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All Journal Journal of Dentistry Indonesia Indonesian Journal of Electrical Engineering and Computer Science
Aleem, Abdul
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Dentistry

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Optimizing clustering efficiency with weighted k-means: a machine learning-driven approach for enhanced accuracy and scalability Kaushik, Vishal; Aleem, Abdul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1121-1128

Abstract

Data analysis unlocks the hidden, latent patterns and structures within datasets. Clustering algorithms, the cornerstone of any data analysis, are usually challenged by high-dimensionality, complexity, or large-scale data. This research proposes a hybrid model that merges neural networks and clustering techniques to handle these problems. Neural networks are used for feature extraction and dimensionality reduction; raw data will be transformed into a robust, low-dimensional representation. With these refined features, the performance of clustering algorithms improves in terms of scalability, efficiency, and accuracy. The proposed model is tested on diversified datasets such as the wisconsin breast cancer dataset (WBCD), GEO Dataset, and image and text data benchmarks for which substantial improvements in clustering metrics such as silhouette score, purity, and computational efficiency are reported. The results demonstrate the efficacy of the hybrid approach in optimizing clustering applications across domains, such as bioinformatics, health care, and image analysis.
Efficient lung disease detection using a hybrid vision transformer and YOLO framework with transfer learning Khan, Kashaf; Aleem, Abdul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1141-1148

Abstract

Lung diseases are among the most important causes of morbidity and mortality worldwide; it require prompt and accurate diagnosis methods. A novel hybrid deep learning framework for integrating you only look once version 8 (YOLOv8), considering real-time detection and vision transformer (ViT-B/16) for global context-based classification of lung diseases in chest X-ray images, is presented. Based on transfer learning and a two-stage detection-classification pipeline, this proposed model is applicable to dealing with inter-image variability, overlapped disease features and lack of annotated medical examples. Our developed hybrid model achieves the highest classification accuracy of 96.8% and 0.98 AUC-ROC on the National Institutes of Health (NIH) Chest X-ray dataset, which consists of over 112,000 images covering 14 diseases, and outperforms its several current state-of-the-art models. In addition, attention heatmaps and bounding box visualizations highly correlate with clinical variables and enhance interpretability. This paper demonstrates the practicability of hybrid vision driven architectures for better medical image analysis and shows their integration into clinical decision-support systems.
Musculoskeletal Disorders in Dentistry: An Insight into Dental Techniques and Practices Kashif, Mehwash; Ashar, Aman; Rehman, Amna; Aleem, Abdul; Hashmi, Sidra-tul-Muntaha; Ali, Muhammad Yousuf
Journal of Dentistry Indonesia
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Objective: Musculoskeletal disorders (MSDs) are common workplace hazards in dentistry resulting from poor ergonomics and static postures, which lead to back, neck, and upper limb (shoulder, elbow, wrist, and hand) problems among dentists, resulting in harm to their health, productivity, and career. This study aimed to assess the frequency of MSDs, risk factors related to MSDs, and awareness of ergonomics among clinicians. Methods: Data were collected from 400 dental professionals, including house officers, postgraduate trainees, general dentists, and teachers/consultants, each with at least 12 months of practice. Data were collected using a structured, closed-ended questionnaire. Before data collection, ethical approval was obtained from the Institutional Review Board of the Karachi Medical and Dental College (ESRC/KMDC/069/2023). Results: It was found that only 12.8% of respondents (n = 49, all postgraduate trainees) regularly adjusted their chairs to an ergonomic position. Maintaining posture for > 40 min (p = 0.025), uncomfortable stools (p = 0.034), repeated motions for more than two hours (p = 0.003), maintained non-neutral postures for > 2 h (p = 0.002), and repetitive wrist flexion or extension (p = 0.014) were all significant risk factors. Conclusion: There is a need to incorporate ergonomics and posturedontics into undergraduate and continuing education curricula to minimize the risk of developing MSDs and to support sustainable careers.
Co-Authors Ali, Muhammad Yousuf Ashar, Aman Hashmi, Sidra-tul-Muntaha Kashif, Mehwash Kaushik, Vishal Khan, Kashaf Rehman, Amna