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All Journal Bulletin of Electrical Engineering and Informatics Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Minh T. Nguyen, Minh T.
Ophthalmo-ORL-Maxillo-Facial-Odontology Hospital of An Giang
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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A novel framework of building operation algorithm for the block of technical diagnostics of aircraft’s automatic control system Vuong, Trung A.; Tran, Dong LT.; Vo, Thanh C.; Nguyen, Minh T.; Tran, Hoang T.
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.5799

Abstract

This article presents the problem of designing an automatic control system that is stable against errors and failures of sensors on aircraft. The sensor system has a technical diagnostic block that ensures diagnosis and eliminates typical errors and failures. Based on the determination of the error vector, damage can occur by adding measurement elements corresponding to the measurement parameters to the control system. When there are errors or failures of the sensor elements, the state vector of the system changes and is determined by measurements. The difference between the measured vector components when there are errors, failures and when working normally is the basis of the working algorithm of the failure diagnosis block. The results demonstrate encouraging prospects for practical implementations.
Comprehensive Review of Security Problems in Mobile Robotic Assistant Systems: Issues, Solutions, and Challenges Dinh, Long Q.; Nguyen, Dung T.; Vu, Thang C.; Nguyen, Tao V.; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11408

Abstract

Nowadays, robots in the modern world are playing an important and increasingly popular role. MRA (Mobile Robotic Assistant) is a type of mobile robot designed to support humans in many different fields, helping to improve efficiency and safety in daily activities, work, or medical treatment. The number of MRAs is increasing and diverse in function, in addition to the ability to collect and process data, MRAs also have the ability to physically interact with users. Therefore, security is one of the important issues to improve the safety and effective operation of MRA. In this paper, through a comprehensive literature review and detailed analysis of the prominent MRA security attacks in recent years (based on criteria such as: attack targets, technologies used, impact level, feasibility, and contribution to addressing overall MRA security issues), a systematic classification by MRA activity fields is conducted. Security attacks, threats, and vulnerabilities are examined from various perspectives, such as hardware attacks or network/system-level attacks, operating systems/application software. Additionally, corresponding security solutions are proposed, compared, and evaluated to enhance MRA security. The paper also addresses challenges and suggests open research directions for the future.
Advanced and AI Embedded Technologies in Education: Effectiveness, Recent Developments, and Opening Issues Nguyen, Minh T.; Nguyen, Thuong TK.
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-19

Abstract

This paper offers a brief analysis highlighting the effectiveness of several well-known Artificial Intelligence (AI) technologies applied in education, particularly in teaching and learning. It provides an overview of how modern classrooms can benefit from more effective teaching strategies that encourage students to engage in hands-on learning. Advanced technologies are changing how knowledge is found and shared, as well as how teaching is delivered. Memorization has been emphasized in educational models as a crucial learning skill until relatively recently. The technologies alter how knowledge is accessed and taught in schools today. Based on that, most knowledge is readily available, quickly accessible, and available online. The skills of reading, sharing, listening, and acting are now prerequisites for schooling. Most recent developments in advanced technologies in education are provided. Some analyses related to opening issues and challenges are shown for future work.
A Novel Clustering Solution Based on Energy Threshold for Energy Efficiency Purposes in Wireless Sensor Networks Vu, Thang C.; Do, Binh D.; Nguyen, Mui D.; Nguyen, Dung T.; Nguyen, Tao V.; Dinh, Long Q.; Nguyen, Hung T.; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 3 No. 1 (2025): JCTA 3(1) 2025 - in progress
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.13022

Abstract

In many wireless sensor network (WSN) applications, nodes are randomly deployed and self-organize into a wireless network to perform tasks. In practice, recharging the batteries of network nodes after deployment is often difficult. Network nodes often operate autonomously, so the main focus is on increasing the node lifetime. Data redundancy is another limitation that makes nodes inefficient. In most cases, densely deployed nodes in a monitoring area will have redundant data from neighboring nodes. Therefore, we propose a clustering technique to select the Cluster Head (CH) node in small-scale WSNs. Since transmission consumes more energy than data collection, this protocol enables reactive routing, where transmission occurs only when a certain threshold is reached. In addition, based on their heterogeneous energy levels, nodes can be grouped into three categories: Normal, Intermediate, and Advanced. Simulation results in MATLAB/Simulink show that, after approximately 3000 rounds, the proposed method successfully transmitted about 3.1 × 104 packets to the base station, compared to 2.3 × 104 packets for the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. In addition, the time when the last node died was approximately 3,500 rounds, whereas the LEACH protocol only maintained about 1,500 rounds. The results have shown the effectiveness of this technique in reducing the dead node rate and increasing packet transmission efficiency.
Indoor Positioning using Smartphones: An Improved Time-of-Arrival Technique Vu, Thang C.; Nguyen, Trung H.; Nguyen, Mui D.; Nguyen, Dung T.; Nguyen, Tao V.; Dinh, Long Q.; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 3 No. 1 (2025): JCTA 3(1) 2025 - in progress
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.13305

Abstract

Indoor positioning technology based on smartphones plays an important role in the current technological development context. Especially in applications such as warehouses, supermarkets, hospitals, or buildings. While the global positioning system (GNSS) is popular and effective outdoors, it has several limitations when operating in enclosed spaces, such as indoors, due to the complexity of these environments. Smartphones have many built-in sensors (such as light sensors, sound sensors, gyroscopes, accelerometers, and magnetic sensors) and support the connection of various types of wireless communication technologies such as Wi-Fi and Bluetooth. However, such sensors were not initially developed for positioning applications. This study addresses the positioning problem using the MUSIC technique in conjunction with the Time of Arrival (ToA) method. The effectiveness of the positioning solution is evaluated through the signal-to-noise ratio (SNR) index. The absolute error and squared error indices are evaluated through the cumulative distribution function (CDF) to indicate the effectiveness of the proposed solution. Additionally, we propose a Pedestrian Dead Reckoning method to determine a person's position in indoor environments continuously. Based on the segmentation of the moving process by turns, the direction measurements in each segment are processed using a Kalman filter, which is designed to enhance the results achieved by the system. We also discuss the challenges and some future research directions in the field of smartphone-based indoor positioning.
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction Kusuma, Muh Galuh Surya Putra; Setiadi, De Rosal Ignatius Moses; Herowati, Wise; Sutojo, T.; Adi, Prajanto Wahyu; Dutta, Pushan Kumar; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): in progress
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.
Co-Authors De Rosal Ignatius Moses Setiadi Dinh, Long Q. Do, Binh D. Dutta, Pushan Kumar Herowati, Wise Kusuma, Muh Galuh Surya Putra Nguyen, Dung T. Nguyen, Hung T. Nguyen, Mui D. Nguyen, Tao V. Nguyen, Thuong TK. Nguyen, Trung H. Prajanto Wahyu Adi, Prajanto Wahyu T. Sutojo Tran, Dong LT. Tran, Hoang T. Vo, Thanh C. Vu, Thang C. Vuong, Trung A.