General Business, Management and Accounting, Business, Management and Accounting, Business and International Management, Strategy and Management
7
Scopus Publications
1144
Scholar Citations
20
Scholar h-index
26
Scholar i10-index
Scopus Publications
An Empirical Study on Implementation of AI & ML in Stock Market Prediction Dr.N. Venkatarathnam, Dr. Laxmana Rao Goranta, P.C. Kiran, Dr.B.P.G. Raju, Dr. Samadhi Dilli, et al. Indian Journal of Information Sources and Services, 2024 The introduction of Artificial Intelligence (AI) and Machine Learning (ML) has transformed numerous fields, including agriculture, industry, economy, and medicine, with significant advancements in automation and decision-making processes. Today, AI and ML have also made notable strides in financial markets, particularly in stock and foreign exchange (Forex) forecasting, where complex algorithms are used to predict market movements and assist in decision-making. This paper examines such applications, particularly on using AI and ML techniques in the stock trading and market prediction. Specifically for this paper, the general approach to the application of AI and more concretely the ML in the trading of stocks is examined in terms of learning processes and the algorithms which are used to make predictions. The paper examines data extraction techniques, which are crucial for identifying such patterns as historical stock prices and volumes of trading. These patterns are utilized in the determination of the future market tendencies hence of more utility in exploring the elusive tendencies of the financial markets. It will also be seen that the usage of deep learning models, as well as neural networks, is very helpful in discovering as well as addressing these patterns. A considerable part of the study is devoted to the presentation of AI-based models implemented in R programming for stock price prediction. Two primary models are examined: the Artificial Neural Network (ANN) and the time series model by employing Auto-Regressive Integrated Moving Average (ARIMA). Another kind of deep learning model is known as the ANN which is a kind of artificial neural network or a computer model of the brain derived after researching the way the human brain processes information, which efficiently learns and identifies patterns in large data presumed to make future predictions on these patterns. On the other hand, the ARIMA is a model developed to handle time series data, because through the exploration of the data used in the analysis when developing a model for the estimation of the future stock price. Thus, the use of ANN and ARIMA models presents a complete solution for forecasting the stock market. Indeed, the use of the ANN model to analyze data demonstrates its strength in finding patterns that could not be easily discernable using standard approaches, in contrast, the ARIMA model is the most effective for short-term forecasting utilizing trends that have already been observed and set. Taken together, this study seeks to improve the reliability of the models used in predicting stock price fluctuations and to contribute to effective investment decision-making. Last but not least, this study intends to raise awareness about how AI and ML perform better in stock market business to negotiate the challenges that are related to market volatility and the unpredictability of data. The realizations produced through these models not only equip the investors with a strategic guide on where to invest but also offer a more technical and rational means of decision-making compared to applying the 'gut feel' in the financial markets. Whereas R programming makes it easier to apply both the ANN and the ARIMA models, the research shows how AI and ML can be employed to control for risks and maximize returns and thus raise the efficiency of the trading models. This research adds to the current knowledge on the trends of applying AI and ML to financial markets since the technology has massive potential to provide additional and advanced tools for traders and investors.
Maximizing Marketing Value: An Empirical Study on the Framework for Assessing AI and ML Integration in Marketing Management Dr.V. Jalaja, Dr. Thejasvi Sheshadri, Dr.V.K. Arthi, Dr.S. Thilaga, Dr.J. Bamini, et al. Indian Journal of Information Sources and Services, 2024 To address this issue, we conduct an empirical investigation into the application of AI and ML in marketing management using a rich framework that aims to optimize marketing value as much as possible. The framework is structured in four pillars: data gathering and processing, customer insights & segmentation, personalized marketing strategies and performance improvement. The utility and practical implementation of the framework was examined through a mixed-methods study design employing quantitative surveys validated by qualitative interviews. The survey was conducted among marketing practitioners across all industries. We also analyze the extent of AI and ML integration in each component empirically, with qualitative insights providing perspectives on opportunity areas, challenges and best practices. Three of these areas in the framework organizational readiness, resource allocations and skill gaps are highlighted as those with most pronounced differences on AI & ML deployment. To learn more about the dataset, demographics of 38 respondents in sample. The sample has a diversified age distribution and it ranges from 27 to 50 years of age. This demonstrates a significant bit of variation (SD = 7.30) and an average age of around 36-37 years Because there's also diversity when it comes to gender representation, with seven people identifying as "Other", besides the six male and fifteen women. According to the statistics, most people are bachelor’s degree holders although also a lot of masters and doctorate degrees which adds depth into information. Overall, the results provide a glimpse into demographic attributes regarding marketing performance and providing useful information for strategic decision making by doing so on behalf of management in charge with implementing those decisions. Finally, this research provides actual data rather than theory on how AI and ML mechanics are being integrated in the marketing management space. The strategic deployment of AI and ML in this research can significantly improve the efficient utilization of resources, maximize marketing efficiency by displaying key parameters impacting shopping behaviour as possesses with more long standing competitive benefits. When they can lean on hard data, researchers, marketers and decision-makers alike are likely to find a more straightforward path to the tasks that matter most as well as how AI/ML factors into driving real marketing value. The research also underscores the importance of ongoing learning, adapting and aligning strategies to leverage new technology in an ever-evolving field like marketing.
Improving Mutual Fund Performance Analysis through the Fusion of CNN-LSTM and Explainable AI Techniques Raju B P G, Navya Francis, N.Venkatarathnam, Kavita Mahar, Manyam Kethan, et al. 2024 3rd International Conference on Electrical Electronics Information and Communication Technologies Iceeict 2024, 2024 Analysis of mutual fund performance is crucial for fund managers and investors to make wise choices. Predictions made using traditional approaches are frequently not as accurate since they are unable to identify intricate patterns in financial data. Convolutional neural networks (CNN) and long short-term memory (LSTM) networks are two examples of deep learning approaches that provide promising ways to improve the predictive accuracy of financial forecasting jobs. Incorporating explainable AI techniques can also help with risk management and decision-making by offering insights into the fundamental causes influencing mutual fund performance. By utilizing the combined strength of explainable AI techniques and CNN-LSTM architecture, this work seeks to improve mutual fund performance analysis. The aim is to create a strong framework that can forecast mutual fund performance with accuracy and offer comprehensible explanations of the underlying elements. This work is interesting because it combines explainable AI methods which are particularly useful for analyzing mutual fund performance with CNN-LSTM architecture. In this work, the dual challenges of prediction accuracy and model transparency in financial forecasting are addressed by integrating deep learning with interpretability. The proposed framework for mutual fund performance analysis uses historical data, CNN-LSTM architecture, and explainable AI methods. The model outperforms traditional methods, achieving higher predictive accuracy and providing actionable insights into fund performance drivers. The model's interpretability enhances trustworthiness and utility for investors and fund managers, empowering stakeholders with better decision-making in dynamic financial markets.
Ethical and Legal Implications of AI on Business and Employment: Privacy, Bias, and Accountability K. Saketh Reddy, Manyam Kethan, S Mahabub Basha, Arti Singh, Praveen Kumar, et al. 2024 International Conference on Knowledge Engineering and Communication Systems Ickecs 2024, 2024 The proliferation of Artificial Intelligence (AI) in business and employment contexts necessitates a critical examination of the ethical and legal implications surrounding privacy, bias, and accountability. As AI systems become integral to decision-making processes, concerns about data privacy violations and algorithmic biases have heightened. This paper delves into these challenges, presenting a comprehensive framework to address the ethical and legal intricacies associated with AI deployment. Drawing on a thorough literature survey, we identify the gaps in current practices and propose a multifaceted approach to mitigate privacy infringements, combat bias, and establish accountability mechanisms. Our methodology combines quantitative and qualitative analyses, examining existing AI systems to gauge their impact on privacy and bias. The proposed implementation model integrates advanced encryption for privacy preservation, bias-detection algorithms for algorithmic fairness, and transparent decision-making processes to enhance accountability. The results showcase significant advancements in each domain, providing a foundation for responsible AI deployment in business and employment. This study contributes to the ongoing discourse on ethical AI by offering practical solutions to the evolving challenges, ultimately promoting a harmonious integration of AI technologies that align with societal values and legal standards.
Revolutions of Blockchain Technology in the Field of Cryptocurrencies Mahabub Basha S., Manyam Kethan, Venkateswarlu Karumuri, Shouvik Kumar Guha, Anita Gehlot, et al. Proceedings of the 2022 11th International Conference on System Modeling and Advancement in Research Trends Smart 2022, 2022 A ledger may be thought of as a group of documents or perhaps an accessible document that would be accessible by persons involved. Every trade which is integrated first goes through user verification. Data that has been saved by bitcoin cannot ever be erased or modified. As a result, the bitcoin may be thought as a log of all transactions processed. Blockchains is also used by cryptocurrency like the decentralized Ethereum and bitcoin, which is also known as peer-to-peer digital money. This study examines the development of currency, some few creative critiques, the operation of chain, and its use.
RECENT SCHOLAR PUBLICATIONS
Portfolio Optimization Using Sharpe, Treynor, and Jensen Measures: Evidence from IT Sector Stocks M Kethan, T Bastray, MK Mahar Restitution Law Review 34 (1), 200-216 , 2026 2026
Financial Risk Tolerance Among Portfolio Investors: Evidence from Bengaluru, India M Kethan, SR Jaladi, P Chandran, K Mahar International Insurance Law Review 33 (S5), 789-803 , 2025 2025
Retail Marketing in the Machine Learning Era: Opportunities, Challenges, and Future Trends M Kethan, KV Varma, GS Kumari, R Bhatnagar, T Sheshadri International Insurance Law Review 33 (S4), 403-415 , 2025 2025
AI-driven personalization: Advancements in dynamic pricing and recommendation systems S Nakirikanti, M Kethan, S Sharma, A Gupta World Journal of Advanced Research and Reviews 26 (1), 1778-1791 , 2025 2025 Citations: 3
A Study on Digital Marketing and Its Impact on Consumer Behaviour in the Retail Sector with reference to Bangalore city DJNA Mr. Manjunatha Naik, Dr. I G Srikanth, Dr. M. Kethan, Kavita Mahar Advances in Consumer Research 2 (4), 346-354 , 2025 2025
Retail Marketing in the Machine Learning Era: Opportunities, Challenges, and Future Trends DTS Dr. M. Kethan, Kanumuri Vinod Varma, Dr. Gondesi Santhoshi Kumari, Dr ... International Insurance Law Review 33 (Special Issue), 404-415 , 2025 2025
An empirical study on implementation of AI & ML in stock market prediction N Venkatarathnam, LR Goranta, PC Kiran, BPG Raju, S Dilli, SM Basha, ... Indian Journal of Information Sources and Services 14 (4), 165-174 , 2024 2024 Citations: 23
10. Development of Technological Advancement, Innovation and Machine Learning and their Influence on Entrepreneurship Business M Kethan, I Liudmyla Recent Advances in Management and Engineering: Proceedings ofRecent Advances … , 2024 2024
Development of Technological Advancement, Innovation and Machine Learning and their Influence on Entrepreneurship Business K Selvasundaram, R Yadav, A Aruna, BA Dar, G Manoharan, M Kethan, ... Recent Advances in Management and Engineering, 58-64 , 2024 2024
Improving Mutual Fund Performance Analysis through the Fusion of CNN-LSTM and Explainable AI Techniques BPG Raju, N Francis, N Venkatarathnam, K Mahar, M Kethan, II Raj 2024 Third International Conference on Electrical, Electronics, Information … , 2024 2024 Citations: 2
Sustainable Parking Innovations: Enhancing Security with IoT and Facial Image Recognition M Deenakonda, VV Vijetha Inti, BNCV Chakravarthi, C Rajyalakshmi, ... IOP Conference Series: Earth and Environmental Science 1375 (1), 012031 , 2024 2024 Citations: 1
Ethical and legal implications of AI on business and employment: privacy, bias, and accountability KS Reddy, M Kethan, SM Basha, A Singh, P Kumar, D Ashalatha 2024 international conference on knowledge engineering and communication … , 2024 2024 Citations: 35
Maximizing marketing value: An empirical study on the framework for assessing AI and ML integration in marketing management V Jalaja, T Sheshadri, VK Arthi, S Thilaga, J Bamini, S Mahabub Basha, ... Indian Journal of Information Sources and Services 14 (3), 64-70 , 2024 2024 Citations: 18
EMERGING BUSINESS PARADIGMS TRANSITION FROM INDUSTRY 4.0 TO INDUSTRY 5.0 IN INDIA DTJ Mr. Mahabub Basha S, Dr. M. Kethan CAHIERS MAGELLANES-NS 6 (Issue 2), 629-632 , 2024 2024
A STUDY ON CONSUMER PERCEPTION TOWARDS FAST FOOD RETAIL OUTLETS WITH REFERENCE TO BENGALURU KARNATAKA. I Gunday, M Kethan Journal of Pharmaceutical Negative Results 14 (3) , 2023 2023 Citations: 27
Banking Reforms in India: Public Sector Banks DMK Dr.A.M.Mahaboob Basha IN Patent App. 202,341,003,934 , 2023 2023
Banking Reforms in India: Public Sector Banks DMK Dr A M Mahaboob Basha IN Patent 2 , 2023 2023
A Study of Emotional Intelligence and Work-Life Balance of Employees in the IT Industry in Bangalore M Kethan, G Sahu, K Mahar, M Khizerulla International Journal of Management and Development Studies 12 (10), 1-7 , 2023 2023 Citations: 2
THE EMERGENCE OF THE FINTECH MARKET: OPPORTUNITIES AND CHALLENGES LK Kumar, M Kethan Journal of Research Administration 5 (2), 9445-9456 , 2023 2023 Citations: 11
CAPSULE FOR HOUSING ELECTRO-MEDICAL EQIUPMENT FOR RADIO DAIGNOSIS DM Dr.Gerad Deepak 2023
MOST CITED SCHOLAR PUBLICATIONS
Indian banking industry: Challenges and opportunities KA Goyal, V Joshi International Journal of Business Research and Management 3 (1), 18-28 , 2012 2012 Citations: 151
Financial literacy and investment behaviour of IT professional in India MB Shaik, M Kethan, T Jaggaiah, M Khizerulla East Asian Journal of Multidisciplinary Research 1 (5), 777-788 , 2022 2022 Citations: 131
An Empirical Study On Socioeconomic Factors Affecting Producer's Participation In Commodity Markets In India. DK Agrawal, M Brinda, A Singh, S Hemalatha, M Chandrakala, M Kethan Journal of Positive School Psychology 6 (5) , 2022 2022 Citations: 77
Relationship of ethical sales behaviour with customer loyalty, trust and commitment: a study with special reference to retail store in Mysore city M Kethan, M Basha East Asian Journal of Multidisciplinary Research 1 (7), 1365-1376 , 2022 2022 Citations: 75
A study of Emotional Intelligence and Quality of Life among Doctors in PandemicCovid 19 V DrSanthosh Kumar, SM Basha International Journal of Early Childhood 14 (02), 2080-2090 , 2022 2022 Citations: 73
WHICH DETERMINANTS MATTER FOR CAPITAL STRUCTURE? AN EMPIRICAL STUDY ON NBFC'S IN INDIA MB Shaik, M Kethan, I Rani, U Mahesh, CS Harsha, MK Navya, D Sravani International Journal of Entrepreneurship 26, 1-9 , 2022 2022 Citations: 69
Financial literacy and investment behaviour of IT professional with reference to Bangalore city MB Shaik, M Kethan, T Jaggaiah Ilomata International Journal of Management 3 (3), 353-362 , 2022 2022 Citations: 56
Covid-19 pandemic and the digital revolution in academia and higher education: an empirical study SM Basha, M Kethan Eduvest-Journal of Universal Studies 2 (8), 1.648-1.656 , 2022 2022 Citations: 48
A Study on Digital Marketing Tools amongst the Marketing Professionals in Bangalore City S. Mahabub Basha ,Dr. Kethan M A Journal Of Composition Theory 14 (9), 17-24 , 2021 2021 Citations: 40
Ethical and legal implications of AI on business and employment: privacy, bias, and accountability KS Reddy, M Kethan, SM Basha, A Singh, P Kumar, D Ashalatha 2024 international conference on knowledge engineering and communication … , 2024 2024 Citations: 35
Service quality in SBI: An assessment of customer satisfaction on e-banking services D Rajasulochana, M Khizerulla Journal of Positive School Psychology 6 (6), 4585-4590 , 2022 2022 Citations: 33
An empirical study on customer satisfaction towards organized Retail outlets in Bengaluru city, Karnataka GS Kumarai, PK Bajaj, SS Rana, M Basha, V Karumuri Academy of Marketing Studies Journal 26 (5) , 2022 2022 Citations: 31
Impact of Indian cinema on youths lifestyle and behavior patterns M Kethan, M Basha East Asian Journal of Multidisciplinary Research 2 (1), 27-42 , 2023 2023 Citations: 28
A STUDY ON CONSUMER PERCEPTION TOWARDS FAST FOOD RETAIL OUTLETS WITH REFERENCE TO BENGALURU KARNATAKA. I Gunday, M Kethan Journal of Pharmaceutical Negative Results 14 (3) , 2023 2023 Citations: 27
A study on issues and challenges on production of handloom sector with special reference to rayalaseema and costal region of Andhra Pradesh M Kethan, M Khizerulla, SC Sekhar, M Basha IJAR 8 (6), 89-95 , 2022 2022 Citations: 27
Effectiveness and efficiency of E-governance in Andhra Pradesh M Prakash, K Manyam International Journal of Advanced Scientific Research & Development 5 (01) , 2018 2018 Citations: 25
A STUDY ON COMPARATIVE FINANCIAL STATEMENT OF HATSUN AGRO PRODUCT LTD (WITH REFERENCE LAST FIVE FINANCIAL YEAR 2013 TO 2017) BSRM M.KETHAN IJRASET,International Journal for Science and Advance Research In Technology … , 2018 2018 Citations: 25
An empirical study on implementation of AI & ML in stock market prediction N Venkatarathnam, LR Goranta, PC Kiran, BPG Raju, S Dilli, SM Basha, ... Indian Journal of Information Sources and Services 14 (4), 165-174 , 2024 2024 Citations: 23
Predicting bankruptcy of heritage foods company by applying Altman’s Z-score model BSR Murthy, K Manyam, K Sravanth, M Ravikumar International journal of innovative research in technology (IJIRT) 4 (12) , 2018 2018 Citations: 23
Revolutions of Blockchain Technology in the Field of Cryptocurrencies M Basha, M Kethan, V Karumuri, SK Guha, A Gehlot, D Gangodkar 2022 11th International Conference on System Modeling & Advancement in … , 2022 2022 Citations: 20
