Dr. Nalla Bala Kalyan
@svce.edu.in
Professor, Department of Management Studies , Sri Venkateswara College of Engineering, (Autonomous), Tirupati-517 507
Currently, Dr. Nalla Bala Kalyan is working as an Associate Professor in the Department of Management Studies at Sri Venkateswara College of Engineering, Tirupati, Andhra Pradesh, India. He holds a Doctorate in Finance from Sri Venkateswara University, Tirupati in the year 2014. He has 12 years of experience in research and teaching. Right now he is serving as a Ph.D. Supervisor of Jawaharlal Nehru Technological University, Anantapur. He is the author of more than 70 research papers published in various national & international journals with high Impact factors and citations, Top 10% of global authors of SSRN-Elsevier. He has attended more than 25 National and International conferences/seminars and presented papers that appeared in the proceedings (Book Chapters) published with ISBN and he has Authored & Edited 9 books. He has posted more than 15 guest articles in Magazines and Blogs.
RESEARCH, TEACHING, or OTHER INTERESTS
Multidisciplinary, Organizational Behavior and Human Resource Management, Business and International Management, Finance
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Rajyalaxmi M, C. Vijai, Kingshuk Srivastava, NallaBala Kalyan, B. Pravallika and Amit Dutt
IEEE
Client segmentation is an essential component of e-commerce operations since it enables more effective marketing and a more satisfying experience for customers. Within the context of e-commerce platforms, this research investigates the functioning of machine literacy algorithms for effective client segmentation. Through the utilization of clustering methods and predictive models, such as k-means clustering, hierarchical clustering, and decision trees, our objective is to categorize guests according to their shopping behavior, demographics, and preferences. In addition, we investigate the incorporation of sophisticated machine literacy methods, such as ensemble styles and deep literacy infrastructures, to improve the delicateness and resilience of client segmentation models. We illustrate the efficacy of machine learningusing empirical evaluation and case studies. This enables us to relate different client components and adapt marketing strategies to fit the individual requirements of each client. In the context of e-commerce enterprises, our findings highlight the value of machine literacy-driven client segmentation in terms of optimizing marketing sweats, enhancing client retention, and maximizing profit.
C.Balarama Krishna, Meeta Joshi, K. Sathesh Kumar, Nalla Bala Kalyan, Shivani Bhardwaj, and Punamkumar Hinge
IEEE
The strategic alignment between organisational objectives and human resource management is stronger in contemporary organizations. As deep learning methods and machine learning solutions play a larger role in managing human resource management operations, organizations are focusing on more applicable sets of solutions. Models based on machine learning are now making progress in a variety of HRM-related fields. Machine learning is being used in human resource management to anticipate who will remain and who will depart the company, as well as to gauge workers' interest in their specific organisation.Data scraping methods are used to extract the data, which is then saved in CSV format. With the aid of ML algorithms, the many characteristics in the data acquired using this method may be used to make predictions. The management may develop a strategy to keep a deserving person in the organization by using the analysis to draw conclusions about who will remain or depart the company.We used a variety of methods in our investigation, including feature scaling and SMOTE. The recommended techniques, such as random forest and XG boost classifier, are supported by the findings. We'll arrive to a judgment based on the accuracy rate (%) numbers for the results generated by the offered approaches.
Kafila, Nalla Bala Kalyan, Kamal Ahmad, Fakhruddin Rahi, Chetan Shelke, and S Mahabub Basha
IEEE
The incorporation of the Internet of Things (IoT) and machine learning (ML) methods has attracted significant interest in a variety of sectors in previous years. This article investigates the use of IoT and ML to improve supply chain financial (SCF) threat management. The supply chain is a complicated network with many key players, and financial threat management is important to its sustainability and success. IoT and ML possess inherent advantages in SCF due to their technology properties. They also have significant opportunities to build trust to solve big challenges in SCF, which aids financial development in the Tonkin Gulf area. This paper focuses on introducing the study on using Machine learning innovation in SCF in the Tonkin Gulf area and aims to offer suggestions on how supply chain finance could evolve there using Machine learning. This paper suggests supply chain finance game applications for pertinent investigations as well as blockchain innovation, supply chain banking threats assessment on the IoT and machine learning, and supply chain finance implementation study methodologies in the Tonkin Gulf region.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
1. Title of the Invention “Management System for Business Rules and Methods Thereof”, Application Number-202241038272, Issue No. 27/2022, Publication Date: 08/07/2022
2. Title of the Invention “A System for Developing Continuous Performance and Method of Operating the Same”, Application Number- 202341045191, Publication Date: 01/09/2023
3. Title of the Invention “Flash Light Enabled Laptop with a Storage Compartment”, Application Number: 388510-001, Design Accepted and Published, Journal No is 37/2023 and Journal Date is 15/09/2023
GRANT DETAILS
1. Organized “Three Day Entrepreneurship Awareness Camp,” Funded by EDI/DST-NIMAT Project 2016 &17, Held on 20 to 22 September 2016.
2. Organized “Three Day Entrepreneurship Awareness Camp,” Funded by EDI/DST-NIMAT Project 2018 &19, Held on 8-10 of August 2018.
CONSULTANCY
1.Organized DST-NIMAT Project "Facilitation Workshop," on 19th, December 2018, Sponsored by NSTEDB, DST, Govt. of India – Program Convener
2.Organized SAMBHAV "E-National Level Awareness Program," (NLAP) On Entrepreneurship, Sponsored by NI-MSME (Ministry of MSME, Govt. of India).
3.Organized "IPR awareness/training program," under the “National Intellectual Property Awareness Mission (NIPAM)” at Sri Venkateswara College of Engineering, Tirupati on 01/03/2023.
4.Organized National Webinar on Financial Empowerment and Career Opportunities in the Financial and Securities Market, Collaboration with Association of Mutual Funds in India (AMFI), held on 12-06-2023
5.Organized National Webinar on “Current Trends in Capital Markets and Due Diligence in Investing in Securities Markets” held on 12-07-2023, in Collaboration with Central Depository Services Limited (CDSL), Mumbai
6.Organized National Webinar on “A Five Days Online Training Program on Overview of Capital Market” Collaboration with Bombay Stock Exchange Brokers' Forum (BBF) Mumbai, Maharashtra, India, held on 4th December to 8th December 2023.
7.Organized National Webinar on “Demystifying Intellectual Property for Academic Research & Innovation” on 7th -September, 2023 at 10.30 am, Collaboration with National Research Development Corporation (NRDC) (An Enterprise of DSIR, Ministry of Science & Technology, Govt. of India)
SOCIAL, ECONOMIC, or ACADEMIC BENEFITS
Memberships:
1.Lifetime Member of (MTC GLOBAL) Management Teachers Consortium-Global, Bangalore, Karnataka, Indian, Registration No: MTCG/M/L2019/05/14, Registered with NITI Aayog, Government of India
2.Fellow Member of (SASS) Scholars Academic & Scientific Society, Assam, India, Registered under Society Registration Act XXI of 1860, India, Fellow ship No: SAS/FSASS-45
3.Fellow Member of (IARA) Indian Academic Researchers Association, India, Fellowship No: F126/2018
4.Lifetime Member of (InSc) Institute of Scholars, Membership ID: INSC20180344, Bangalore, India
5.Fellow Member of (ISROSET) International Scientific Research Organization for Science, Engineering and Technology, Fellowship No: ISROSET-FM-1063
6.Eminent Fellow Member of Scholars Academic and Scientific Society (SASS), Assam- 782439, India, Membership ID: SAS/SEFM/001/2021