Transforming Industries, Empowering Societies
A Comprehensive Examination of Industry 5.0 and Society 5.0
- 1st Edition - December 9, 2025
- Latest edition
- Editors: Parikshit Narendra Mahalle, Gitanjali R. Shinde, Namrata N. Wasatkar, Prashant Ramchandra Anerao
- Language: English
With the ever-increasing use of AI technologies, ethical considerations take on greater importance. Human-centric AI emphasizes transparency, making sure that AI systems work in a… Read more
With the ever-increasing use of AI technologies, ethical considerations take on greater importance. Human-centric AI emphasizes transparency, making sure that AI systems work in a way that users can comprehend and trust. Additionally, it addresses bias and discrimination issues, ensuring fairness and inclusion in the design and implementation of AI apps. By emphasizing user experience, security, and human-centric AI, the goal is to improve collaboration between people and machines, rather than replacing human decisions, creating a future where technology is a force for good, benefiting both businesses and society. Written from a technological point of view, Industry 5.0 for Society 5.0 explores the impact of cutting-edge technologies, including the Internet of Things, cloud, artificial intelligence, and digital twin, on individuals and community, and considers how they can be used to solve societal problems. The book considers how these technologies can positively affect industry, healthcare, agriculture, design and manufacture, contributing to the development of a sustainable environment that ultimately creates a positive and mutually beneficial relationship between people and AI.
- Presents the fundamentals, challenges and advancements in AI required for Industry 5.0 to be beneficial to society
- Focuses on human-centric AI and how it can be used to create more sustainable industry
- Explores the ethical considerations and regulatory aspects of Edge AI, helping readers navigate the responsible use of this technology
Researchers, graduate students and postgraduate students in AI, machine learning, data science, computer science and related fields
Contents
Contributors xiii
Foreword xv
Preface xvii
Part I
Transformation towards Industry 5.0
1. Transitioning from traditional artificial intelligence to emerging trends: Exploring paradigm shifts, challenges, and opportunities
Anamika Anu, Jagrati Nagdiya and Sheril Thomas
1.1. Introduction
1.2. Paradigm shifts
1.3. Technology
1.4. Computational power
1.5. Cognitive understanding
1.6. Traditional artificial intelligence approaches
1.7. Limitations of early artificial intelligence systems
1.8. Emerging trends in artificial intelligence
1.9. Artificial intelligence-powered solutions
1.10. Challenges and ethical considerations
1.11. Data privacy, security, and interpretability
1.12. Challenges and opportunities in the transition
1.13. Conclusion
References
2. Human-machine collaboration in Industry 5.0 using Big Data analytics
Samiksha Khule, Muskan Sihare, Rakhi Arora, Nitin Dixit, Gaurav Dubey and Yogesh Kumar Sharma
2.1. Introduction
2.2. Technologies of Industry 5.0
2.3. Creative applications of Industry 5.0
2.4. The role of vision transformers in industry 4.0 and Industry 5.0
2.5. Principles of Industry 5.0
2.6. Literature review
2.7. Challenges in Industry 5.0
2.8. Limitations in Industry 5.0
2.9. Conclusion
References
3. Implications of Industry 5.0 for Society 5.0: A systematic literature review
Ganesh Narkhede, Gajanan Ghuge and Madahavi Mohite
3.1. Introduction
3.2. Literature review
3.3. Results and discussion
3.4. Conclusion
References
4. Cloud security through robust cryptographic measures: Overview, advances, and application
Radha Nishant Deoghare, Prachi Nishant Shah-Bahekar, Shradha Nishant Tawade and Sapana Nishant Kolambe
4.1. Introduction
4.2. Related Work
4.3. Proposed Approach
4.4. Result Analysis
4.5. Conclusion
References
5. Mesocaps: Enhancing deepfake detection 1
Umesh Pranjal Shirsat, Shivani Joshi, Siddhi Shinde, Vaibhav Garje, Amit Joshi and Suraj Sawant
5.1. Introduction
5.2. Literature review
5.3. Deepfake generation
5.4. Deepfake detection
5.5. Gap analysis
5.6. Methodology
5.7. Model architecture
5.8. MesoNet
5.9. Capsule network
5.10. Results and discussion
5.11. Experimental setup
5.12. Performance metrics and comparison
5.13. Conclusion and future scope
References
Part II
Transformation in Healthcare 5.0
6. Digital health evaluation: A roadmap ahead
Pranali Chavhan, Namrata Kharate, Prashant Anerao and Gajanan Chavhan
6.1. Introduction
6.2. Current Approaches to Digital Health Evaluation
6.3. A Roadmap for Future Evaluation
6.4. Case Studies
6.5. Digital Health: Barrier and Solution
6.6. Conclusion
References
7. Adapting online medical services for the well-being diverse patients
Jyoti Deshmukh, Vijay Rathod, Nilesh Sable and Gitanjali Shinde
7.1. Introduction
7.2. Telemedicine Strategy Implementation in 2019 During the COVID-19 Era
7.3. Technological Solutions for Telemedicine
7.4. Related Information
7.5. COVID-19 Pandemic: The Remote Medication Network for Neurorehabilitation
7.6. Conclusion
References
8. Revolutionizing healthcare using digital twins: Monitoring, analysis, and advancement
Rakhi Arora, Nitin Dixit, Jigyasa Mishra, Muskan Sihare, Samiksha Khule and Yogesh Kumar Sharma
8.1. Introduction
8.2. Literature Survey
8.3. Enabling Technologies and Data Sources
8.4. Digital Twin In Healthcare—Applications
8.5. Integration of Artificial Intelligence in Human Digital Twins
8.6. Limitations Associated with Healthcare Digital Twins
8.7. Conclusion
References
9. Wellbeing of working mothers based on decision making: A data science approach 1
Jyoti Deshmukh, Vijay Rathod, Nilesh Sable and Gitanjali Shinde
9.1. Introduction
9.2. Exploration of Wearable Devices
9.3. Experimental Methods
9.4. Monitoring of Fetal Movement
9.5. The Wearable Device Design
9.6. The Patient Data Possession
9.7. Energy Assessment
9.8. Fetal Movement Extraction of Feature
9.9. Design of Phantom—The Simulation System for Fetal Movement
9.10. Conclusion
References
Part III
Transformation in agriculture
10. Navigating the agricultural landscape: Artificial intelligence and Industry 5.0 insights
Pradnya Samit Mehta and Sanved Narwadkar
10.1. Overview of Artificial Intelligence in Agriculture
10.2. Role of Artificial Intelligence in Decision-making
10.3. Precision Agriculture Techniques
10.4. Data-driven Crop Yield Predictions
10.5. Climate and Weather Impact Assessment Strategies With Artificial Intelligence
10.6. Holistic Approach With Artificial Intelligence for Industry 5.0 Society 5.0
10.7. Smart Irrigation Systems for Artificial Intelligence Advancements in Farming: A Revolution an Agriculture
10.8. Case Studies Demonstrating Increased Water Efficiency and Crop Yield
10.9. Conclusion
References
11. Industry 5.0 unveiled, precision agriculture empowered: Integrating recommendation and prediction systems for transparent farming transactions
Kaustubh Vitthal Rathod, Devesh Rathi and Sankalp Naranje
11.1. Introduction
11.2. Methodology
11.3. Results and Discussion
11.4. Conclusion
11.5. Future Scope
References
12. Enhancing agricultural resilience through synergistic human–AI collaboration in Industry 5.0
Yogesh Kumar Sharma, Samiksha Khule, Gaurav Dubey, Rakhi Arora, Nitin Dixit and Muskan Sihare
12.1. Introduction
12.2. Literature Review
12.3. Industry 5.0 Technologies
12.4. Industry 4.0 vs Industry 5.0
12.5. Challenges of Industry 5.0
12.6. Industry 5.0: Applications
12.7. Industry 5.0: Limitations
12.8. Future Directions
12.9. Conclusion
References
13. Cultivating the future of agriculture where digital twin meets artificial intelligence
Muskan Sihare, Samiksha Khule, Rakhi Arora, Nitin Dixit, Gaurav Dubey and Yogesh Kumar Sharma
13.1. Introduction
13.2. Literature Review
13.3. Digital Twin Definition
13.4. Digital Twin in Agriculture
13.5. Artificial Intelligence for the Digital Twin
13.6. Artificial Intelligence and Digital Twin Convergence
13.7. Agriculture Has Undergone Distinct Phases of Evolution
13.8. The Industrial Revolution’s Phases Can Be Compared With The Development of Agricultural Technology
13.9. Digital Agriculture Tools
13.10. Application of Digital Twins in Agriculture
13.11. Benefits and Challenges
13.12. The Future Pathways for Digital Twins
13.13. Conclusion
References
14. Explainable artificial intelligence for plant disease diagnosis
Diana Susan Joseph and Pranav M Pawar
14.1. Introduction
14.2. Related Works
14.3. Methods of Explainable Artificial Intelligence
14.4. Explainable Artificial Intelligence For Sustainable Agriculture
14.5. Research Directions of Artificial Intelligence in Agriculture With Explainable Artificial Intelligence
14.6. Conclusion
References
Part IV
Transformation in Design & Manufacturing
15. Challenges, opportunities, and frameworks for human-centric design and manufacturing in Industry 5.0
Prashant Anerao, Namrata Kharate, Yashwant Shrirang Munde and Pranali Chavhan
15.1. Introduction to Industry 5.0
15.2. Challenges and Opportunities
15.3. Framework of Industry 5.0 in Design and Manufacturing
15.4. Key Considerations for Implementation
15.5. Roadmap Ahead
15.6. Conclusion
References
16. Transformation in manufacturing industry: Review and future trends
Mansi Subhedar and Suyog Dasnurkar
16.1. Introduction
16.2. Collaborative Robots
16.3. Digital Twins and Simulations
16.4. Virtual Reality and Augmented Reality for Industrial Testing
16.5. AI and ML in Manufacturing
16.6. Challenges for Transformations in the Manufacturing Industry
16.7. Future Directions
16.8. Conclusion
References
17. The pivotal role of artificial intelligence in digital twins: A case study
Nalini Jagtap, Trisha Singh and Eshwari Sonawane
17.1. Introduction
17.2. Literature Survey
17.3. Core Functionalities of Artificial Intelligence in Digital Twins
17.4. Case Studies and Applications
17.5. Conclusion
References
18. Developing artificial intelligence applications in manufacturing using digital twin-driven machine learning technology1
Dixit Nitin, Rakhi Arora, Vijay Sharma, Muskan Sihare, Samiksha Khule and Bhawna Ojha
18.1. Introduction
18.2. Background and Recent Advances
18.3. Framework for Digital Twin-driven Industrial Artificial Intelligence
18.4. Digital Twin in Machine Learning
18.5. Conclusion
References
Part V
Energy and sustainable development
19. The role of optimization techniques in achieving sustainable artificial intelligence 1
Hanan Hussain and S. Tamizharasan
19.1. Introduction
19.2. Related Works
19.3. Optimization Techniques for Sustainable Artificial Intelligence
19.4. Challenges and Open Issues in Achieving Sustainable Artificial Intelligence
19.5. Conclusion
References
20. Smart disaster management: Leveraging machine learning and remote sensing for informed decision-making
Ruta Prabhu, Anupama Jawale, Hiral Patel, Disha Gandhi, Shivwani Nadar and Riddhi Lonandkar
20.1. Introduction
20.2. Literature Review
20.3. Methods for Disaster Monitoring
20.4. Overview of Various Algorithms for Disaster and Hazard Detection
20.5. Tsunami Detection
20.6. Conclusion
References
21. Navigating ethical complexities in energy transitions
Bhawna Ojha, Yogesh Kumar Sharma, Khemchand Shakywar and Aniket Arya
21.1. Introduction
21.2. Understanding Industry 5.0
21.3. Benefits and Challenges of Industry 5.0 Implementation
21.4. Ethical Complexities in Energy Transitions
21.5. Addressing Ethical Complexities Through Industry 5.0
21.6. Stakeholder Engagement and Collaboration7
21.7. Future Outlook and Recommendations
21.8. Conclusion
References
22. Revolutionizing energy storage for a smart society
Asmita Kalamkar, Gitanjali Shinde, Riddhi Mirajkar, ParikshitMahalle,Namrata Kharate and Prashant Anerao
22.1. Background and Context
22.2. Green Computing: Principles and Practices
22.3. Renewable Energy Integration
22.4. Case Studies and Applications
22.5. Challenges and Barriers
22.6. Conclusion
References
23. Green energy storage: Bridging sustainability and smart industries
Riddhi Mirajkar, Gitanjali Shinde, Snehal Rathi, Vidula Meshram, Pankaj Chandre and Pranali Chavhan
23.1. Introduction
23.2. Fundamentals of Green Energy Storage
23.3. Advanced Energy Storage Technologies
23.4. Artificial Intelligence and Internet of Things in Smart Energy Storage
23.5. Integrating Green Energy Storage in Industry 5.0
23.6. Policy and Regulatory Frameworks
23.7. Challenges and Future Prospects of Energy Storage
23.8. Conclusion
References
Index
Contributors xiii
Foreword xv
Preface xvii
Part I
Transformation towards Industry 5.0
1. Transitioning from traditional artificial intelligence to emerging trends: Exploring paradigm shifts, challenges, and opportunities
Anamika Anu, Jagrati Nagdiya and Sheril Thomas
1.1. Introduction
1.2. Paradigm shifts
1.3. Technology
1.4. Computational power
1.5. Cognitive understanding
1.6. Traditional artificial intelligence approaches
1.7. Limitations of early artificial intelligence systems
1.8. Emerging trends in artificial intelligence
1.9. Artificial intelligence-powered solutions
1.10. Challenges and ethical considerations
1.11. Data privacy, security, and interpretability
1.12. Challenges and opportunities in the transition
1.13. Conclusion
References
2. Human-machine collaboration in Industry 5.0 using Big Data analytics
Samiksha Khule, Muskan Sihare, Rakhi Arora, Nitin Dixit, Gaurav Dubey and Yogesh Kumar Sharma
2.1. Introduction
2.2. Technologies of Industry 5.0
2.3. Creative applications of Industry 5.0
2.4. The role of vision transformers in industry 4.0 and Industry 5.0
2.5. Principles of Industry 5.0
2.6. Literature review
2.7. Challenges in Industry 5.0
2.8. Limitations in Industry 5.0
2.9. Conclusion
References
3. Implications of Industry 5.0 for Society 5.0: A systematic literature review
Ganesh Narkhede, Gajanan Ghuge and Madahavi Mohite
3.1. Introduction
3.2. Literature review
3.3. Results and discussion
3.4. Conclusion
References
4. Cloud security through robust cryptographic measures: Overview, advances, and application
Radha Nishant Deoghare, Prachi Nishant Shah-Bahekar, Shradha Nishant Tawade and Sapana Nishant Kolambe
4.1. Introduction
4.2. Related Work
4.3. Proposed Approach
4.4. Result Analysis
4.5. Conclusion
References
5. Mesocaps: Enhancing deepfake detection 1
Umesh Pranjal Shirsat, Shivani Joshi, Siddhi Shinde, Vaibhav Garje, Amit Joshi and Suraj Sawant
5.1. Introduction
5.2. Literature review
5.3. Deepfake generation
5.4. Deepfake detection
5.5. Gap analysis
5.6. Methodology
5.7. Model architecture
5.8. MesoNet
5.9. Capsule network
5.10. Results and discussion
5.11. Experimental setup
5.12. Performance metrics and comparison
5.13. Conclusion and future scope
References
Part II
Transformation in Healthcare 5.0
6. Digital health evaluation: A roadmap ahead
Pranali Chavhan, Namrata Kharate, Prashant Anerao and Gajanan Chavhan
6.1. Introduction
6.2. Current Approaches to Digital Health Evaluation
6.3. A Roadmap for Future Evaluation
6.4. Case Studies
6.5. Digital Health: Barrier and Solution
6.6. Conclusion
References
7. Adapting online medical services for the well-being diverse patients
Jyoti Deshmukh, Vijay Rathod, Nilesh Sable and Gitanjali Shinde
7.1. Introduction
7.2. Telemedicine Strategy Implementation in 2019 During the COVID-19 Era
7.3. Technological Solutions for Telemedicine
7.4. Related Information
7.5. COVID-19 Pandemic: The Remote Medication Network for Neurorehabilitation
7.6. Conclusion
References
8. Revolutionizing healthcare using digital twins: Monitoring, analysis, and advancement
Rakhi Arora, Nitin Dixit, Jigyasa Mishra, Muskan Sihare, Samiksha Khule and Yogesh Kumar Sharma
8.1. Introduction
8.2. Literature Survey
8.3. Enabling Technologies and Data Sources
8.4. Digital Twin In Healthcare—Applications
8.5. Integration of Artificial Intelligence in Human Digital Twins
8.6. Limitations Associated with Healthcare Digital Twins
8.7. Conclusion
References
9. Wellbeing of working mothers based on decision making: A data science approach 1
Jyoti Deshmukh, Vijay Rathod, Nilesh Sable and Gitanjali Shinde
9.1. Introduction
9.2. Exploration of Wearable Devices
9.3. Experimental Methods
9.4. Monitoring of Fetal Movement
9.5. The Wearable Device Design
9.6. The Patient Data Possession
9.7. Energy Assessment
9.8. Fetal Movement Extraction of Feature
9.9. Design of Phantom—The Simulation System for Fetal Movement
9.10. Conclusion
References
Part III
Transformation in agriculture
10. Navigating the agricultural landscape: Artificial intelligence and Industry 5.0 insights
Pradnya Samit Mehta and Sanved Narwadkar
10.1. Overview of Artificial Intelligence in Agriculture
10.2. Role of Artificial Intelligence in Decision-making
10.3. Precision Agriculture Techniques
10.4. Data-driven Crop Yield Predictions
10.5. Climate and Weather Impact Assessment Strategies With Artificial Intelligence
10.6. Holistic Approach With Artificial Intelligence for Industry 5.0 Society 5.0
10.7. Smart Irrigation Systems for Artificial Intelligence Advancements in Farming: A Revolution an Agriculture
10.8. Case Studies Demonstrating Increased Water Efficiency and Crop Yield
10.9. Conclusion
References
11. Industry 5.0 unveiled, precision agriculture empowered: Integrating recommendation and prediction systems for transparent farming transactions
Kaustubh Vitthal Rathod, Devesh Rathi and Sankalp Naranje
11.1. Introduction
11.2. Methodology
11.3. Results and Discussion
11.4. Conclusion
11.5. Future Scope
References
12. Enhancing agricultural resilience through synergistic human–AI collaboration in Industry 5.0
Yogesh Kumar Sharma, Samiksha Khule, Gaurav Dubey, Rakhi Arora, Nitin Dixit and Muskan Sihare
12.1. Introduction
12.2. Literature Review
12.3. Industry 5.0 Technologies
12.4. Industry 4.0 vs Industry 5.0
12.5. Challenges of Industry 5.0
12.6. Industry 5.0: Applications
12.7. Industry 5.0: Limitations
12.8. Future Directions
12.9. Conclusion
References
13. Cultivating the future of agriculture where digital twin meets artificial intelligence
Muskan Sihare, Samiksha Khule, Rakhi Arora, Nitin Dixit, Gaurav Dubey and Yogesh Kumar Sharma
13.1. Introduction
13.2. Literature Review
13.3. Digital Twin Definition
13.4. Digital Twin in Agriculture
13.5. Artificial Intelligence for the Digital Twin
13.6. Artificial Intelligence and Digital Twin Convergence
13.7. Agriculture Has Undergone Distinct Phases of Evolution
13.8. The Industrial Revolution’s Phases Can Be Compared With The Development of Agricultural Technology
13.9. Digital Agriculture Tools
13.10. Application of Digital Twins in Agriculture
13.11. Benefits and Challenges
13.12. The Future Pathways for Digital Twins
13.13. Conclusion
References
14. Explainable artificial intelligence for plant disease diagnosis
Diana Susan Joseph and Pranav M Pawar
14.1. Introduction
14.2. Related Works
14.3. Methods of Explainable Artificial Intelligence
14.4. Explainable Artificial Intelligence For Sustainable Agriculture
14.5. Research Directions of Artificial Intelligence in Agriculture With Explainable Artificial Intelligence
14.6. Conclusion
References
Part IV
Transformation in Design & Manufacturing
15. Challenges, opportunities, and frameworks for human-centric design and manufacturing in Industry 5.0
Prashant Anerao, Namrata Kharate, Yashwant Shrirang Munde and Pranali Chavhan
15.1. Introduction to Industry 5.0
15.2. Challenges and Opportunities
15.3. Framework of Industry 5.0 in Design and Manufacturing
15.4. Key Considerations for Implementation
15.5. Roadmap Ahead
15.6. Conclusion
References
16. Transformation in manufacturing industry: Review and future trends
Mansi Subhedar and Suyog Dasnurkar
16.1. Introduction
16.2. Collaborative Robots
16.3. Digital Twins and Simulations
16.4. Virtual Reality and Augmented Reality for Industrial Testing
16.5. AI and ML in Manufacturing
16.6. Challenges for Transformations in the Manufacturing Industry
16.7. Future Directions
16.8. Conclusion
References
17. The pivotal role of artificial intelligence in digital twins: A case study
Nalini Jagtap, Trisha Singh and Eshwari Sonawane
17.1. Introduction
17.2. Literature Survey
17.3. Core Functionalities of Artificial Intelligence in Digital Twins
17.4. Case Studies and Applications
17.5. Conclusion
References
18. Developing artificial intelligence applications in manufacturing using digital twin-driven machine learning technology1
Dixit Nitin, Rakhi Arora, Vijay Sharma, Muskan Sihare, Samiksha Khule and Bhawna Ojha
18.1. Introduction
18.2. Background and Recent Advances
18.3. Framework for Digital Twin-driven Industrial Artificial Intelligence
18.4. Digital Twin in Machine Learning
18.5. Conclusion
References
Part V
Energy and sustainable development
19. The role of optimization techniques in achieving sustainable artificial intelligence 1
Hanan Hussain and S. Tamizharasan
19.1. Introduction
19.2. Related Works
19.3. Optimization Techniques for Sustainable Artificial Intelligence
19.4. Challenges and Open Issues in Achieving Sustainable Artificial Intelligence
19.5. Conclusion
References
20. Smart disaster management: Leveraging machine learning and remote sensing for informed decision-making
Ruta Prabhu, Anupama Jawale, Hiral Patel, Disha Gandhi, Shivwani Nadar and Riddhi Lonandkar
20.1. Introduction
20.2. Literature Review
20.3. Methods for Disaster Monitoring
20.4. Overview of Various Algorithms for Disaster and Hazard Detection
20.5. Tsunami Detection
20.6. Conclusion
References
21. Navigating ethical complexities in energy transitions
Bhawna Ojha, Yogesh Kumar Sharma, Khemchand Shakywar and Aniket Arya
21.1. Introduction
21.2. Understanding Industry 5.0
21.3. Benefits and Challenges of Industry 5.0 Implementation
21.4. Ethical Complexities in Energy Transitions
21.5. Addressing Ethical Complexities Through Industry 5.0
21.6. Stakeholder Engagement and Collaboration7
21.7. Future Outlook and Recommendations
21.8. Conclusion
References
22. Revolutionizing energy storage for a smart society
Asmita Kalamkar, Gitanjali Shinde, Riddhi Mirajkar, ParikshitMahalle,Namrata Kharate and Prashant Anerao
22.1. Background and Context
22.2. Green Computing: Principles and Practices
22.3. Renewable Energy Integration
22.4. Case Studies and Applications
22.5. Challenges and Barriers
22.6. Conclusion
References
23. Green energy storage: Bridging sustainability and smart industries
Riddhi Mirajkar, Gitanjali Shinde, Snehal Rathi, Vidula Meshram, Pankaj Chandre and Pranali Chavhan
23.1. Introduction
23.2. Fundamentals of Green Energy Storage
23.3. Advanced Energy Storage Technologies
23.4. Artificial Intelligence and Internet of Things in Smart Energy Storage
23.5. Integrating Green Energy Storage in Industry 5.0
23.6. Policy and Regulatory Frameworks
23.7. Challenges and Future Prospects of Energy Storage
23.8. Conclusion
References
Index
- Edition: 1
- Latest edition
- Published: December 9, 2025
- Language: English
PM
Parikshit Narendra Mahalle
Dr Parikshit is a senior member IEEE and is Professor, Dean Research and Development and Head - Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology, Pune, India. He completed his Ph. D from Aalborg University, Denmark and continued as Post Doc Researcher at CMI, Copenhagen, Denmark. He has 23 + years of teaching and research experience. He is an ex-member of the Board of Studies in Computer Engineering, Ex-Chairman Information Technology, Savitribai Phule Pune University and various Universities and autonomous colleges across India. He has 15 patents, 200+ research publications (Google Scholar citations-2950 plus, H index-25 and Scopus Citations are 1550 plus with H index -18, Web of Science citations are 438 with H index - 10) and authored/edited 56 books with Springer, CRC Press, Cambridge University Press, etc. He is editor in chief for IGI Global International Journal of Rough Sets and Data Analysis, Inter-science International Journal of Grid and Utility Computing, member-Editorial Review Board for IGI Global – International Journal of Ambient Computing and Intelligence and reviewer for various journals and conferences of the repute. His research interests are Machine Learning, Data Science, Algorithms, Internet of Things, Identity Management and Security. He is guiding 8 PhD students in the area of IoT and machine learning and SIX students have successfully defended their PhD under his supervision from SPPU. He is also the recipient of “Best Faculty Award” by Sinhgad Institutes and Cognizant Technologies Solutions. He has delivered 200 plus lectures at national and international level.
Affiliations and expertise
Professor, Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, Maharashtra, IndiaGS
Gitanjali R. Shinde
Dr. Gitanjali R. Shinde has overall 15 years of experience, presently working as Associate Professor in Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India. She did her Ph.D. in Wireless Communication from CMI, Aalborg University, Copenhagen, Denmark on Research Problem Statement “Cluster Framework for Internet of People, Things and Services”. She obtained M.E. (Computer Engineering) degree from the University of Pune, Pune in 2012 and B.E. (Computer Engineering) degree from the University of Pune, Pune in 2006. She has received research funding for the project “Lightweight group authentication for IoT” by SPPU, Pune. She has presented a research article in the World Wireless Research Forum (WWRF) meeting, Beijing China. She has published 50+ papers in National, International conferences and journals. She is author of 10+ books with publishers Springer and CRC Taylor & Francis Group and she is also editor of books. Her book “Data Analytics for Pandemics A COVID 19 Case Study” is awarded outstanding Book of the year 2020.
Affiliations and expertise
Vishwakarma Institute of Information Technology, IndiaNW
Namrata N. Wasatkar
Dr.Namrata N Wasatkar has overall 10 years of experience, presently working as Assistant Professor in Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India. She has done Ph.D. in Computer Engineering from Savitribai Phule Pune University, Pune, India on Research Problem Statement “Rule based Machine translation of simple Marathi sentences to English sentences”. She obtained M.E. (Computer Engineering) degree from the University of Pune, Pune in 2014 and B.E. (Computer Engineering) degree from the University of Pune, Pune in 2012. She has received research funding for the project “SPPU online chatbot” by SPPU, Pune. She has published 14+ papers in National, International conferences and journals.
Affiliations and expertise
Vishwakarma Institute of Information TechnologyPA
Prashant Ramchandra Anerao
Prashant Anerao is an assistant professor in the Department of Mechanical Engineering at the Vishwakarma Institute of Information Technology, Pune. He earned his M. Tech. in Mechanical Engineering, specialisation in Manufacturing Engineering, from the Indian Institute of Technology, Bombay, in 2009. His research encompasses additive manufacturing of bio composites and the application of machine learning in 3D printing. He has published research papers in peer-reviewed journals, contributed to book chapters, and presented findings at national and international conferences. His work reflects a dedication to advancing engineering and technology, with a focus on innovative approaches in manufacturing and 3D printing.
Affiliations and expertise
Vishwakarma Institute of Information Technology, India