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Keyword: Logistic Regression
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Open AccessArticle
10 Pages, 1,948 KB Download PDF
Binary Image Classification with CNNs, Transfer Learning and Classical Models

by Nikolaos Vasileios Oikonomou, Dimitrios Vasileios Oikonomou, Sofia Panagiota Chaliasou and Nikolaos Rigas
Journal of Engineering Research and Sciences, Volume 5, Issue 1, Page # 66-75, 2026; DOI: 10.55708/js0501006
Abstract: This study presents a comprehensive comparative analysis of binary face classification utilizing Deep Learning and traditional Machine Learning approaches. We evaluate three distinct modeling strategies: (1) End-to-end Convolutional Neural Networks (CNNs), including a baseline TensorFlow model and an optimized PyTorch architecture; (2) Hybrid CNN-MLP networks; and (3) Feature extraction via a pre-trained ResNet50 coupled with… Read More

(This article belongs to the Special Issue on SP7 (Special Issue on Multidisciplinary Sciences and Advanced Technology (SI-MSAT 2025)) and the Section Artificial Intelligence – Computer Science (AIC))

Open AccessArticle
30 Pages, 6,424 KB Download PDF
Model Uncertainty Quantification: A Post Hoc Calibration Approach for Heart Disease Prediction

by Peter Adebayo Odesola, Adewale Alex Adegoke and Idris Babalola
Journal of Engineering Research and Sciences, Volume 4, Issue 12, Page # 25-54, 2025; DOI: 10.55708/js0412003
Abstract: We investigated whether post-hoc calibration improves the trustworthiness of heart-disease risk predictions beyond discrimination metrics. Using a Kaggle heart-disease dataset (n = 1,025), we created a stratified 70/30 train-test split and evaluated six classifiers, Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Naive Bayes, Random Forest, and XGBoost. Discrimination was quantified by stratified 5-fold cross-validation with… Read More

(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))

Open AccessArticle
8 Pages, 1,327 KB Download PDF
Fire Type Classification in the USA Using Supervised Machine Learning Techniques

by Ranyah Taha, Fuad Musleh and Abdel Rahman Musleh
Journal of Engineering Research and Sciences, Volume 4, Issue 6, Page # 1-8, 2025; DOI: 10.55708/js0406001
Abstract: Wildfires are a growing global concern, causing widespread environmental, economic, and health impacts. In the USA, fire incidents have become more frequent and intense due to factors such as climate change, prolonged droughts, and human activities. Machine learning plays a vital role in predicting and classifying fires by analyzing vast satellite and environmental datasets with… Read More

(This article belongs to the Special Issue on SP6 (Special Issue on Computing, Engineering and Sciences (SI-CES 2024-25)) and the Section Remote Sensing (RMS))

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