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All Journal International Journal of Advances in Applied Sciences Advance Sustainable Science, Engineering and Technology (ASSET)
Mudhafar, Yousif Samer
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Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics

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Optimized Deep Learning Framework for Clinical Data Classification Using Firefly-Enhanced Stacked Sparse Autoencoders Mudhafar, Yousif Samer; Al-Fatlawy, Ramy Riad; Al-Fatlawy, Ali Ahmed; Shakir, Aboothar Mahmood
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.v8i1.2283

Abstract

Diabetes is a chronic metabolic disorder characterized by sustained high blood sugar levels, which frequently cause complications, including neuropathy and cardiovascular disease. Due to the complex and nonlinear nature of clinical data, accurate and timely prediction is challenging. Traditional approaches struggle to generalize or extract rich features from low-resolution datasets. In this paper, a hybrid deep learning model (FA-SSAE: Firefly Algorithm-based Stacked Sparse Autoencoder) is proposed to improve diabetes classification using the Pima Indians Diabetes dataset. Data is synthesized using Variational Autoencoder (VAE) developed data augmentation and deep features are extracted using SSAE. The model achieved 91.67% accuracy, 96.38% precision, and 98.75% recall; results that significantly outperformed several state-of-the-art methods. The results demonstrate the robustness and reliability of the proposed approach. Its lightweight architecture can be deployed in resource-limited environments, providing value for mobile or embedded systems used in remote clinics. This research advances the development of scalable and accessible tools for diagnostic detection of diabetes in the earliest possible stages to aid in unsupervised clinical care.
Robust multi-faces recognition and tracking via fuzzy genetic algorithms and deep coupled features Abushana, Adil Abdulhur; Mudhafar, Yousif Samer
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp209-218

Abstract

In real-world surveillance environments, face recognition and tracking remain challenging due to partial occlusion, pose variation, illumination changes, and background clutter. This paper presents a robust hybrid framework that integrates fuzzy genetic algorithms (FGA) with deep coupled feature learning for multi-face recognition and tracking. The proposed system comprises three main modules: i) face detection and pre processing using the multi-task cascaded convolutional network (MTCNN), ii) deep coupled ResNet embeddings that jointly learn identity and appearance-invariant representations, and iii) a fuzzy rule-based genetic optimizer that adaptively refines tracking decisions based on uncertainty in motion, appearance similarity, and occlusion levels. The novelty of this work lies in the fusion of fuzzy inference with evolutionary search to guide the genetic optimization process—allowing dynamic adaptation to noisy and uncertain visual conditions. Moreover, probabilistic data association filters (PDAF) and conditional joint likelihood filters (CJLF) are employed to further enhance temporal consistency under occlusion and appearance variation. The results confirm that fuzzy evolutionary optimization, when coupled with deep feature learning, significantly improves robustness and stability for real-time face tracking in complex, dynamic scenes.
Co-Authors Abushana, Adil Abdulhur Al-Fatlawy, Ali Ahmed Al-Fatlawy, Ramy Riad Shakir, Aboothar Mahmood