Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Polyhedron International Journal in Mathematics Education Advance Sustainable Science, Engineering and Technology (ASSET)
Alok Singh Chauhan
Galgotias University
Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
ECCFD-GNN: A Novel Risk-Sensitive Graph Neural Network Model for Fraudulent Transaction Detection Shilpa Srivastava; Varuna Gupta; Alok Singh Chauhan; Sakshi Kumar; Sonia Rani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3393

Abstract

The study presents the integration of machine learning techniques for detecting the credit card fraud. Its integration maintains a behavioral profile of cardholders and other parameters like location, frequency, amount etc. resulting in the timely detection of any anomaly from the normal behavior. A novel approach ECCFD-GNN (Enhanced Credit Card Fraud Detection based on Graph Neural networks) is proposed enhancing the performance of fraud detection. The various behavioral indicators taken into consideration are number of months with late payments, the frequency of low payments and the length of the account, which are further combined to a newly introduced feature “risk score”. The purpose of risk score is to increase the model’s sensitivity to the transactions having complex fraud risks. The approach uses three Graph Neural Network architectures namely KNN graph GNN, Radius Graph GNN and Feature Correlation GNN. The experiment is performed with both the optimizers Adam and RAdam. With Adam optimizers the results show that KNN graph GNN provides better performance when compared on the basis of different evaluation parameters with accuracy 85%, precision 76% recall 70% and F1-score as 73%.  The results are improved when tested with RAdam optimizers leading to increased accuracy, precision, recall and F1 score.
Ways of thinking 3D geometry: exploratory case study in junior high school students Sudirman Sudirman; Camilo Andrés Rodríguez-Nieto; Zwelithini Bongani Dhlamini; Alok Singh Chauhan; Umida Baltaeva; Abdulhalim Abubakar; Jenisus O. Dejarlo; Mela Andriani
Polyhedron International Journal in Mathematics Education Vol. 1 No. 1 (2023): pijme
Publisher : Nashir Al-Kutub Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59965/pijme.v1i1.5

Abstract

Each student has their own characteristics and way of doing 3D geometric thinking. The way of thinking that students do influences the resulting understanding of the concept of 3D geometry. Therefore, this study aims to investigate students' geometric thinking based on the level of achievement of students in completing the 3D geometric thinking ability test (3D GTA). This study uses an exploratory case study design. The participants who voluntarily participated were 33 junior high school students (14 boys, 19 girls) in one of the schools in Indramayu Regency, Indonesia. Data obtained from the process of observation, tests, interviews, and documentation were analyzed qualitatively using Atlas. ti 8 software. The findings revealed that students with low 3D GTA achievements experienced difficulties in representing and calculating the surface area and volume of 3D shapes. In addition, students with moderate 3D GTA achievements experienced difficulties in representing 3D shapes but were able to translate 2D shapes from 3D shapes. Furthermore, students with high 3D GTA achievements experienced difficulties in calculating the surface area and volume of 3D shapes, but were able to use appropriate formulas and were able to interpret the comparisons of 3D geometric shapes well. The results of this study have implications for helping teachers identify student characteristics in understanding the concept of 3D geometry and connections with 2D geometry.
Co-Authors Abdulhalim Abubakar Camilo Andrés Rodríguez-Nieto Jenisus O. Dejarlo Mela Andriani Sakshi Kumar Shilpa Srivastava Sonia Rani Sudirman Sudirman Umida Baltaeva Varuna Gupta Zwelithini Bongani Dhlamini