B. Sharmila

Work place: PG and Research Department of Commerce, Arulmigu Palaniandavar Arts College for Women, Palani 624615, India

E-mail:

Website: https://orcid.org/0009-0001-1506-6809

Research Interests:

Biography

Sharmila B. received her Ph.D. in Commerce in the year 2020 from Bharathiar University and M.Phil. degree in Commerce from Madurai Kamaraj University in the year 2005. She has recognized as a Research Guide for M.Phil in Bharathiar University and she has serviced more than 14 years of teaching experience in reputed colleges. She has attended 1 month orientation course conducted by Bharathiar University in the year 2006. She has attended and presented 43 papers in National Seminars, National and International Conferences. She has attended 13 FDP programs in National level. She has published 03 articles in International Peer Reviewed Journals and UGC Care Listed journals. She has published 6 Book Chapters which are relevant to current theme. She acted as a Board of studies Member and Academic Audit Member in NGM College, Pollachi. She acted as Commerce Association In-charge.

Author Articles
Optimizing Credit Risk Assessment in Banking Human Resource Management: A Enhanced Humboldt Squid based Probabilistic Spiking Neural Networks with Shunted Self-Attention

By R. Sangeetha S. Sathish Kumar B. Sharmila P. Dency Mary

DOI: https://doi.org/10.5815/ijieeb.2025.03.02, Pub. Date: 8 Jun. 2025

The movement of capital, integration, distribution, and social supply and demand adjustment are all greatly aided by commercial banks; yet, integrating credit risk assessment is a difficult task for banking Human Resource Management (HRM). To overcome these issues, a novel credit risk assessment in HRM frameworks is done using the Enhanced Humboldt Squid based Probabilistic Spiking Neural Networks with Shunted self-attention (EHSPNN-SSA) method is proposed. Initially, the input commercial bank datasets are taken from General Data Protection Regulation (GDPR) and Advanced Analytics of Credit Registry (AACR) Datasets. Then these data are pre-processes using Grid-Restricted Data Filtering Approach (GRDFA). After that, the data is extracted using Hybrid Absolute deviation factors (ADFs) class document frequency (CDF) (hyb ADF-CDF) based feature extraction method. Then these data are classified using Enhanced Probabilistic Spiking Neural Networks with Shunted self-attention (EPSNN-SSA) and optimized using the Humboldt Squid Optimization Algorithm (HSOA). The framework is validated using real-world banking data and compared to existing methods to demonstrate its efficacy in assessing credit risk and optimizing human resource management processes. The results show that the introduced approach performs better than previous approaches in a number of performance measures, including risky data accuracy (99.6%), non-risky data accuracy (99.7%), and risky data accuracy (99.4%) for dataset 1 and dataset 2, respectively. This indicates the method's exceptional effectiveness and room for advancement in the field.

[...] Read more.
Other Articles