i-manager Publications

Volume 1 Issue 1 January - June 2023

Research Paper

Video Analytics for Optimizing Bank Services

Dhyey Valera* , Amit Vishwakarma, Ananya Paliwal*, Veena Kulkarni****

*-**** Department of Computer Engineering, Thakur College of Engineering & Technology, Mumbai, India.

Valera, D., Vishwakarma, A., Paliwal, A., and Kulkarni, V. (2023). Video Analytics for Optimizing Bank Services. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 1-8. https://doi.org/10.26634/jds.1.1.19480

Abstract

The use of Closed-Circuit Television (CCTV) footage for surveillance, demographic monitoring, and behavior analysis has increased dramatically in the recent years. One of the key challenges in analyzing such recordings is to count people and identify emotions efficiently and accurately. This paper proposed a system for counting people and identifying facial expressions of emotion in CCTV footage. It used Deep Neural Networks (DNN) trained on massive annotation datasets to recognize people in videos, extract facial images, and identify emotions. It evaluated the performance of the method on publicly available datasets and compares it with state-of-the-art techniques. The findings demonstrate the ability of the method to accurately count people and identify their emotions.

References

[1]. Cohen, C. J., Morelli, F., & Scott, K. A. (2008, May). A surveillance system for the recognition of intent within individuals and crowds. In 2008 IEEE Conference on Technologies for Homeland Security (pp. 559-565). IEEE.

[2]. Dumas, M. (2001). Emotional expression recognition using support vector machines. In Proceedings of International Conference on Multimodal Interfaces.

[3]. Halder, R., Sengupta, S., Pal, A., Ghosh, S., & Kundu, D. (2016). Real time facial emotion recognition based on image processing and machine learning. International Journal of Computer Applications, 139(11).

[4]. Kulkarni, V., & Talele, K. (2020, December). Video analytics for face detection and tracking. In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (pp. 962-965). IEEE.

[5]. Lasri, I., Solh, A. R., & El Belkacemi, M. (2019, October). Facial emotion recognition of students using convolutional neural network. In 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS) (pp. 1-6). IEEE.

[6]. Pervaiz, M., Ghadi, Y. Y., Gochoo, M., Jalal, A., Kamal, S., & Kim, D. S. (2021). A smart surveillance system for people counting and tracking using particle flow and modified SOM. Sustainability, 13(10), 5367.

[7]. Suhane, A. K., Vani, A., Parihar, H., Raghuwanshi, U., Nimbark, A., & Saxena, L. (2023). Human detection and crowd counting using yolo. In 2023 Tech Fest IIT Conference, Roorkee (pp. 1-7).

[8]. Wen, W., Ho, M., & Huang, C. (2008, July). People tracking and counting for applications in video surveillance system. In 2008 International Conference on Audio, Language and Image Processing (pp. 1677-1682). IEEE.

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Research Paper

A Comparative Analysis for Identifying the Polarity of People Based on Emotional Pulse in a Smart City

Reddi Kiran Kumar* , D. N. V. S. L. Indira, Sri Jyothi Goriparthi, L. B. Pranati, G. Keerthi

* Department of Computer Science, Krishna University, Machilipatnam, Andhra Pradesh, India.

-*** Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India.

Kumar, R. K., Indira, D. N. V. S. L., Goriparthi, S. J., Pranati, L. B., and Keerthi, G. (2023). A Comparative Analysis for Identifying the Polarity of People Based on Emotional Pulse in a Smart City. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 9-16. https://doi.org/10.26634/jds.1.1.19232

Abstract

Over the past decade, the field of industrial informatics has extensively studied smart-city applications. This paper proposes a method for identifying the polarity of people based on the emotional pulse of a city by using publicly available, large-scale social media interactions that capture individuals' ideas and beliefs. The effectiveness of the framework was assessed and controversial ideas and unpleasant arguments in the public sphere were tracked. The level of hostility was measured through online discussions using a Deep Learning (DL)-based classifier. Natural Language Processing (NLP) and Markov models have been used to characterize the mood of the general populace. The results and applications of Artificial Intelligence (AI) to identify citizens' emotional pulses could enhance security, communication, and policymaking.

References

[1]. Adikari, A., & Alahakoon, D. (2020). Understanding citizens' emotional pulse in a smart city using artificial intelligence. IEEE Transactions on Industrial Informatics, 17(4),2743-2751.

[2]. Balduini, M., Bocconi, S., Bozzon, A., Della Valle, E., Huang, Y., Oosterman, J., & Tsytsarau, M. (2014, October). A case study of active, continuous and predictive social media analytics for smart city. In S4SC@ISWC (pp. 31-46).

[3]. Behrens, M., Valkanova, N., gen. Schieck, A. F., & Brumby, D. P. (2014, June). Smart citizen sentiment dashboard: A case study into media architectural interfaces. In Proceedings of the International Symposium on Pervasive Displays (pp. 19-24).

[4]. Bruckner, D., Zeilinger, H., & Dietrich, D. (2011). Cognitive automation-Survey of novel artificial general intelligence methods for the automation of human technical environments. IEEE Transactions on Industrial Informatics, 8(2), 206-215.

[5]. Diaz-Aviles, E., Orellana-Rodriguez, C., & Nejdl, W. (2012, October). Taking the pulse of political emotions in Latin America based on social web streams. In 2012 Eighth Latin American Web Congress (pp. 40-47). IEEE.

[6]. Doran, D., Gokhale, S., & Dagnino, A. (2013, August). Human sensing for smart cities. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 1323-1330).

[7]. Granier, B., & Kudo, H. (2016). How are citizens involved in smart cities? Analysing citizen participation in Japanese “Smart Communities”. Information Polity, 21(1), 61-76.

[8]. Guthier, B., Alharthi, R., Abaalkhail, R., & El Saddik, A. (2014, November). Detection and visualization of emotions in an affect-aware city. In Proceedings of the 1st International Workshop on Emerging Multimedia Applications and Services for Smart Cities (pp. 23-28).

[9]. Li, M., Ch'ng, E., Chong, A., & See, S. (2016, July). The new eye of smart city: Novel citizen sentiment analysis in twitter. In 2016 International Conference on Audio, Language and Image Processing (ICALIP) (pp. 557-562). IEEE.

[10]. Milne, D., Paris, C., Christensen, H., Batterham, P., & O'Dea, B. (2015, August). We Feel: Taking the emotional pulse of the world. In Proceedings 19th Triennial Congress of the IEA, 9, 14.

[11]. Nam, T., & Pardo, T. A. (2011, June). Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times (pp. 282-291).

[12]. OECD. (2019). Enhancing the Contribution of Digitalisation to the Smart Cities of the Future.

[13]. Peng, W., Adikari, A., Alahakoon, D., & Gero, J. (2019). Discovering the influence of sarcasm in social media responses. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6), e1331.

[14]. Roza, V. C. C., & Postolache, O. A. (2016, July). Citizen emotion analysis in Smart City. In 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-6). IEEE.

[15]. Van Aken, B., Risch, J., Krestel, R., & Löser, A. (2018). Challenges for toxic comment classification: An in-depth error analysis. arXiv preprint arXiv:1809.07572.

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Research Paper

Influence of Digital Transformation and Artificial Intelligence in Business

Sentamilselvan K.* , Shivani K., Praseetha S., Vishal Sabari I., Shivani S.

*-***** Department of Management Studies, SRM Valliammai Engineering College, Kattankulathur, Kanchipuram, Tamil Nadu, India.

Sentamilselvan, K., Shivani, K., Praseetha, S., Sabari, I. V., and Shivani, S. (2023). Influence of Digital Transformation and Artificial Intelligence in Business. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 17-23. https://doi.org/10.26634/jds.1.1.19877

Abstract

Artificial Intelligence is an integral component that drives digital transformation, which involves leveraging digital technologies to reshape business operations, processes, and customer experience. AI-powered automation tools can help businesses streamline processes, reduce costs, and improve their efficiency. This paper discusses the influence of digital transformation and artificial intelligence in business in terms of efficiency, productivity, and customer satisfaction. It also highlights the challenges that businesses face when there is digital transformation for change management and the risk of disruption. Digital transformation is a complex and challenging process, but it can be a valuable tool for businesses that want to remain competitive in the digital age.

References

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[5]. Bart, Y., Shankar, V., Sultan, F., & Urban, G. L. (2005). Are the drivers and role of online trust the same for all web sites and consumers? A large-scale exploratory empirical study. Journal of Marketing, 69(4), 133-152.

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[7]. Casalo, L. V., Flavián, C., & Guinalíu, M. (2007). The influence of satisfaction, perceived reputation and trust on a consumer's commitment to a website. Journal of Marketing Communications, 13(1), 1-17.

[8]. Chatterjee, P., Hoffman, D. L., & Novak, T. P. (2003). Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science, 22(4), 520-541.

[9]. Choi, S. M., & Rifon, N. J. (2002). Antecedents and consequences of web advertising credibility: A study of consumer response to banner ads. Journal of interactive Advertising, 3(1), 12-24.

[10]. Constantinides, E. (2002). The 4S web-marketing mix model. Electronic Commerce Research and Applications, 1(1), 57-76.

[11]. Hjartar, K., Krishnakanthan, K., Prieto-Munoz, P., Shenai, G., & Kuiken, S. (2019). The CEO's New Technology Agenda.

[12]. McAfee, A., & Brynjolfsson, E. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. WW Norton & Company.

[13]. Ross, J. W., Beath, C. M., & Mocker, M. (2019). Designed for Digital: How to Architect Your Business for Sustained Success. MIT Press.

[14]. Siebel, T. M. (2019). Digital Transformation: Survive and Thrive in an Era of Mass Extinction. RosettaBooks.

[15]. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital: Turning Technology into Business Transformation. Harvard Business Press.

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Research Paper

A Study on Employee Perception towards Digital Marketing Services

Soundharya V.* , Sidharth D., Nithiesh Kumar V.*, M. Ganesan Kanagaraj****

*-**** Department of Management Studies, SRM Valliammai Engineering College, Kanchipuram, Tamil Nadu, India.

Soundharya, V., Sidharth, D., Kumar, V. N., and Kanagaraj, M. G. (2023). A Study on Employee Perception towards Digital Marketing Services. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 24-30. https://doi.org/10.26634/jds.1.1.19879

Abstract

This study focuses on investigating the effectiveness of new strategies in digital marketing, specifically at Technopundits Knowledge Solutions Private Limited. The success of digital marketing relies on a comprehensive strategy built upon seven core capabilities: strategic approach, performance improvement process, management buy-in, resourcing and structure, data and infrastructure, integrated customer communication, and customer experience. The research is confined to Technopundits employees, aiming to gauge their perceptions of the company's services and examine their impact on firm sales. To achieve these objectives, various statistical tools and techniques were used to analyze the collected data. Insights from this research have the potential to help Technopundits and similar companies to refine their digital marketing strategies, enhance their online presence, and meet customer expectations in an ever-evolving digital landscape. By understanding employee perceptions, this study aims to provide valuable guidance for leveraging digital marketing's full potential, to achieve greater success and competitiveness in the market. It is important to note that the scope of this research is limited to Technopundits and may not be directly applicable to other digital marketing services or industries.

References

[1]. Decker, R., & Stummer, C. (2017). Marketing management for consumer products in the era of the Internet of Things. Advances in Internet of Things, 7(3), 47-70.

[2]. Ghazali, E., Nguyen, B., Mutum, D. S., & Mohd-Any, A. A. (2016). Constructing online switching barriers: Examining the effects of switching costs and alternative attractiveness on e-store loyalty in online pure-play retailers. Electronic Markets, 26, 157-171.

[3]. Grubor, A., & Jakša, O. (2018). Internet marketing as a business necessity. Interdisciplinary Description of Complex Systems: INDECS, 16(2), 265-274.

[4]. Legner, C., Eymann, T., Hess, T., Matt, C., Böhmann, T., Drews, P., & Ahlemann, F. (2017). Digitalization: Opportunity and challenge for the business and information systems engineering community. Business & Information Systems Engineering, 59, 301-308.

[5]. Melović, B., Jocović, M., Dabić, M., Vulić, T. B., & Dudic, B. (2020). The impact of digital transformation and digital marketing on the brand promotion, positioning and electronic business in Montenegro. Technology in Society, 63, 101425.

[6]. Nirmala, G., Thirumurugan, G., & Jayasree, M. (2022, November). An analysis comparing traditional and digital marketing (Advertising) using chi square test and linear regression model. In 2022 1st International Conference on Computational Science and Technology (ICCST) (pp. 99-104). IEEE.

[7]. Spante, M., Hashemi, S. S., Lundin, M., & Algers, A. (2018). Digital competence and digital literacy in higher education research: Systematic review of concept use. Cogent Education, 5(1), 1519143.

[8]. Syazali, M., Putra, F., Rinaldi, A., Utami, L., Widayanti, W., Umam, R., & Jermsittiparsert, K. (2019). Retracted: Partial correlation analysis using multiple linear regression: Impact on business environment of digital marketing interest in the era of industrial revolution 4.0. Management Science Letters, 9(11), 1875-1886.

[9]. Yadagiri, M. M., Saini, S. K., & Sinha, R. (2015). A nonparametric approach to the multi-channel attribution problem. In Web Information Systems Engineering–WISE 2015: 16th International Conference (pp. 338-352). Springer International Publishing.

[10]. Yasmin, A., Tasneem, S., & Fatema, K. (2015). Effectiveness of digital marketing in the challenging age: An empirical study. International Journal of Management Science and Business Administration, 1(5), 69-80.

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Research Paper

A Study on Spending Patterns in the Digital Era with Special Reference to Tamilnadu

Bhavadharini P.*

Saveetha Institute of Medical and Technical Science (SIMATS), Chennai, Tamil Nadu, India.

Bhavadharini, P. (2023). A Study on Spending Patterns in the Digital Era with Special Reference to Tamilnadu. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 31-39. https://doi.org/10.26634/jds.1.1.19876

Abstract

This study highlights the impact of digital payments on spending patterns and financial behavior. A structured questionnaire was used to collect data from 200 participants. The results indicate that income, age, family status, and occupation played a significant role in financial behavior and priorities. The data also revealed that many respondents faced various financial challenges, such as insufficient money, debt payments, education expenses, healthcare costs, and meeting expenses. Digital payments and financial planning have been identified as potential solutions for managing these challenges and achieving financial goals. Financial awareness and education are needed, particularly for younger individuals, to improve their financial decision-making skills, investments, and stability.

References

[1]. Gurusamy, R. (2023). Digital payment technology and consumer behavior–Saving, spending patterns: Are saving and spending patterns a concern? Journal of Information Technology Teaching Cases, 204388692311 78846.

[2]. Ismail, N. A. (2011). The rising cost of living among B40 households in Penang state: Does lifestyle matter? Malaysian Journal of Consumer and Family Economics, 27, 25-43.

[3]. Jain, N., & Jain, V. (2022). The determinants of digital payment usage from consumers' perspective: A literature review. Apeejay Journal of Managemnet & Technology, 17(2), 50-61.

[4]. Latimaha, R., Bahari, Z., & Ismal, N. (2019). Middle income household spending patterns on housing in Malaysian state capital cities: Where does all the money go? Jurnal Ekonomi Malaysia, 53(2), 55-65.

[5]. Nchor, J. U. (2023). Livelihood strategies and their determinants among informal households in Calabar, Nigeria. Sustainability, 15(4), 2855.

[6]. Rafee, B. M., Ramesh, V., Asan, S. J., Basha, A., & Zaheed, K. S. M. (2022). A survey on implications of cashless payments on the spending patterns of urbanites in the era of digital India. International Journal of Early Childhood Special Education, 14(7), 2040-2048.

[7]. Sriram, M., & Vanithamani, M. R. (2020). A study on the usage of E-Payment system and its influence to digital financial inclusion in Coimbatore District, Tamil Nadu. Test Engineering and Management, 83, 2057-2066.

[8]. Vasudevan, U., Akkilagunta, S., & Kar, S. S. (2019). Household out-of-pocket expenditure on health care-A cross-sectional study among urban and rural households, Puducherry. Journal of Family Medicine and Primary Care, 8(7), 2278-2282.

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Review Paper

Artificial Intelligence in Investment Management, Asset Management and Warehouse Management

R. Karthika* , A. Anburaj, S. Raja*

- M.A.M B School, Tiruchirappalli, Tamil Nadu, India.

Department of Management Studies, SRM Valliammai Engineering College, Kanchipuram, Tamil Nadu, India.

Karthika, R., Anburaj, A., and Raja, S. (2023). Artificial Intelligence in Investment Management, Asset Management and Warehouse Management. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 40-46. https://doi.org/10.26634/jds.1.1.19874

Abstract

Artificial Intelligence (AI) has revolutionized diverse industries, including investment, asset, and warehouse management. In this analysis, the exploration of the applications and benefits of AI in each domain highlights how it enhances decision-making and optimizes operations efficiently. AI analyzes extensive financial data to make informed investment decisions, create personalized portfolios, and predict market trends in investment management. Asset Management leverages AI's predictive capabilities to monitor asset performance, assess risks, and optimize asset allocation strategies. AI tools can continuously monitor the environment and activities of a warehouse. This study explains AI and its impact on investment, asset, and warehouse management. The impact of AI is undeniable, and creates opportunities for better performance, reduced costs, and enhanced customer satisfaction.

References

[1]. Connolly, C. (2008). Warehouse management technologies. Sensor Review, 28(2), 108-114.

[2]. Golić, Z. (2019). Finance and artificial intelligence: The fifth industrial revolution and its impact on the financial sector. Zbornik Radova Ekonomskog Fakulteta u Istočnom Sarajevu, (19), 67-81.

[3]. Harman, M. (2012, June). The role of artificial intelligence in software engineering. In 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE) (pp. 1-6). IEEE.

[4]. Hu, Y., Kuang, W., Qin, Z., Li, K., Zhang, J., Gao, Y., ... & Li, K. (2021). Artificial intelligence security: Threats and countermeasures. ACM Computing Surveys (CSUR), 55(1), 1-36.

[5]. Ibrahim, A., Thiruvady, D., Schneider, J. G., & Abdelrazek, M. (2020). The challenges of leveraging threat intelligence to stop data breaches. Frontiers in Computer Science, 2, 36.

[6]. In, S. Y., Rook, D., & Monk, A. (2019). Integrating alternative data (also known as ESG data) in investment decision making. Global Economic Review, 48(3), 237-260.

[7]. Kruse, L., Wunderlich, N., & Beck, R. (2019). Artificial intelligence for the financial services industry: What challenges organizations to succeed. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 6408- 6417).

[8]. Kunwar, M. (2019). Artificial Intelligence in Finance: Understanding how Automation and Machine Learning is Transforming the Financial Industry (Master thesis, Centria University of Applied Sciences).

[9]. Lior, A. (2022). Insuring AI: The role of insurance in artificial intelligence regulation. Harvard Journal of Law and Technology, 35(2), 469-530.

[10]. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1-35.

[11]. Mestikou, M. A., Smeti, K. E., & Hachaichi, Y. (2023). Artificial intelligence and machine learning in financial services market developments and financial stability implications. Financial Stability Board (FSB).

[12]. Mitchell, R., Michalski, J., & Carbonell, T. (2013). An Artificial Intelligence Approach. Machine Learning. Springer, Berlin, Heidelberg.

[13]. Qiao, Q., & Beling, P. A. (2016). Decision analytics and machine learning in economic and financial systems. Environment Systems and Decisions, 36, 109-113.

[14]. Uykur, D. (2018, August). The New Physics of Financial Services-Understanding how Artificial Intelligence is Transforming the Financial EcosystemPart of the Future of Financial Services Series| Prepared In Collaboration with Deloitte. World Economic Forum, Switzerland.

[15]. Van Liebergen, B. (2017). Machine learning: A revolution in risk management and compliance? Journal of Financial Transformation, 45, 60-67.

i-manager's Journal on Data Science & Big Data Analytics (JDS)

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Volume 1 Issue 1 January - June 2023

Video Analytics for Optimizing Bank Services

Dhyey Valera* , Amit Vishwakarma**, Ananya Paliwal***, Veena Kulkarni****

*-**** Department of Computer Engineering, Thakur College of Engineering & Technology, Mumbai, India.

Valera, D., Vishwakarma, A., Paliwal, A., and Kulkarni, V. (2023). Video Analytics for Optimizing Bank Services. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 1-8. https://doi.org/10.26634/jds.1.1.19480

Abstract

References

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A Comparative Analysis for Identifying the Polarity of People Based on Emotional Pulse in a Smart City

Reddi Kiran Kumar* , D. N. V. S. L. Indira**, Sri Jyothi Goriparthi***, L. B. Pranati****, G. Keerthi*****

* Department of Computer Science, Krishna University, Machilipatnam, Andhra Pradesh, India. **-***** Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India.

Abstract

References

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Influence of Digital Transformation and Artificial Intelligence in Business

Sentamilselvan K.* , Shivani K.**, Praseetha S.***, Vishal Sabari I.****, Shivani S.*****

*-***** Department of Management Studies, SRM Valliammai Engineering College, Kattankulathur, Kanchipuram, Tamil Nadu, India.

Sentamilselvan, K., Shivani, K., Praseetha, S., Sabari, I. V., and Shivani, S. (2023). Influence of Digital Transformation and Artificial Intelligence in Business. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 17-23. https://doi.org/10.26634/jds.1.1.19877

Abstract

References

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A Study on Employee Perception towards Digital Marketing Services

Soundharya V.* , Sidharth D.**, Nithiesh Kumar V.***, M. Ganesan Kanagaraj****

*-**** Department of Management Studies, SRM Valliammai Engineering College, Kanchipuram, Tamil Nadu, India.

Soundharya, V., Sidharth, D., Kumar, V. N., and Kanagaraj, M. G. (2023). A Study on Employee Perception towards Digital Marketing Services. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 24-30. https://doi.org/10.26634/jds.1.1.19879

Abstract

References

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A Study on Spending Patterns in the Digital Era with Special Reference to Tamilnadu

Bhavadharini P.*

Saveetha Institute of Medical and Technical Science (SIMATS), Chennai, Tamil Nadu, India.

Bhavadharini, P. (2023). A Study on Spending Patterns in the Digital Era with Special Reference to Tamilnadu. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 31-39. https://doi.org/10.26634/jds.1.1.19876

Abstract

References

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Artificial Intelligence in Investment Management, Asset Management and Warehouse Management

R. Karthika* , A. Anburaj**, S. Raja***

*-** M.A.M B School, Tiruchirappalli, Tamil Nadu, India. *** Department of Management Studies, SRM Valliammai Engineering College, Kanchipuram, Tamil Nadu, India.

Karthika, R., Anburaj, A., and Raja, S. (2023). Artificial Intelligence in Investment Management, Asset Management and Warehouse Management. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 40-46. https://doi.org/10.26634/jds.1.1.19874

Abstract

References

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Dhyey Valera* , Amit Vishwakarma, Ananya Paliwal*, Veena Kulkarni****

Reddi Kiran Kumar* , D. N. V. S. L. Indira, Sri Jyothi Goriparthi, L. B. Pranati, G. Keerthi

* Department of Computer Science, Krishna University, Machilipatnam, Andhra Pradesh, India.

-*** Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India.

Sentamilselvan K.* , Shivani K., Praseetha S., Vishal Sabari I., Shivani S.

Soundharya V.* , Sidharth D., Nithiesh Kumar V.*, M. Ganesan Kanagaraj****

R. Karthika* , A. Anburaj, S. Raja*

- M.A.M B School, Tiruchirappalli, Tamil Nadu, India.

Department of Management Studies, SRM Valliammai Engineering College, Kanchipuram, Tamil Nadu, India.