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Keyword: Diagnostic Accuracy
Enhancing Breast Cancer Detection through a Hybrid Approach of PCA and 1D CNN
by Samet Aymaz
Journal of Engineering Research and Sciences, Volume 4, Issue 4, Page # 20-30, 2025; DOI: 10.55708/js0404003
Abstract: Breast cancer is a prevalent disease, particularly among women. Unlike many other cancers, early diagnosis and treatment can significantly improve patients’ quality of life. This study develops a hybrid approach for breast cancer detection using the Wisconsin datasets by combining Principal Component Analysis (PCA) and 1D Convolutional Neural Network (CNN) architectures to effectively separate and… 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))
A Swarm-Based Clinical Validation Framework of Artificial Intelligence Solutions for Non-Communicable Diseases
by Kitty Kioskli, Spyridon Papastergiou and Theofanis Fotis
Journal of Engineering Research and Sciences, Volume 2, Issue 9, Page # 1-11, 2023; DOI: 10.55708/js0209001
Abstract: Non-communicable diseases (NCDs) present complex challenges in patient care. Artificial Intelligence (AI) offers transformative potential, but its implementation requires addressing key issues. This study proposes a swarm intelligence-inspired clinical validation framework for NCDs, promoting openness, trustworthiness, and continuous self-validation. The framework creates a collaborative environment, connecting healthcare entities, patients, caregivers, and professionals. The swarm-based approach… Read More
(This article belongs to the Special Issue on SP3 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2023) and the Section Medical Informatics (MDI))
Bearing Fault Diagnosis Based on Ensemble Depth Explainable Encoder Classification Model with Arithmetic Optimized Tuning
by Kaibi Zhang, Yanyan Wang and Hongchun Qu
Journal of Engineering Research and Sciences, Volume 1, Issue 3, Page # 81-97, 2022; DOI: 10.55708/js0103009
Abstract: In a dynamic and complex bearing operating environment, current auto-encoder-based deep models for fault diagnosis are having difficulties in adaptation, which usually leads to a decline in accuracy. Besides, the opaqueness of the decision process by such deep models might reduce the reliability of the diagnostic results, which is not conducive to the subsequent optimization… Read More
(This article belongs to the Special Issue on SP1 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2022) and the Section Artificial Intelligence – Computer Science (AIC))