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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

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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.
A Hybrid Deep-Learning and Evolutionary Feature-Selection Framework for Skin Lesion Classification: Application to Monkeypox Detection Nidhi Chauhan; Alok Singh Chauhan
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2786

Abstract

The recent resurgence of Monkeypox has highlighted the urgent need for fast and accurate diagnostic tools. In this paper, we propose a new framework of hybrid deep learning to combine both DenseNet121 and MobileNetV2 to obtain both rich and supplementary attributes of the skin lesion images. By pooling the outputs of these two models in terms of features, we get the lightweight representation of the images as well as rich representations of the images. To improve the feature set, we use Genetic Algorithm (GA) which is useful in reducing the dimensions and eliminating redundancy. Optimized features are then categorized with the help of the Random Forest model, which has been selected due to its good performance and capacity to work with high-dimensional data. Using two publicly accessible datasets, MSID and MSLD, we tested our approach and obtained remarkable classification accuracies of 92.71% and 97.77%, respectively. These findings highlight the success of combining ensemble learning, evolutionary optimization, and deep learning to achieve accuracy and proper diagnosis of monkeypox through medical images.
Comparative Evaluation of Parameter-Efficient Fine-Tuning Strategies for Continual Image Classification Nancy Agarwal; Alok Singh Chauhan; Patrick Bours
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2787

Abstract

Catastrophic forgetting remains a major challenge in continual transfer learning, where performance on earlier tasks degrades after sequential adaptation. While full fine-tuning updates all parameters and achieves strong performance on new tasks, it is computationally expensive and prone to forgetting. This study compares parameter-efficient fine-tuning (PEFT) methods—adapters, additive learning, side-tuning, LoRA, and zero-initialized layers—against full fine-tuning on CIFAR-100 using a two-stage protocol: task-A (classes 0–49) followed by task-B (classes 50–99), evaluated on ResNet-18 and ResNet-50. Results are reported as mean ± standard deviation over three runs (n = 3), with retention measured using a Swapback-based recall method that distinguishes true forgetting (Δ). Across both architectures, all PEFT methods maintain task-A knowledge (Δ = 0.00), while full fine-tuning exhibits forgetting (Δ = 0.31 on ResNet-18; Δ = 0.20 on ResNet-50). PEFT methods achieve competitive task-B performance while updating only 0.22–4.49% of parameters. Notably, LoRA on ResNet-50 achieves the highest task-B accuracy (0.82) with only 0.93% parameter updates and no forgetting, slightly outperforming full fine-tuning (0.81). These findings highlight PEFT as an efficient and stable alternative for scalable continual transfer learning.
Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features Vineeta Khemchandani; Alok Singh Chauhan; Shahnaz Fatima; Jalauk Singh Maurya; Abhay Singh Rathaur; Narendra Kumar Sharma; Daya Shankar Srivastava; Vugar Abdullayev
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2833

Abstract

Cognitive workload plays a vital role in tasks that demand dynamic decision-making, especially under high-risk and time-sensitive conditions. An excessive workload can lead to unexpected and disproportionate risks, whereas insufficient workload may cause disengagement, undermining task performance. This underscores the importance of maintaining an optimal level of mental focus in high-pressure situations to ensure successful task execution. This study leverages deep learning methods alongside functional connectivity measures to classify cognitive workload levels. Using the N-back EEG dataset, functional connectivity metrics such as Phase Locking Value (PLV), Phase Lagging Index (PLI), and Coherency are extracted after data pre-processing. These metrics, characterized as directed or non-directed, enable efficient computational analysis. A convolutional neural network (CNN) classifier is employed to categorize cognitive workload into three levels: low (0-back), medium (2-back), and high (3-back). The CNN-A architecture achieves peak performance with an accuracy of 93.75% using PLV, 87.5% using Coherency, and 68.75% using PLI.
Co-Authors Abdulhalim Abubakar Abhay Singh Rathaur Camilo Andrés Rodríguez-Nieto Daya Shankar Srivastava Jalauk Singh Maurya Jenisus O. Dejarlo Mela Andriani Nancy Agarwal Narendra Kumar Sharma Nidhi Chauhan Patrick Bours Sakshi Kumar Shahnaz Fatima Shilpa Srivastava Sonia Rani Sudirman Sudirman Umida Baltaeva Varuna Gupta Vineeta Khemchandani Vugar Abdullayev Zwelithini Bongani Dhlamini