Dac-Nhuong Le

@dhhp.edu.vn

Faculty of Information Technology
Haiphong University, Haiphong, Vietnam

Dac-Nhuong Le


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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

Research on developing a search system (HPUMind) to support teaching and learning in Information Technology, integrated circuit design, and semiconductors at Hai Phong University (ĐT.XH.2025.980).

This study assesses the current state of technologies used for information retrieval and teaching/learning support in Information Technology, integrated circuit design, and semiconductor engineering at Hai Phong University. It researches the development of a search system (HPUmind) and the requirements for building and operating a system to support teaching and learning in Information Technology, integrated circuit design, and semiconductor engineering at Hai Phong University. The study also includes a pilot test of the HPUmind system to support teaching and learning in Information Technology, integrated circuit design, and semiconductor engineering at Hai Phong University. Finally, it evaluates and refines the HPUmind system and proposes its replication at other universities and colleges in Hai Phong city.


Applications Invited
ĐT.XH.2025.980 HPUMind
172

Scopus Publications

6004

Scholar Citations

43

Scholar h-index

109

Scholar i10-index

Scopus Publications

  • An integrated framework for outcome based education and AI supported blended learning in curriculum redesign and intelligent training management
    Trung-Nghia Phung, Dinh-Cuong Do, Toan-Thang Nguyen, Van-Su Nguyen, The-Vinh Nguyen, et al.
    Discover Computing, 2026
  • DMA-D2-UNet: A Scalable Solution for Precise Semantic Segmentation
    Journal of Information Science and Engineering, 2026
  • 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
  • Bioengineering and IoT: Shaping the Future of Healthcare
    Jayashree Katti, Atul Kathole, Dac-Nhuong Le
    Bioengineering and Iot Shaping the Future of Healthcare, 2026
  • 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
  • AI-Based fault detection and predictive maintenance for power transmission networks in remote areas using catboost for enhanced reliability
    Dac-Nhuong Le, Bakri Hossain Awaji, Osamah AlDhafer, Khalid Alkhattabi, Mohamed Ghouse, et al.
    Intelligent Data Analysis, 2026
    Power transmission network plays a crucial role in maintaining the stability and reliability of electrical systems. Fault detection and predictive maintenance are essential to ensure continuous operation and minimize downtimes, but traditional fault detection methods face challenges, particularly in remote areas where manual inspections are impractical. This paper presents a framework to enhance the steadiness and efficacy of power transmission networks through advanced fault detection and predictive maintenance. The proposed framework begins with data collection from power transmission sensors, including voltage, current, and temperature readings, along with historical fault records. Next, data pre-processing is performed using median imputation to handle missing values and categorical encoding to transform non-numeric data into numerical form. Feature extraction follows, where time-domain features like Peak-to-Peak Value, RMS, and Zero-Crossing Rate are computed to detect potential faults. The CatBoost model is then trained on the extracted features, and hyperparameter optimization is conducted using the Coati Optimization Algorithm. Once trained, the model performs fault detection and prediction, identifying faults such as Transformer Failures, Overheating, and Line Breakages. The model is assessed using metrics like accuracy of 99.42%, precision of 99.37%, recall of 99.40%, and F1-score of 99.38%. The framework achieves high performance in detecting faults and can be deployed in power transmission systems for proactive maintenance, reducing reliance on manual inspections, improving system reliability, and addressing challenges in remote locations.
  • 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, et al.
    Recent Advances in Computer Science and Communications, 2026
    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.
  • Next-Generation Data-Driven Business 4.0 using the Internet of Things, Blockchain, and Interconnected Devices
    Hemant Kumar Saini, Rupali A. Mahajan, Dac-Nhuong Le
    Next Generation Data Driven Business 4 0 Using the Internet of Things Blockchain and Interconnected Devices, 2025
  • Optimized XGBoost Model with Whale Optimization Algorithm for Detecting Anomalies in Manufacturing
    Surjeet Dalal, Uma Rani, Umesh Kumar Lilhore, Neeraj Dahiya, Reenu Batra, et al.
    Journal of Computational and Cognitive Engineering, 2025
    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, Dac-Nhuong Le
    SN Computer Science, 2025
  • 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
  • A Combine Solution for Online Exams Cheating Detection, Prediction, and Prevention Using Artificial Intelligence
    Dao Phuc Minh Huy, Nguyen Gia Nhu, Dac-Nhuong Le
    Lecture Notes in Networks and Systems, 2025
  • Green Innovations in Computational Intelligence: Sustainable Strategies and Emerging Technologies
    Green Innovations in Computational Intelligence Sustainable Strategies and Emerging Technologies, 2025
  • Preface
    Green Innovations in Computational Intelligence Sustainable Strategies and Emerging Technologies, 2025
  • Applications of the Internet of Things and Data Science for Sustainable Development
    Noor Zaman Jhanjhi, Mohit Gambhir, Brojo Kishore Mishra, Dac-Nhuong Le
    Recent Advances in Computer Science and Communications, 2025
  • Recognition of Wh-Question Sign Gestures in Video Streams using an Attention Driven C3D-BiLSTM Network
    Arnab Dey, Samit Biswas, Dac-Nhuong Le
    Procedia Computer Science, 2024
  • Preface
    Smart Sensors for Industry 4 0 Fundamentals Fabrication and Iiot Applications, 2024
  • 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
  • Preface
    Applications of Blockchain and Artificial Intelligence in Finance and Governance, 2024
  • Foreword
    Smart Sensors for Industry 4 0 Fundamentals Fabrication and Iiot Applications, 2024
  • Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network
    Arnab Dey, Samit Biswas, Dac-Nhuong Le
    Computers Materials and Continua, 2024
  • Analysis of Multi-Join Query Optimization Using ACO and Q-Learning
    M. P.Karthikeyan, K. Krishnaveni, Dac-Nhuong Le
    International Journal of Computing and Digital Systems, 2024
  • Applications of Blockchain and Artificial Intelligence in Finance and Governance
    A M Viswa Bharathy, Dac-Nhuong Le, P. Karthikeyan
    Applications of Blockchain and Artificial Intelligence in Finance and Governance, 2024
  • Smart Sensors for Industry 4.0: Fundamentals, Fabrication and IIoT Applications
    Smart Sensors for Industry 4 0 Fundamentals Fabrication and Iiot Applications, 2024
  • The Need for XAI: Challenges and Its Applications
    Swati, Menu Vijarania, Vivek Jaglan, Dac‐Nhuong Le
    Reshaping Intelligent Business and Industry Convergence of AI and Iot at the Cutting Edge, 2024

RECENT SCHOLAR PUBLICATIONS

  • AI-Based fault detection and predictive maintenance for power transmission networks in remote areas using catboost for enhanced reliability
    DN Le, BH Awaji, O AlDhafer, K Alkhattabi, M Ghouse, E Muniyandy
    Intelligent Data Analysis, 1088467X261446579 , 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
  • Merging Quantum Cloning and Blockchain Solutions for Health Informatics
    DN Le, A Kumar, P Dixit, PS Rathore
    IGI Global , 2026
    2026
  • An integrated framework for outcome based education and AI supported blended learning in curriculum redesign and intelligent training management
    TN Phung, DC Do, TT Nguyen, VS Nguyen, TV Nguyen, DN Le
    Discover Computing 29 (1), 196 , 2026
    2026
    Citations: 2
  • Automatic Test Case Generation for XACML Access Control Policies Using Graph-Based Modeling and Genetic Algorithms
    TB Trinh, CN Van, NM Le, DN Le
    VNU Journal of Science: Computer Science and Communication Engineering 42 (1) , 2026
    2026
  • A SMART WERABLE GAS DETECTION, MEASUREMENT AND ALERT DEVICE
    DK SINGH, DACN LE, P RAJA, PK MALIK, A GANESH, PK SHARMA
    IN Patent 582,588 , 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
  • Future Trends in Blockchain and EOG-Based Systems for Higher Education
    RSS Nehru, DN Le
    Semantic Annotation of Edge/Fog Services With Blockchain Integration, 405-432 , 2026
    2026
  • DMA-D2-UNet: A Scalable Solution for Precise Semantic Segmentation.
    A DEY, S BISWAS, DACN LE
    Journal of Information Science & Engineering 42 (1) , 2026
    2026
    Citations: 1
  • Bioengineering and IoT Shaping the Future of Healthcare
    J Katti, A Kathole, DN Le
    CRC Taylor & Francis. ISBN 9781032903156 , 2026
    2026
    Citations: 1
  • Next-Generation Data-Driven Business 4.0 using the Internet of Things, Blockchain, and Interconnected Devices
    HK Saini, RA Mahajan, DN Le
    CRC Press. ISBN 9781032941066 , 2026
    2026
  • Green Computational Intelligence: Sustainable Strategies and Emerging Technologies
    N Pathak, N Sharma, M Sharma, DN Le
    John Wiley & Sons , 2025
    2025
  • Quantum Protocols in Blockchain Security
    A Kumar, P Batta, DN Le
    Blockchain Technologies, Springer. ISBN 978-981-96-9147-0 , 2025
    2025
  • Computational Optimization: Machine Learning and Fuzzy Systems
    N Kaur, B Kaur, Y Gulzar, DN Le
    De Gruyter , 2025
    2025
  • Applications of the Internet of Things and Data Science for Sustainable Development
    NZ Jhanjhi, BK Mishra, M Gambhir, DN Le
    Recent Advances in Computer Science and Communications 18 (6), E26662558412956 , 2025
    2025
  • Exploring the Impact of Extended Reality (XR) Technologies on Promoting Environmental Sustainability
    SK Gupta, N Maurya, DN Le, T Mzili
    Information Systems Engineering and Management (ISEM, volume 38). Springer … , 2025
    2025
    Citations: 1
  • 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 1302, 665-676 , 2025
    2025
    Citations: 4
  • Recent Advancements in Computational Intelligence and Design Engineering
    DN Le, A Dhar, R Kumar, S Muthaiyah, S Adhikari
    CRC Press. ISBN 9781032980355 , 2025
    2025
    Citations: 1
  • Optimized XGBoost Hyper-Parameter Tuned Model with Krill Herd Algorithm (KHA) for Accurate Drinking Water Quality Prediction
    N Malik, A Kalonia, S Dalal, DN Le
    SN Computer Science 6 (2025), 263 , 2025
    2025
    Citations: 13

MOST CITED SCHOLAR PUBLICATIONS

  • Virtual Reality (VR) & Augmented Reality (AR) technologies for tourism and hospitality industry
    A Nayyar, B Mahapatra, D Le, G Suseendran
    International Journal of Engineering & Technology 7 (2.21), 156-160 , 2018
    2018
    Citations: 340
  • Integrating encryption techniques for secure data storage in the cloud
    B Seth, S Dalal, V Jaglan, DN Le, S Mohan, G Srivastava
    Transactions on Emerging Telecommunications Technologies 33 (4), e4108 , 2022
    2022
    Citations: 281
  • IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification
    DN Le, VS Parvathy, D Gupta, A Khanna, JJPC Rodrigues, K Shankar
    International Journal of Machine Learning and Cybernetics 2021, 1-14 , 2021
    2021
    Citations: 241
  • LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities
    DK Singh, R Sobti, A Jain, PK Malik, DN Le
    IET communications 16 (5), 604-618 , 2022
    2022
    Citations: 170
  • Capitalizing on big data and revolutionary 5G technology: Extracting and visualizing ratings and reviews of global chain hotels
    L Gaur, A Afaq, A Solanki, G Singh, S Sharma, NZ Jhanjhi, HT My, DN Le
    Computers and Electrical Engineering 95, 107374 , 2021
    2021
    Citations: 155
  • Advances in Swarm Intelligence for Optimizing Problems in Computer Science
    A Nayyar, DN Le, NG Nguyen
    ISBN 9781138482517. 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
    International Journal of Electrical & Computer Engineering (2088-8708) 6 (6) , 2016
    2016
    Citations: 150
  • Internet of nano things (IoNT): Next evolutionary step in nanotechnology
    A Nayyar, V Puri, DN Le
    Nanoscience and Nanotechnology 7 (1), 4-8 , 2017
    2017
    Citations: 142
  • COVID-DeepNet: hybrid multimodal deep learning system for improving COVID-19 pneumonia detection in chest X-ray images
    AS Al-Waisy, MA Mohammed, S Al-Fahdawi, MS Maashi, ...
    Computers, Materials and Continua 67 (2), 2409-2429 , 2021
    2021
    Citations: 137
  • A comparative analysis of machine learning models for banking news extraction by multiclass classification with imbalanced datasets of financial news: challenges and solutions
    V Dogra, S Verma, K Verma, NZ Jhanjhi, U Ghosh, DN Le
    IJIMAI 7 (3), 35-52 , 2022
    2022
    Citations: 120
  • A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of COVID-19 based on X-ray images
    DN Le
    Computers, Materials & Continua , 2021
    2021
    Citations: 101
  • A comprehensive study on the role of advanced technologies in 5G based smart hospital
    A Kumar, R Dhanagopal, MA Albreem, DN Le
    AEJ Alexandria Engineering Journal 60 (6), 5527-5536 , 2021
    2021
    Citations: 95
  • 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: 94
  • Global forecasting confirmed and fatal cases of COVID-19 outbreak using autoregressive integrated moving average model
    D Dansana, R Kumar, J Das Adhikari, M Mohapatra, R Sharma, ...
    Frontiers in public health 8, 580327 , 2020
    2020
    Citations: 93
  • 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: 92
  • RETRACTED ARTICLE: An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks: S. Dalal et al.
    S Dalal, B Seth, V Jaglan, M Malik, Surbhi, N Dahiya, U Rani, DN Le, ...
    Soft Computing 26 (11), 5377-5388 , 2022
    2022
    Citations: 88
  • Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm
    S Chatterjee, S Sarkar, S Hore, N Dey, AS Ashour, F Shi, DN Le
    Structural Engineering and Mechanics 63 (4), 429-438 , 2017
    2017
    Citations: 87
  • 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
  • Cyber Security in Parallel and Distributed Computing: Concepts, Techniques, Applications and Case Studies
    DN Le, R Kumar, BK Mishra, JM Chatterjee, M Khari
    Wiley, ISBN: 978-1-119-48805-7 , 2019
    2019
    Citations: 78
  • Synchronization phenomena investigation of A new nonlinear dynamical system 4-D by gardano’s and lyapunov’s methods
    DN Le
    Computers, Materials & Continua , 2021
    2021
    Citations: 77