Dr. Sushil Kumar Gupta

@mitwpu.edu.in

Assistant Professor, School of Management
Dr. Vishwanath Karad MIT World Peace University, Pune



              

https://researchid.co/ksushil111978

Dr. Sushil Kumar Gupta, currently working as an Assistant Professor in School of Management at Dr. Vishwanath Karad MIT World Peace University, Pune. Having a rich experience of 10 years in the area of Financial Advisory, Asset Management, and Sales& business development of Financial Services and 8 years in Teaching and Research. Has authored multiple research papers on Mutual Fund, Micro Finance, and Family Office which were published in peer-reviewed Scopus Index Journals in the area of Finance.

EDUCATION

Ph.D. in Management, MBA (Finance), UGC NET, NCMP Level 1 Certification.

RESEARCH INTERESTS

Finance, Wealth Management, Banking, Insurance, Mutual Fund, and Stock Market

17

Scopus Publications

Scopus Publications

  • Compensator design for DC-DC converters to improve the performance of the system
    Sangeet Adhikary and Sushil Kumar Gupta

    IEEE
    A DC-DC converter in the context of power electronics is a device which is used to power distribution in different level (E.g., lights, sensors, motor control etc.). Now a days with the increasing demand of Electric Vehicles (EVs), not only design of converter but also good controller/compensator is required to improve efficiency of converter and reliability of the system for users. In the context of power electronics, compensator is used to upgrade the behavior and stability of a converter. This paper presents the study on stability analysis & implementation of compensator for DC-DC converters as per requirement whereas an example of buck converter has used as reference. The proposed converter topology is discussed & as per requirement designed compensator for the buck converter. MATLAB is utilized as the simulation platform to model and analyze the open loop & closed loop system of the converter.

  • ICT for women entrepreneurs in MSMEs in India
    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%.

  • Crypto-hesitancy: is regulation the answer?
    Joseph Ejike Ojih, Parikshit Joshi, Ashish Mohture, and Sushil Kumar Gupta

    Emerald
    Purpose The purpose of this paper is to explore and address the possible reasons for the hesitancy in accepting cryptocurrency as an asset class by the world governments and central banks. The behaviour of delaying the acceptance or using cryptocurrency has been termed as crypto-hesitancy. Design/methodology/approach To establish the conceptual understanding of crypto-hesitancy, the bibliometric analysis was performed through Bibliometrix and VOSviewer. Through keyword search technique this study has located 507 useful studies in Scopus database, which were used for the bibliometric analysis. Findings The findings of the study reveal that the government of developed and developing nations and central banks hesitate to regulate and accept cryptocurrency due to the following reasons: cryptocurrency’s ties to illegal activity, speculation and cryptocurrency’s capacity to circumvent government-imposed capital controls. The findings of this study can be used as platform to develop the construct – crypto-hesitancy – further and explore the empirical insights of it. Originality/value To the best of the authors’ knowledge, the construct crypto-hesitancy has not been evolved yet, which makes this study the first attempt to theoretically understand the concept and its evolution.

  • Blockchain technology-based fake news detection: Applications and future research directions
    Susheel Yadav, Om Jee Gupta, Sushil Kumar Gupta, and Harish Babu

    Chapman and Hall/CRC

  • When Sustainable Development Embraces Blockchain: A Systematic Literature Review
    Parikshit Joshi, Anshu Singh, Shailendra Kumar, Garima Joshi, Ankit Aggarwal, and Sushil Kumar Gupta

    Springer Nature Singapore

  • Factors Influencing Behavioural Intentions Towards Investment in Cryptocurrency: A Study on Generation Z Female of India
    Garima Joshi, Prabodh Narayan Gour, Pravesh Soti, Ankit Aggarwal, Harshwardhan Singh, and Sushil Kumar Gupta

    Springer Nature Singapore

  • A novel brainstorm based optimization method for optimum planning of reactive power with FACTS devices
    Lalit Kumar, Sushil Kumar Gupta, and Sanjay Kumar

    Springer Science and Business Media LLC

  • Optimal reactive power dispatch under coordinated active and reactive load variations using FACTS devices
    Sushil Kumar Gupta, Lalit Kumar, Manoj Kumar Kar, and Sanjay Kumar

    Springer Science and Business Media LLC

  • Asset Class Market Investment Portfolio Analysis and Tracking
    Pramod A Jadhav, C. Vinotha, Sushil Kumar Gupta, Bijesh Dhyani, Vinod H Patil, and Rohit Kumar

    IEEE
    Researchers and analysts have, for a long time, taken an interest in the expectations that consumers have regarding stock marketing. There is a widespread perception that trying to forecast what will happen in the stock market is fruitless since the market behaves fundamentally like a random walk. Predicting swings in stock prices may be difficult because of the many different elements that are in play. In the short run, the showcase acts as a voting machine, but in the long run, it acts as a scale, which enables the prediction of its long-term trajectory. It seems that the application of machine learning and other technologies to the issues of inventory analysis and cost estimates is a new sector that has a lot of potential. To begin, we will present a concise introduction to stock markets and an in-depth explanation of how to make predictions regarding stock prices. The next step is for us to hone in on many of the most significant problems surrounding the precision of stock research and estimates. The fundamental, intermediate, short-term, and long-term perspectives on stocks, as well as their technical aspects, are explored. In conclusion, we highlight a few problems and investigate possible advancements that the future may hold in this sector.

  • Detection of Malicious Social Bots with the Aid of Learning Automata on Twitter
    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.

  • Anamoly Detection in Very Large Scale System using Big Data
    Sushil Kumar Gupta, Govinda Rajulu Lanke, Manoj Pareek, Monika Mittal, Dharmesh Dhabliya, T Venkatesh, and Subhra Chakraborty

    IEEE
    Big data refers to a term that is used to describe vast amounts of data that have multiple kinds of Vs: velocity, variety, and volume. It could be semi-structured, unstructured, or even structured, making data analysis difficult. New architecture, methodologies, algorithms, and analytics are needed to extract hidden data and identify assaults on enormous amounts of data. It is quite challenging to identify assaults using conventional methods. This study provides a thorough analysis of malware detection in several sectors using deep learning and provides an overview of deep learning data. In networked computers, there have been more attacks. To protect a network, a strong intrusion detection system (IDS) is necessary. Reviewing the literature reveals that while some research has been conducted in this area, a thorough and in-depth investigation has not yet been carried out. For unanticipated and unpredictable assaults, many academics suggested an IDS employing deep learning, but not for big data. The present research design is based on three ensemble methods, Randam Forest, Decision tree regression, and Gradient Boosting Tree, as well as a deep learning-based intrusion detection system for large datasets named RNN that runs for 1,000 epochs with a learning rate complexity and diversity [0.01-0.5]. It is employed in the creation of the hybrid, safe, and scalable, which is based on big data and deep learning methods. In comparison to using just one classifier, the suggested classifiers provide a more accurate classification. Detection rate (99 percentage), false positive rate (1.5 percent), accuracy (99 percentage), and F-Measure (99.03%) are the experimental results. The results show that new anomaly detection methods work better in the big data context.

  • A Simplified Sine Cosine Algorithm for the Solution of Optimal Reactive Power Dispatch
    Sushil Kumar Gupta, Manoj Kumar Kar, Lalit Kumar, and Sanjay Kumar

    Hindawi Limited
    In this article, a simplified sine cosine algorithm (SSCA) is applied to solve the optimal reactive power dispatch (ORPD) issues by estimating the control variables. This algorithm uses sine cosine functions and generates number of random solutions to obtain the best solution by fluctuating inwards or outwards. The SSCA is implemented in the ORPD problem to find the best control variables to achieve minimum power loss and maximum net savings. Furthermore, the efficacy of SSCA is validated with other recently used algorithms considering three case studies, i.e., IEEE-30, -57, and -118 bus test system. The results show that the SSCA approach finds more precise and superior ORPD solutions. A comparison among SSCA and other methods proves the robustness of SSCA to attain the solution with faster convergence. The statistical analysis is performed to justify the effectiveness of SSCA by yielding minimum operating cost and maximum net savings as compared to other techniques considered in this study.

  • Transmission Congestion Management with FACTS Devices Using SOS Algorithm
    Khushboo Verma, S. K. Gupta, S. Kumar, and Gaurav Singh

    Springer Singapore

  • Harmonic Elimination of a T-Type Multilevel Inverter Based on Multistate Switching Cell
    Manoj Kumar Kar, Mohd Imam Hasan Mansoori, Sanjay Kumar, and S. K. Gupta

    Springer Singapore

  • Voltage Stability Enhancement Using Shunt Devices and Identification of Weak Bus through Voltage Stability Indices
    Lalit Kumar, Brijesh Kumar Raw, Sushil Kumar Gupta, and Sanjay Kumar

    IEEE
    Voltage Stability analysis is a very vital and challenging issue of the power system and maintains the stable bus voltage profile is the biggest problem. Here we applied Modal analysis, Line voltage stability indices, to study voltage collapse problem and find the weakest bus in the test bus system by using the PV curve. After investigating the weak bus, we have injected the shunt devices for maintaining the voltage profile. All parameter decided for voltage collapse by the PSAT MATLAB simulation. The modal analysis gives the Eigenvalue and Participation factor and these values help to find the weakest node in the system. Here see that smallest Eigenvalue gives the voltage collapse point and. Line Voltage stability Indices help to find the weakest line corresponding to the weakest bus. We proposed the comparative analysis of three line indices Line Stability Index, Fast Voltage Stability Index, and Line Stability or Quality Factor. The PV curve gives the voltage collapse point with and without injecting shunt devices in the test bus system.

  • The impact of economic and political events on performance of selected mutual funds of emerging economy: A systemic view


  • A study on impact of national and global events on performance of Indian mutual fund: Pre and post event analysis


RECENT SCHOLAR PUBLICATIONS

    INDUSTRY EXPERIENCE

    10 Years of Industry Experience in Asset Management and Financial Industry.