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All Journal International Journal of Advances in Applied Sciences International Journal of Electronics and Communications Systems
Ghosh, Kingkar Prosad
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A convolutional neural network with attention mechanism-based malaria detection from blood smear images Ghosh, Kingkar Prosad; Jibon, Ferdaus Anam; Haque, Shahina; Ali, Md. Suhag; Islam, Md. Monirul; Uddin, Jia
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1010-1017

Abstract

With 249 million cases and 608,000 fatalities recorded in 2022, malaria is one of the major worldwide health concerns, particularly in areas with low resources. In this paper, we propose a custom convolutional neural network (CNN) with an integrated attention mechanism to inspect malaria from blood smear images. To improve model robustness, we combined three publicly available datasets from the NIH and Kaggle. The proposed model achieved 98.20% accuracy, 97.85% precision, 98.55% recall, and 98.20% F1-score, outperforming conventional di agnostic methods. In addition, we conduct comparative analyses using two transfer learning models, ResNet50 and DenseNet. ResNet50 attained 95.06% precision, 95.44% recall, with 95.05% F1-score, while DenseNet achieved a pre cision of 87.96%, recall of 88.33%, and F1-score of 87.90%. For interpretability, Grad-CAMandsaliency map visualizations highlighted key image regions, with saliency maps offering finer pixel-level localization. These results highlight the potential of our attention-based CNN as a feasible, interpretable diagnostic tool for malaria, particularly in low-resource settings.
Credit Card Fraud Detection Using a Stacked DNN–XGBoost–LightGBM–CatBoost Ensemble (DXCL): A Comparative Performance Study on Real-World Transaction Data Ghosh, Kingkar Prosad; Roy, Ankan; Saha, Shatabdi; Singha, Anupam; Chakraborty, Kanika; Mandal, Sukanya
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.28228

Abstract

E-commerce has increased the productivity of international trade, causing an increase in credit card fraud that has damaged finances and weakened public confidence in digital payment systems. This study aims to improve the sensitivity and reliability of fraud detection on highly imbalanced transaction data through the design and evaluation of the DXCL model compared to conventional individual and ensemble models. This methodology uses resampling approaches such as random undersampling and SMOTE oversampling during training to reduce class imbalance. DXCL's performance is evaluated against six benchmark models Random Forest, standalone DNN, XGBoost, LightGBM, CatBoost, and a dummy classifier utilizing accuracy, precision, recall, F1-score, ROC-AUC, TPR, and FPR metrics on a 2013 European credit card transaction dataset. The results prove that DXCL outperforms individual models and Random Forest in effective rate while eliminating false positive rate with 99.98% accuracy, precision, recall, F1-score, and ROCAUC of 1.00. Deep feature extraction and ensemble enhancement significantly improve fraud examination of class imbalanced transaction datasets. DXCL supports the application of a more dependable approach for detecting credit card fraud with low false positive rates in highly imbalanced digital transaction environments
Co-Authors Ali, Md. Suhag Chakraborty, Kanika Haque, Shahina Islam, Md. Monirul Jibon, Ferdaus Anam Mandal, Sukanya Roy, Ankan Saha, Shatabdi Singha, Anupam Uddin, Jia