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p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Computing Theories and Applications
Minh T. Nguyen
Thai Nguyen University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction Muh Galuh Surya Putra Kusuma; De Rosal Ignatius Moses Setiadi; Wise Herowati; T. Sutojo; Prajanto Wahyu Adi; Pushan Kumar Dutta; Minh T. Nguyen
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14873

Abstract

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.
Performance evaluation of YOLOv11-based vehicle detection and tracking for urban intelligent transportation systems Thang C. Vu; Dung T. Nguyen; Minh T. Nguyen; Long Q. Dinh; Mui D. Nguyen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3518-3527

Abstract

This paper proposes and evaluates an integrated vehicle detection and tracking framework based on you only look once (YOLO)v11 for intelligent transportation systems (ITS). The combination of the deep simple online and real-time tracking (DeepSORT) algorithm helps maintain vehicle identity across consecutive frames, thereby enhancing the stability of the multi-object tracking system. Additionally, the slicing-aided hyper inference (SAHI) technique is integrated to improve the detection efficiency of small vehicles in remote sensing imagery and urban surveillance video data collected in Thai Nguyen, Vietnam. The system's performance is comprehensively evaluated through several key quantitative indicators, including mean average precision (mAP), multiple objects tracking accuracy (MOTA), and identification F1-score (IDF1), across realistic urban traffic scenarios. The results show that the framework significantly improves detection accuracy, tracking consistency, and small object recognition efficiency in real-world urban traffic scenarios. This paper provides useful insights for selecting appropriate detection and tracking configurations in ITS applications.
Co-Authors De Rosal Ignatius Moses Setiadi Dung T. Nguyen Long Q. Dinh Muh Galuh Surya Putra Kusuma Mui D. Nguyen Prajanto Wahyu Adi Pushan Kumar Dutta T. Sutojo Thang C. Vu Wise Herowati