A Large-Scale Dataset for Real-Time Vehicle Detection in Vietnamese Urban Traffic Scenes Quang Dong Nguyen Vo, Gia Nhu Nguyen, Hoang Vu Tran Computers Materials and Continua, 2026 Reliable vehicle detection in urban traffic environments remains challenging, particularly for fixed-view CCTV systems deployed in Southeast Asian cities, where heterogeneous traffic composition, high traffic density, frequent occlusions, and complex visual conditions are prevalent. The absence of large-scale datasets tailored to such mixed-traffic environments poses a significant limitation to the performance and generalization capability of existing object detection models. To address this gap, this paper presents a large-scale traffic image dataset for real-time vehicle detection in Vietnamese urban environments. The proposed dataset comprises 23,364 images collected from fixed-view CCTV traffic cameras deployed across Da Nang City, a representative urban area exhibiting mixed-traffic patterns commonly observed in Southeast Asian cities. The data cover diverse temporal periods, weather conditions, and traffic density levels encountered in real-world traffic monitoring scenarios. To comprehensively characterize these conditions, over 1.1 million instances are annotated across multiple traffic-related categories, including pedestrians, bicycles, motorbikes, cars, buses, trucks, and traffic lights with explicit signal-state labels. Such fine-grained, multi-class annotations support not only object-level detection but also higher-level traffic scene analysis relevant to intelligent transportation system (ITS) applications, such as traffic flow analysis and signal control. To balance annotation accuracy and scalability, a semi-automatic labeling pipeline is employed. Initial object annotations are generated using a pretrained YOLOv11m model and subsequently refined through systematic manual verification using the CVAT platform. Comprehensive experiments are conducted under the same experimental protocol, using the same YOLOv11m architecture, comprising a pretrained baseline and a version fine-tuned on the proposed dataset with domain-specific data augmentation and optimized hyperparameter settings tailored to fixed-view CCTV conditions. Under the same evaluation setting, the pretrained YOLOv11m achieves a mean Average Precision (mAP) of 0.409; in contrast, fine-tuning on the proposed dataset improves the mAP to 0.788. These results underscore the necessity of localized, context-aware datasets such as the one presented in this work for robust real-time traffic perception in Vietnam and similar Southeast Asian urban contexts.
A Hybrid Deep Learning Approach for Real-Time Cheating Behaviour Detection in Online Exams Using Video Captured Analysis Dao Phuc Minh Huy, Gia Nhu Nguyen, Dac-Nhuong Le Computers Materials and Continua, 2026 Online examinations have become a dominant assessment mode, increasing concerns over academic integrity. To address the critical challenge of detecting cheating behaviours, this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification. The methodology utilises object detection models—You Only Look Once (YOLOv12), Faster Region-based Convolutional Neural Network (RCNN), and Single Shot Detector (SSD) MobileNet—integrated with classification models such as Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Unit (Bi-GRU), and CNN-LSTM (Long Short-Term Memory). Two distinct datasets were used: the Online Exam Proctoring (EOP) dataset from Michigan State University and the School of Computer Science, Duy Tan Unievrsity (SCS-DTU) dataset collected in a controlled classroom setting. A diverse set of cheating behaviours, including book usage, unauthorised interaction, internet access, and mobile phone use, was categorised. Comprehensive experiments evaluated the models based on accuracy, precision, recall, training time, inference speed, and memory usage. We evaluate nine detector–classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies, enabling deployment-oriented selection under latency and memory constraints. Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) are reported for the top configurations, revealing consistent advantages of object-centric pipelines for fine-grained cheating cues. The highest overall score is achieved by YOLOv12 + CNN (97.15% accuracy), while SSD-MobileNet + CNN provides the best speed–efficiency trade-off for edge devices. This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
A Comparative Analysis of Machine Learning Algorithms for Spam and Phishing URL Classification Tran Minh Bao, Kumar Shashvat, Nguyen Gia Nhu, Dac-Nhuong Le Computers Materials and Continua, 2026 The sudden growth of harmful web pages, including spam and phishing URLs, poses a greater threat to global cybersecurity than ever before. These URLs are commonly utilised to trick people into divulging confidential details or to stealthily deploy malware. To address this issue, we aimed to assess the efficiency of popular machine learning and neural network models in identifying such harmful links. To serve our research needs, we employed two different datasets: the PhiUSIIL dataset, which is specifically designed to address phishing URL detection, and another dataset developed to uncover spam links by examining the wording and structure of every URL. Our strategy was to train and evaluate four classification models, namely Random Forest, Support Vector Machine (SVM), Naive Bayes, and Artificial Neural Networks (ANN), under two different feature engineering approaches: statistical text-based analysis and heuristic-based structural features. The results are in, and they are stunning: Random Forest and ANN models were always the best. During our research, we achieved some outstanding results. On the PhiUSIIL phishing dataset, the model achieved an accuracy of 99.99%, and on the spam dataset, it attained an accuracy of 99.62%. Studies surpass any previously reported findings, firmly establishing the efficacy of machine learning and neural networks in detecting malicious URLs. Not only does this work reinforce the superiority of these in-demand models, but it also sets a high bar for subsequent research and development in the field. In general, this provides the direction for building smarter, faster, and more precise tools that can spot online threats as they develop.
Deep learning approaches for predicting cheating from student exam results: a comparative study under imbalanced data conditions Dao Phuc Minh Huy, Nguyen Gia Nhu, Dac-Nhuong Le Applied Computing and Informatics, 2025 Purpose To detect academic misconduct in students' assessment score trajectories under severe class imbalance. The paper compares tabular learners, gradient-boosting models, and sequence-aware deep networks, and proposes a precision–recall–centric evaluation and deployment protocol (calibration, threshold selection and Recall@Top-k%) tailored to rare-event screening in educational settings. Design/methodology/approach A cohort of 1,527 students (2021–2024) is modeled using ten algorithms: LR, DT, RF, SVM, MLP, XGBoost, CatBoost, LightGBM, GRU-RNN and 1D-CNN. Features encode sequential score dynamics and metadata. Models are tuned via cross-validation; probabilities are calibrated (Platt/Isotonic); operating thresholds are chosen on validation to maximize minority-class F1 or a cost-sensitive utility. Performance is assessed on a hold-out test set with PR-AUC (headline), F1(+), Recall@Top-k%, ROC-AUC, calibration curves, Brier, and bootstrap CIs. Findings Sequence-aware models dominate: GRU-RNN and 1D-CNN achieve ROC-AUC ˜0.97–0.98 and the highest F1(+) and Recall@Top-5%. Tabular/boosting baselines show ˜0.90 accuracy yet miss most positives at the default 0.5 threshold, highlighting the necessity of calibration and threshold optimization. With PR-centric selection and tuned operating points, deep temporal models yield strong screening utility for limited human review budgets. Research limitations/implications Labels reflect suspected–not adjudicated–cheating, introducing noise. The single-institution cohort may limit external validity; temporal shift across semesters can degrade performance. Future work should include multi-site evaluation, collusion/graph modeling, semi-/weak-supervision for noisy labels and governance topics (fairness audits, drift monitoring and uncertainty reporting). Practical implications This study highlights how educational institutions can leverage machine learning for early detection of academic dishonesty based on historical performance data. CNN and RNN models are promising tools for identifying anomalous learning patterns. However, practical deployment requires preprocessing techniques to manage class imbalance and threshold optimization to reduce false negatives. The findings provide a roadmap for building automated cheating detection systems in both online and traditional assessment environments. Social implications By improving the ability to detect cheating, this research contributes to fairer academic environments, upholding educational integrity and credibility of credentials. However, ethical considerations must be taken into account to avoid false accusations and ensure student rights. Human-in-the-loop systems are crucial for verifying algorithmic predictions before disciplinary action, thereby fostering transparency and accountability in automated decision-making processes. Originality/value The study unifies a minority-focused evaluation protocol with a comprehensive comparison of tabular, boosting, and sequence-aware models for cheating detection from score trajectories. It demonstrates the decisive value of temporal representation learning and provides a reproducible pipeline and operational metrics that align model performance with real investigative workflows.
Stabilizing Quality of Wi-Fi-Based Location Services Using High-Performance Distributed Stream Processing and Data Pipelines Chen-Kun Tsung, Ching-Hsien Hsu, Jung-Chun Liu, Nguyen Gia Nhu, Chun Hsiung, Xin-Ting Zhang, Chao-Tung Yang IEEE Transactions on Services Computing, 2025 Location-based Systems (LBS) are popular for delivering customized information. However, some issues, such as place credibility, the efficiency of position calculations, and communication latency, pose challenges for indoor LBS. This work proposes the High-performance Perspective Platform (H3P) to help network managers understand network users’ information. The H3P provides indoor positioning services based on Wi-Fi 6 (IEEE 802.11ax) to stabilize service quality and ensure high computation efficiency for rapid service response. It utilizes Apache Kafka and Apache Zookeeper clusters on Kubernetes to handle large amounts of data. Wi-Fi usage data is transmitted to Kafka’s distributed real-time data streaming to enhance position credibility and the immediacy of position calculations. The data structure is also optimized to improve computation efficiency. Experimental results show that H3P improves data latency by up to 69% and data insertion latency by up to 73%. Additionally, H3P offers more stability and efficiency than Chang et al., 2012 in terms of data transmission, with an improvement of approximately 16.15%. This allows administrators to manage the network with user-friendly interfaces and a smooth user experience.
CNN-FSPM-Based Fingerprint Indexing and Matching for Detecting, Predicting, and Preventing Cheating in Online Examinations Dao Phuc Minh Huy, Nguyen Gia Nhu, Dac-Nhuong Le International Journal of Knowledge and Systems Science, 2024 This paper presents a comprehensive approach to the detection and prevention of cheating in online exams using AI. The authors employ various technical solutions to monitor proctors throughout all stages of the exam: before, during, and after. To address the formulations and ensure the continuous expansion of the database, the authors rely on a fast convolutional neural network (CNN) that utilizes a full-scope pattern matching algorithm (FSPM) to enhance the ability to match fingerprint formats using descriptive network cryptography. The authors anticipate reliable matching across the complete fingerprint image set through the utilization of deep-learning (DL) symbols. Furthermore, the authors demonstrate that solving image-matching problems does not necessitate tool training data, which is typically required for such problems. Thanks to the highly parallelizable nature of these tasks, the authors provide an efficient method with minimal computational cost during test time to detect cheating during some exams at School of Computer Science at Danang University of Technology (SCS-DTU) University, Vietnam.
Preface Advances in Intelligent Systems and Computing, 2020
Deep Feature Extraction for Panoramic Image Stitching Van-Dung Hoang, Diem-Phuc Tran, Nguyen Gia Nhu, The-Anh Pham, Van-Huy Pham Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
Challenges faced by cloud computing Shouray Kumra, Tanupriya Choudhury, Nguyen Gia Nhu, Tarun Nalwa Proceedings of the 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology Icatcct 2017, 2018
Preface Bansal, Himani 1985-, Shrivastava, Gulshan, Nguyen, Gia Nhu, Stanciu, Loredana-Mihaela Social Network Analytics for Contemporary Business Organizations, 2018
Cloud Computing and Virtualization Dac-Nhuong Le, Raghvendra Kumar, Gia Nhu Nguyen, Jyotir Moy Chatterjee Cloud Computing and Virtualization, 2018
Evolutionary framework for coding area selection from cancer data Sarwar Kamal, Nilanjan Dey, Sonia Farhana Nimmy, Shamim H. Ripon, Nawab Yousuf Ali, Amira S. Ashour, Wahiba Ben Abdessalem Karaa, Gia Nhu Nguyen, Fuqian Shi Neural Computing and Applications, 2018
Augmenting dental care: A current perspective Anand Nayyar, Gia Nhu Nguyen Emerging Technologies for Health and Medicine Virtual Reality Augmented Reality Artificial Intelligence Internet of Things Robotics Industry 4 0, 2018
Analysis of telemedicine technologies Vikram Puri, Jolanda G Tromp, Noell C.L. Leroy, Chung Le Van, Nhu Gia Nguyen Emerging Technologies for Health and Medicine Virtual Reality Augmented Reality Artificial Intelligence Internet of Things Robotics Industry 4 0, 2018
Proceedings of Fifth International Conference on Computing and Communication Networks: ICCCN 2025, Volume 4 GN Nguyen Springer Nature , 2026 2026
A Comparative Analysis of Machine Learning Algorithms for Spam and Phishing URL Classification. TM Bao, K Shashvat, NG Nhu, DN Le Computers, Materials & Continua 87 (2), 1 , 2026 2026
A Hybrid Deep Learning Approach for Real-Time Cheating Behaviour Detection in Online Exams Using Video Captured Analysis. DPM Huy, GN Nguyen, DN Le Computers, Materials & Continua 86 (3) , 2026 2026 Citations: 1
Deep learning approaches for predicting cheating from student exam results: a comparative study under imbalanced data conditions DP Minh Huy, NG Nhu, DN Le Applied Computing and Informatics, 1-20 , 2026 2026 Citations: 3
A Large-Scale Dataset for Real-Time Vehicle Detection in Vietnamese Urban Traffic Scenes QDN Vo, GN Nguyen, HV Tran Computers, Materials, & Continua 88 (1) , 2026 2026
Vietnam and Korea in the Age of AI: Toward Human-Centered and Wellbeing-Oriented Education NG Nhu, YJ Jeon 한국콘텐츠학회 ICCC 논문집, 509-510 , 2025 2025
Stabilizing Quality of Wi-Fi-based Location Services Using High-Performance Distributed Stream Processing and Data Pipelines CK Tsung, CH Hsu, JC Liu, NG Nhu, C Hsiung, XT Zhang, CT Yang IEEE Transactions on Services Computing , 2025 2025
A Combine Solution for Online Exams Cheating Detection, Prediction, and Prevention Using Artificial Intelligence DPM Huy, NG Nhu, DN Le Lecture Notes in Networks and Systems , 2025 2025 Citations: 4
Multiple digital patient check-in through blockchain and medical sensor network B Le, V Puri, NG Nguyen, C Van Le Sensor Networks for Smart Hospitals, 589-602 , 2025 2025
A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing: M. Hosseinzadeh et al. M Hosseinzadeh, AM Rahmani, FM Husari, OM Alsalami, M Marzougui, ... Archives of Computational Methods in Engineering 32 (1), 269-310 , 2025 2025 Citations: 27
Student Monitoring System Combining Facial Recognition and Identification DPM Huy, HTH Thom, NG Nhu, DN Le Big Data Analytics and Data Science: Proceedings of Eighth International … , 2024 2024
Integration of Long-Term Memory Architectures in Large Language Models A Methodological Approach for Vietnamese Education NG Nhu, T Nguyen 한국콘텐츠학회 ICCC 논문집, 185-188 , 2024 2024
Mixed Traffic Flow Modeling Solution on Digital Map HV Tran, NVQ Dong, PC Tho, GN Nguyen 2024 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 1-4 , 2024 2024
Deep Learning for Outage Probability Minimization in Secure NOMA Energy Harvesting UAV IoT Networks QL Nguyen, VH Dang, GN Nguyen, TT Nguyen, DT Ho, H Tran, DD Tran, ... 2024
Student monitoring system combining facial recognition and identification methods DPM Huy, HTH Thom, NG Nhu, DN Le Proceedings of Eighth International Conference on Information System Design … , 2024 2024 Citations: 2
Upgrade Throughput and Delay in Wireless Communication Radio's based Internet of Things Using Simulation and Mathematical Analysis GN Nguyen, L Van Chung, DN Le International Journal of Computer Science & Network Security 24 (12), 81-86 , 2024 2024
CNN-FSPM-based fingerprint indexing and matching for detecting, predicting, and preventing cheating in online examinations DPM Huy, NG Nhu, DN Le International Journal of Knowledge and Systems Science (IJKSS) 15 (1), 1-20 , 2024 2024 Citations: 2
Deep learning for outage probability minimization in secure NOMA energy harvesting UAV IoT networks NQ Long, VH Dang, NG Nguyen, TT Nguyen, TD Ho, H Tran, DD Tran, ... Mobile Networks and Applications 28 (6), 2275-2287 , 2023 2023 Citations: 6
Multi-attribute decision-making approach based on Aczel-Alsina power aggregation operators under bipolar fuzzy information & its application to quantum computing H Garg, T Mahmood, U ur Rehman, GN Nguyen Alexandria Engineering Journal 82, 248-259 , 2023 2023 Citations: 37
A Novel Methodology for Real-Time Face Mask Detection Using PSO Optimized CNN Technique A Nayyar, NG Nguyen, S Natani, A Sharma, S Vyas International Symposium on Integrated Uncertainty in Knowledge Modelling and … , 2023 2023
MOST CITED SCHOLAR PUBLICATIONS
Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model GN Nguyen, NH Le Viet, M Elhoseny, K Shankar, BB Gupta, ... Journal of parallel and distributed computing 153, 150-160 , 2021 2021 Citations: 374
Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network PK Mallick, SH Ryu, SK Satapathy, S Mishra, GN Nguyen, P Tiwari IEEE Access 7, 46278-46287 , 2019 2019 Citations: 349
Sound classification using convolutional neural network and tensor deep stacking network A Khamparia, D Gupta, NG Nguyen, A Khanna, B Pandey, P Tiwari Ieee Access 7, 7717-7727 , 2019 2019 Citations: 317
An effective training scheme for deep neural network in edge computing enabled Internet of medical things (IoMT) systems IV Pustokhina, DA Pustokhin, D Gupta, A Khanna, K Shankar, GN Nguyen IEEE Access 8, 107112-107123 , 2020 2020 Citations: 286
Privacy preserving blockchain technique to achieve secure and reliable sharing of IoT data B Le Nguyen, EL Lydia, M Elhoseny, IV Pustokhina, DA Pustokhin, ... Computers, Materials, & Continua 65 (1), 87 , 2020 2020 Citations: 179
The internet of drone things (IoDT): future envision of smart drones A Nayyar, BL Nguyen, NG Nguyen First International Conference on Sustainable Technologies for Computational … , 2019 2019 Citations: 171
Advances in swarm intelligence for optimizing problems in computer science A Nayyar, DN Le, NG Nguyen CRC press , 2018 2018 Citations: 153
An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding S Hore, S Chakraborty, S Chatterjee, N Dey, AS Ashour, VC Le, ... International Journal of Electrical and Computer Engineering (IJECE) 6 (6 … , 2016 2016 Citations: 150
Mitigation of black hole and gray hole attack using swarm inspired algorithm with artificial neural network P Rani, S Verma, GN Nguyen IEEE access 8, 121755-121764 , 2020 2020 Citations: 145
A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses GN Nguyen, LH Son, AS Ashour, N Dey International Journal of Machine Learning and Cybernetics 10 (1), 1-13 , 2019 2019 Citations: 139
BioSenHealth 1.0: a novel internet of medical things (IoMT)-based patient health monitoring system A Nayyar, V Puri, NG Nguyen International Conference on Innovative Computing and Communications … , 2018 2018 Citations: 119
Introduction to swarm intelligence A Nayyar, NG Nguyen Advances in swarm intelligence for optimizing problems in computer science … , 2018 2018 Citations: 90
Emerging technologies for health and medicine: virtual reality, augmented reality, artificial intelligence, internet of things, robotics, industry 4.0 DN Le, C Van Le, JG Tromp, GN Nguyen John Wiley & Sons , 2018 2018 Citations: 88
An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19 DA Pustokhin, IV Pustokhina, PN Dinh, SV Phan, GN Nguyen, GP Joshi, ... Journal of Applied Statistics 50 (3), 477-494 , 2023 2023 Citations: 81
Efficient dual-cooperative bait detection scheme for collaborative attackers on mobile ad-hoc networks OI Khalaf, F Ajesh, AA Hamad, GN Nguyen, DN Le IEEE Access 8, 227962-227969 , 2020 2020 Citations: 80
Cloud computing and virtualization DN Le, R Kumar, GN Nguyen, JM Chatterjee John Wiley & Sons , 2018 2018 Citations: 74
Light microscopy image de-noising using optimized LPA-ICI filter AS Ashour, S Beagum, N Dey, AS Ashour, DS Pistolla, GN Nguyen, ... Neural Computing and Applications 29 (12), 1517-1533 , 2018 2018 Citations: 69
Smart surveillance robot for real-time monitoring and control system in environment and industrial applications A Nayyar, V Puri, NG Nguyen, DN Le Information Systems Design and Intelligent Applications: Proceedings of … , 2018 2018 Citations: 65
Deep feature extraction for panoramic image stitching VD Hoang, DP Tran, NG Nhu, TA Pham, VH Pham Asian Conference on Intelligent Information and Database Systems, 141-151 , 2020 2020 Citations: 58
Blockchain enabled energy efficient red deer algorithm based clustering protocol for pervasive wireless sensor networks GN Nguyen, NH Le Viet, AFS Devaraj, R Gobi, K Shankar Sustainable Computing: Informatics and Systems 28, 100464 , 2020 2020 Citations: 55
