation Efficient
Face Recognition Based Real Time Attendance System
5. The Role of Chatbots in
Student Interaction: EFL Speaking and Cognitive Load Theory Management
6.
Where You Live Matters: Decoding the Geographic Factors Influencing Data
Scientist Salaries Through Machine Learning
7. Perception of Fairness: The
Role of Explainable and Trustworthy Artificial Intelligence
8. Prosthetic
Hand with Expended Gestures Using Sequential Artificial Intelligence Models
9. Generative Adversarial Networks (GANs) for Brain Tumor Imaging
Applications: A Systematic Review
10. Machine Learning and Deep Learning for
Colon Cancer Classification with Gene Expression and Histological Image
Datasets
11. Transfer Learning-Machine Learning Hybrid Approach for Binary
Classification of Breast Cancer Using Bilateral Filtering
12. Analyzing the
Agricultural as well as Environmental Data to Address Predicting the Crop
Yields for Achieving Zero Hunger (UN SDG 2: Zero Hunger)
13. Smart Homes and
Beyond: A Review of IoT Applications Transforming Daily Life
14. AI-Powered
CrossFit Coach: Integrating Local Small Language Model and Geospatial
Technology for Enhanced Fitness Training
15. Deep Learning Architectures for
Generative Models in Business Data Management
16. Optimizing Human-AI
Collaboration for Strategic Decision-Making in Business Practices
17.
Benchmarking Practices and Evaluation Metrics for Generative AI in Business
Data Analytics
A researcher and academician, Dr. Adarsh Garg has 24 years of teaching, research, consultancy, and administrative experience. She received her PhD degree in information technology from GGSIP University, Delhi. She is currently working as Professor of Data Analytics and IT at GL Bajaj Institute of Management and Research, Gautam Buddh Nagar, Greater Noida, and as a Visiting Professor at Delhi Technical University, Delhi. Prior to joining GLBIMR, she worked with organizations like Galgotias University, WIPRO Tech, GE, IMT Ghaziabad, and Punjabi University, Patiala. She is currently supervising eight PhDs. She has published more than 70 research papers and edited five books.
Dr. Fadi Al-Turjman received his Ph.D. in computer science from Queens University, Canada, in 2011. He is a professor and the associate dean for research and the founding director of the International Research Center for AI and IoT at Near East University, Nicosia, Cyprus. Prof. Al-Turjman is the head of Artificial Intelligence Engineering Dept., and a leading authority in the areas of smart/intelligent IoT systems, wireless, and mobile networks architectures, protocols, deployments, and performance evaluation in Artificial Intelligence of Things (AIoT).
Prof. John Walsh is the Associate Dean and Director, International College, Krirk University, Thailand. He received his doctorate from Oxford University in 1997 for a thesis concerning international market entry strategy and the success of UK firms in Korea, Japan and Taiwan. He has lived and worked in Sudan, Greece, Korea, Australia, the United Arab Emirates, Thailand and Vietnam, as well as his native UK. He has also taught courses at undergraduate, graduate and PhD level in a number of countries and led the campus at Mandalay and Kathmandu for a previous position, during which time he has taught courses in international business, marketing, management, entrepreneurialism, human resources, and finance.
