Dac-Nhuong Le

Dac-Nhuong Le (Lê Đắc Nhường) received the M.Sc. (2009) and PhD (2015) degrees in Computer Science from Vietnam National University, Vietnam. He is currently an Associate Professor of Computer Science and Head of the Faculty of Information Technology at Haiphong University, Vietnam. With over 20 years of academic teaching and research experience, he has published extensively in reputable international journals and conferences and has contributed numerous book chapters. His publications are indexed in major scholarly databases, including WoS, Scopus, ACM, and DBLP. His research interests include soft computing, network communications, cybersecurity (security and vulnerability assessment), network performance analysis and simulation, cloud computing, the Internet of Things (IoT), and biomedical image processing, with a primary focus on network security, soft computing, IoT, and biomedical imaging. He has served on technical program committees, as a reviewer, and as a trac

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Networks and Communications, Artificial Intelligence, Computational Theory and Mathematics

FUTURE PROJECTS

Scopus Publications

DMA-D2-UNet: A Scalable Solution for Precise Semantic Segmentation




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, and Dac-Nhuong Le
Tech Science Press



Next-Generation Data-Driven Business 4.0 using the Internet of Things, Blockchain, and Interconnected Devices
Hemant Kumar Saini, Rupali A. Mahajan, and Dac-Nhuong Le
CRC Press



Optimized XGBoost Model with Whale Optimization Algorithm for Detecting Anomalies in Manufacturing
Surjeet Dalal, Uma Rani, Umesh Kumar Lilhore, Neeraj Dahiya, Reenu Batra, Nasratullah Nuristani, and Dac-Nhuong Le
BON VIEW PUBLISHING PTE
Anomalies and defects in the manufacturing process hinder operating efficiency and product quality. The Whale Optimization Algorithm (WOA) optimizes the XGBoost model for better anomaly identification by iteratively refining hyperparameters. Experiments using real-world manufacturing datasets prove proposed model works. Comparing the proposed model to traditional anomaly detection methods shows its superior performance in industry patent concept. The optimized XGBoost model's interpretability and anomaly detection features are also discussed. In this paper, WOA is applied in this work to optimize hyperparameters of XGBoost, a robust gradient boosting technique for accurate anomaly detection in manufacturing systems. Optimized XGBoost gained 1.00 precision value, 0.9 recall value, and 0.96 f1-score for class 0.0 and gained a 0.95 precision value, 1.00 recall value, and a 0.97 f1-score for class 1.0. The proposed model gained 0.993 Train Score and 0.964 Test Score. Our findings suggest that integrating XGBoost with the WOA may uncover manufacturing process irregularities. Optimization improves detection accuracy and provides a flexible and interpretable framework, helping modern industrial processes maintain quality and efficiency. This research encourages machine learning optimization for industrial patent applications, advancing anomaly detection methods.   Received: 2 June 2024 | Revised: 29 August 2024 | Accepted: 27 September 2024   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.   Data Availability Statement Data are available on request from the corresponding author upon reasonable request.   Author Contribution Statement Surjeet Dalal: Conceptualization, Validation, Writing – original draft, Project administration. Uma Rani: Conceptualization, Formal analysis, Writing – review & editing. Umesh Kumar Lilhore: Methodology, Investigation, Resources, Writing – original draft. Neeraj Dahiya: Methodology, Data curation, Writing - review & editing. Reenu Batra: Software, Visualization, Supervision. Nasratullah Nuristani: Software, Formal analysis, Investigation, Visualization. Dac-Nhuong Le: Validation, Supervision, Project administration.


Optimized XGBoost Hyper-Parameter Tuned Model with Krill Herd Algorithm (KHA) for Accurate Drinking Water Quality Prediction
Nikhil Malik, Arpna Kalonia, Surjeet Dalal, and Dac-Nhuong Le
Springer Science and Business Media LLC



Deep learning approaches for predicting cheating from student exam results: a comparative study under imbalanced data conditions
Dao Phuc Minh Huy, Nguyen Gia Nhu, and Dac-Nhuong Le
Emerald
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.


A Combine Solution for Online Exams Cheating Detection, Prediction, and Prevention Using Artificial Intelligence
Dao Phuc Minh Huy, Nguyen Gia Nhu, and Dac-Nhuong Le
Springer Nature Singapore



Green Innovations in Computational Intelligence: Sustainable Strategies and Emerging Technologies
Wiley



Preface




Applications of the Internet of Things and Data Science for Sustainable Development
Noor Zaman Jhanjhi, Mohit Gambhir, Brojo Kishore Mishra, and Dac-Nhuong Le
Bentham Science Publishers Ltd.



A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities
Surjeet Dalal, Neeraj Dahiya, Amit Verma, Neetu Faujdar, Sarita Rathee, Vivek Jaglan, Uma Rani, and Dac-Nhuong Le
Bentham Science Publishers Ltd.
Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data preprocessing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings.


Recognition of Wh-Question Sign Gestures in Video Streams using an Attention Driven C3D-BiLSTM Network
Arnab Dey, Samit Biswas, and Dac-Nhuong Le
Elsevier BV



Preface




Preface




CNN-FSPM-Based Fingerprint Indexing and Matching for Detecting, Predicting, and Preventing Cheating in Online Examinations
Dao Phuc Minh Huy, Nguyen Gia Nhu, and Dac-Nhuong Le
IGI Global
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.


Applications of Blockchain and Artificial Intelligence in Finance and Governance
A M Viswa Bharathy, Dac-Nhuong Le, and P. Karthikeyan
CRC Press



Foreword




Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network
Arnab Dey, Samit Biswas, and Dac-Nhuong Le
Tech Science Press



Analysis of Multi-Join Query Optimization Using ACO and Q-Learning
M. P.Karthikeyan, K. Krishnaveni, and Dac-Nhuong Le
Deanship of Scientific Research



Smart Sensors for Industry 4.0: Fundamentals, Fabrication and IIoT Applications
Wiley



Artificial intelligence in wireless communication
Hemang A. Thakar, Vidisha Pradhan, Dac-Nhuong Le, and Pankaj Jain
CRC Press



The Need for XAI: Challenges and Its Applications
Swati, Menu Vijarania, Vivek Jaglan, and Dac‐Nhuong Le
Wiley



MAPPING OF E-WALLETS WITH FEATURES
Alisha Sikri, Surjeet Dalal, N.P Singh, and Dac‐Nhuong Le
Wiley



Preface




Machine Learning Approach for Predicting the Price of Used Cars
Swati, Meenu Vijarania, Akshat Agarwal, and Dac‐Nhuong Le
Wiley

RECENT SCHOLAR PUBLICATIONS

Publications

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)