Atul Narayan Fegade
@sbup.edu.in
Senior Assistant Professor
Sri Balaji University, Pune
EDUCATION
PhD + MBA
RESEARCH, TEACHING, or OTHER INTERESTS
Marketing, Strategy and Management, Management of Technology and Innovation
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Atul Narayan Fegade, Sushil Kumar Gupta, and Vishnu Maya Rai
IGI Global
The micro, small, and medium enterprises (MSMEs) are the backbone of Indian economy. MSMEs have significant contributions in the entrepreneurial activities in India. There is special focus on women-owned enterprises by Ministry of MSME with offering many schemes. The economic empowerment of women can be achieved through promoting micro and small-scale industries of women. This would also help in reducing poverty and gender inequality. The percentage of female population in India as per the 2011 census is 48.49%, and as per the MSME Ministry's Annual Report 2020-21, only 20% of the 63 million MSMEs in India are owned by women. India ranks a lowly 70th among 77 countries covered in the Female Entrepreneurship Index. It also has the third-highest gender gap in entrepreneurship across the world. Only 33% of early-stage entrepreneurs in India are women; male entrepreneurial activity rates exceed female entrepreneurial rates by 7%.
Atul Fegade, Rajesh Raut, Amruta Deshpande, Amit Mittal, Natashaa Kaul, and Vandana Khanna
IEEE
Integrating generative artificial intelligence tools can allow one to manage and develop novel business methods efficiently. With the introduction of self-learning generative artificial intelligence (GAI) tools, the corporate world is trying to figure out the applications and their business implications. Major technology companies like Microsoft, Google, Facebook, etc., have shown enormous interest in investing in ventures developing generative AI tools/models. These tools/models can be experimented with, and their business use is being explored. The study aims to explore the potential uses of generative AI (GAI) in academics through its current capabilities and applications. The study also highlights the potential challenges and concerns arising from using generative AI in academics. The study has used descriptive and quantitative methods to answer the research questions. The study has used a questionnaire based on the Likert scale to measure the significance of indirect variables, viz. perceived ease of use (PEOU) and perceived trust (PT) on direct variable adoption of generative artificial intelligence (AGAI) in academics. The sample size consists of 100 students and 100 university professors. The study Mentions that perceived ease of use (PEOU) positively influences GAI (AGAI) adoption. Also, perceived trust (PT) has a predictive ability of AGIA when controlling for PEOU. The use of generative AI in a few years will become imperative in most human lives, which makes it mandatory to explore the possible ways of its adoption in the mainstream rather than avoiding or restricting its usage in academics.
Swati Vashisht, Sushil Kumar Gupta, Atul Fegade, Shiv Ashish Dhondiyal, Rohit Kumar, and G Revathy
IEEE
Violent social bots automate social interactions, create fictitious profiles to spread destructive propaganda, or assume the identities of followers to make misleading tweets. Furthermore, malicious social bots disseminate malicious root URLs, which reroute requests from online social media agents to certain malicious servers. Therefore, one of the most crucial jobs of the Twitter network is to distinguish between actual drug users and active social bots. Instead of taking as long to remove as social graph-based features, URL-based features can identify the cruel conduct of social bots. It’s difficult for malicious social bots to change URL redirect chains. This part offers a literacy automaton-grounded vicious social bot discovery (LA-MSBD) for safe (drug) agents on the Twitter network by fusing URL-based functionality with a trust computation model. The research discussed in this paper focuses on designing, utilizing, and evaluating robotic sensors based on deep literacy models rather than adding metadata about position or birthpoint counting. This paper also demonstrates how deep literacy models can compete with conventional machine-ability idioms. The findings of this study demonstrate that in-depth comprehension models can be made more complex by utilizing pre-trained models.