Dac-Nhuong Le


Faculty of Information Technology
Haiphong University, Haiphong, Vietnam



Dac-Nhuong Le (Lê Đắc Nhường) has a M.Sc. and Ph.D in computer science from Vietnam National University, Vietnam in 2009 and 2015, respectively. He is Associate Professor in Computer Science, Deputy-Head of Faculty of Information Technology, Haiphong University, Vietnam. Presently, he is also the Vice-Director of Information Technology Apply and Foreign Language Training Center in the same university.

He has a total academic teaching experience of 12 years with many publications in reputed international conferences, journals and online book chapter contributions (Indexed By: SCI, SCIE, SSCI, Scopus, ACM, DBLP). His area of research include: Soft computing, Network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT and Image processing in biomedical.

His core work in network security, soft computing and IoT and image processing in biomedical. Recently, he has been the technique program committee, the technique reviews, the track chair for international conferences: FICTA 2014, CSI 2014, IC4SD 2015, ICICT 2015, INDIA 2015, IC3T 2015, INDIA 2016, FICTA 2016, ICDECT 2016, IUKM 2016, INDIA 2017, FICTA 2017, CISC 2017, ICICC 2018, ICCUT 2018, FICTA 2018 under Springer-ASIC/LNAI Series.

Presently, he is serving in the editorial board of international journals and he authored/edited 12 computer science books by Springer, Wiley, CRC Press.

email: nhuongld@


Computer Science, Computer Networks and Communications


Research on Some Optimization Algorithms for Risk and Conflict Management in Software Project Scheduling (NAFOSTED 102.03-2019.10)

Risks and conflicts are subjective events that interfere with the development of software projects. Because risks and conflicts cannot be completely eliminated during the project schedule due to complexity arising from unique characteristics, variability, lack of data, structure, and deviation in prediction/estimation. Many different techniques and tools have been developed to support better project scheduling, but the quantification of risk factors and conflicts has not been adequately considered. In it, the most challenging problem is estimating the time and resources for each specific task in project scheduling. Most research on software project risk analysis focuses on finding the link between risk factors and project outcomes. The goal of risk and conflict management problems in software projects is to provide a multi-objective optimization plan to manage and minimize its level of damage. Therefore, it is almost impossible to find exact algorithms in polynomial time. Then, we need to consider the design of optimal algorithms with the best approximation to help accurately predict, quantify risks, conflicts, as well as their consequences, impact on the project from the critical process, is project planning.

Applications Invited

Scopus Publications


Scholar Citations


Scholar h-index


Scholar i10-index

Scopus Publications

  • RETRACTED ARTICLE: Utilizing Index-Based Periodic High Utility Mining to Study Frequent Itemsets
    Roy Setiawan, Dac-Nhuong Le, Regin Rajan, Thirukumaran Subramani, Dilip Kumar Sharma, Vidya Sagar Ponnam, Kailash Kumar, Syed Musthafa Akbar Batcha, Pankaj Dadheech, and Sudhakar Sengan

    Springer Science and Business Media LLC

  • Recognition of Human Interactions in Still Images using AdaptiveDRNet with Multi-level Attention
    Arnab Dey, Samit Biswas, and Dac-Nhoung Le

    The Science and Information Organization
    —Human-Human Interaction Recognition (H2HIR) is a multidisciplinary field that combines computer vision, deep learning, and psychology. Its primary objective is to decode and understand the intricacies of human-human interactions. H2HIR holds significant importance across various domains as it enables machines to perceive, comprehend, and respond to human social behaviors, gestures, and communication patterns. This study aims to identify human-human interactions from just one frame, i.e. from an image. Diverging from the realm of video-based interaction recognition, a well-established research domain that relies on the utilization of spatio-temporal information, the complexity of the task escalates significantly when dealing with still images due to the absence of these intrinsic spatio-temporal features. This research introduces a novel deep learning model called AdaptiveDRNet with Multi-level Attention to recognize Human-Human (H2H) interactions. Our proposed method demonstrates outstanding performance on the Human-Human Interaction Image dataset (H2HID), encompassing 4049 meticulously curated images representing fifteen distinct human interactions and on the publicly accessible HII and HIIv2 related benchmark datasets. Notably, our proposed model excels with a validation accuracy of 97.20% in the classification of human-human interaction images, surpassing the performance of EfficientNet, InceptionResNetV2, NASNet Mobile, ConvXNet, ResNet50, and VGG-16 models. H2H interaction recognition’s significance lies in its capacity to enhance communication, improve decision-making, and ultimately contribute to the well-being and efficiency of individuals and society as a whole.

  • An Overview of Healthcare Policy in India for Designing New Customised Health Services for the Patient
    Akshaya Nidhi Bhati, Arun Kumar, Mehedi Masud, and Dac-Nhuong Le

    CRC Press

  • Preface

  • Evolving Networking Technologies: Developments and Future Directions

  • Foreword

  • Data Communication and Information Exchange in Distributed IoT Environment: Issues and Their Solutions

  • Smart Autonomous Breaking System for Vehicles to Improve Road Safety in India
    Praveen Kumar Malik, Abdul Rahim, and Dac-Nhuong Le

    Springer Nature Singapore

  • Preface

  • The State of CDNs Today and What AI-Assisted CDN Means for the Future

  • Deep Learning Based Automated Chest X-ray Abnormalities Detection
    Vraj Parikh, Jainil Shah, Chintan Bhatt, Juan M Corchado, and Dac-Nhuong Le

    Springer International Publishing

  • A Comparative Analysis of Performances of Different Ensemble Approaches for Classification of Android Malwares
    Abhishek Bhattacharya, Soumi Dutta, Mohammad Kamrul Hasan, Kusum Yadav, Dac-Nhuong Le, and Pastor Arguelles

    Springer Nature Singapore

  • A Deep Trash Classification Model on Raspberry Pi 4
    Thien Khai Tran, Kha Tu Huynh, Dac-Nhuong Le, Muhammad Arif, and Hoa Minh Dinh

    Computers, Materials and Continua (Tech Science Press)

  • A Novel Design of Morlet Wavelet to Solve the Dynamics of Nervous Stomach Nonlinear Model
    Zulqurnain Sabir, Muhammad Asif Zahoor Raja, S. R. Mahmoud, Mohammed Balubaid, Ali Algarni, Abdulaziz H. Alghtani, Ayman A. Aly, and Dac-Nhuong Le

    Springer Science and Business Media LLC
    AbstractThe present study introduces a novel design of Morlet wavelet neural network (MWNN) models to solve a class of a nonlinear nervous stomach system represented with governing ODEs systems via three categories, tension, food and medicine, i.e., TFM model. The comprehensive detail of each category is designated together with the sleep factor, food rate, tension rate, medicine factor and death rate are also provided. The computational structure of MWNNs along with the global search ability of genetic algorithm (GA) and local search competence of active-set algorithms (ASAs), i.e., MWNN-GA-ASAs is applied to solve the TFM model. The optimization of an error function, for nonlinear TFM model and its related boundary conditions, is performed using the hybrid heuristics of GA-ASAs. The performance of the obtained outcomes through MWNN-GA-ASAs for solving the nonlinear TFM model is compared with the results of state of the article numerical computing paradigm via Adams methods to validate the precision of the MWNN-GA-ASAs. Moreover, statistical assessments studies for 50 independent trials with 10 neuron-based networks further authenticate the efficacy, reliability and consistent convergence of the proposed MWNN-GA-ASAs.

  • Adaptive Neural Backstepping Control Approach for Tracker Design of Wheelchair Upper-Limb Exoskeleton Robot System
    Ayman A. Aly, Kuo-Hsien Hsia, Fayez F. M. El-Sousy, Saleh Mobayen, Ahmed Alotaibi, Ghassan Mousa, and Dac-Nhuong Le

    In this study, the desired tracking control of the upper-limb exoskeleton robot system under model uncertainty and external disturbance is investigated. For this reason, an adaptive neural network using a backstepping control strategy is designed. The difference between the actual values of the upper-limb exoskeleton robot system and the desired values is considered as the tracking error. Afterward, the auxiliary variable based on the tracking error is defined and the virtual control input is obtained. Then, by using the backstepping control procedure and Lyapunov stability concept, the convergence of the position tracking error is proved. Moreover, for the compensation of the model uncertainty and the external disturbance that exist in the upper-limb exoskeleton robot system, an adaptive neural-network procedure is adopted. Furthermore, for the estimation of the unknown coefficient related to the parameters of the neural network, the adaptive law is designed. Finally, the simulation results are prepared for demonstration of the effectiveness of the suggested method on the upper-limb exoskeleton robot system.

  • Adaptive Neural Network-Based Fixed-Time Tracking Controller for Disabilities Exoskeleton Wheelchair Robotic System
    Ayman A. Aly, Mai The Vu, Fayez F. M. El-Sousy, Kuo-Hsien Hsia, Ahmed Alotaibi, Ghassan Mousa, Dac-Nhuong Le, and Saleh Mobayen

    In this paper, an adaptive neural network approach is developed based on the integral nonsingular terminal sliding mode control method, with the aim of fixed-time position tracking control of a wheelchair upper-limb exoskeleton robot system under external disturbance. The dynamical equation of the upper-limb exoskeleton robot system is obtained using a free and typical model of the robotic manipulator. Afterward, the position tracking error between the actual and desired values of the upper-limb exoskeleton robot system is defined. Then, the integral nonsingular terminal sliding surface based on tracking error is proposed for fixed-time convergence of the tracking error. Furthermore, the adaptive neural network procedure is proposed to compensate for the external disturbance which exists in the upper-limb exoskeleton robotic system. Finally, to demonstrate the effectiveness of the proposed method, simulation results using MATLAB/Simulink are provided.

  • Fuzzy-Based Fixed-Time Nonsingular Tracker of Exoskeleton Robots for Disabilities Using Sliding Mode State Observer
    Ayman A. Aly, Mai The Vu, Fayez F. M. El-Sousy, Ahmed Alotaibi, Ghassan Mousa, Dac-Nhuong Le, and Saleh Mobayen

    In this article, the position tracking control of the wheelchair upper-limb exoskeleton robotic system is investigated with the aim of rehabilitation of disabled people. Hence, the fuzzy nonsingular terminal sliding mode control method by using the state observer with a fixed-time convergence rate is designed in three main parts. In the first part, the fixed-time state observer is proposed for estimation of the states of the system. Secondly, the fixed-time convergence of position tracking error of the upper-limb exoskeleton robot system is examined by using the nonsingular terminal sliding mode control approach. In the third part, with the target of the improvement of the controller performance for removal of the chattering phenomenon which diminishes the controller performance, the fuzzy control method is used. Finally, the efficiency and proficiency of the proposed control method on the upper limb exoskeleton robotic system are demonstrated via the simulation results which are provided by MATLAB/Simulink software. In this part, simulation results are obtained based on different initial conditions in two examples using various desired values. Thus, it can be demonstrated that the proposed method applied to the upper-limb exoskeleton robot system is robust under various initial conditions and desired values.

  • Multi-Criteria Service Selection Agent for Federated Cloud
    S. Sudhakar, B. L. Radhakrishnan, P. Karthikeyan, K. Martin Sagayam, and Dac-Nhuong Le

    Croatian Communications and Information Society

  • Towards aspect based requirements mining for trace retrieval of component-based software management process in globally distributed environment
    Sadia Ali, Yaser Hafeez, Mamoona Humayun, N. Z. Jhanjhi, and Dac-Nhuong Le

    Springer Science and Business Media LLC

  • A neuro-swarming intelligent heuristic for second-order nonlinear Lane–Emden multi-pantograph delay differential system
    Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Dac-Nhuong Le, and Ayman A. Aly

    Springer Science and Business Media LLC
    AbstractThe current study is related to present a novel neuro-swarming intelligent heuristic for nonlinear second-order Lane–Emden multi-pantograph delay differential (NSO-LE-MPDD) model by applying the approximation proficiency of artificial neural networks (ANNs) and local/global search capabilities of particle swarm optimization (PSO) together with efficient/quick interior-point (IP) approach, i.e., ANN-PSOIP scheme. In the designed ANN-PSOIP scheme, a merit function is proposed by using the mean square error sense along with continuous mapping of ANNs for the NSO-LE-MPDD model. The training of these nets is capable of using the integrated competence of PSO and IP scheme. The inspiration of the ANN-PSOIP approach instigates to present a reliable, steadfast, and consistent arrangement relates the ANNs strength for the soft computing optimization to handle with such inspiring classifications. Furthermore, the statistical soundings using the different operators certify the convergence, accurateness, and precision of the ANN-PSOIP scheme.

  • An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks
    Surjeet Dalal, Bijeta Seth, Vivek Jaglan, Meenakshi Malik, Surbhi, Neeraj Dahiya, Uma Rani, Dac-Nhuong Le, and Yu-Chen Hu

    Springer Science and Business Media LLC

  • Integrating encryption techniques for secure data storage in the cloud
    Bijeta Seth, Surjeet Dalal, Vivek Jaglan, Dac‐Nhuong Le, Senthilkumar Mohan, and Gautam Srivastava


  • LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities
    Dushyant Kumar Singh, Rajeev Sobti, Anuj Jain, Praveen Kumar Malik, and Dac‐Nhuong Le

    Institution of Engineering and Technology (IET)

  • Preface


  • Quantum Machine Learning: Quantum Algorithms and Neural Networks
    P Raj, HH Song, DN Le, N Vyas
    Publisher: De Gruyter. ISBN: 9783111342092 2025

  • Smart Sensors for Industry 4.0: Fundamentals, Fabrication and IIoT Applications
    BK Mishra, S Mallik, DN Le
    John Wiley & Sons. ISBN: 978-1-394-21356-6 2024

  • A Method of Integrating Length Constraints into Encoder-Decoder Transformer for Abstractive Text Summarization
    NK Nguyen, DN Le, VH Nguyen, AC Le
    Intelligent Automation & Soft Computing 38 (1), 1-18 2024

  • An Overview of Healthcare Policy in India for Designing New Customised Health Services for the Patient
    AN Bhati, A Kumar, M Masud, DN Le
    5G-Based Smart Hospitals and Healthcare Systems: Evaluation, Integration 2023

  • Recognition of Human Interactions in Still Images using AdaptiveDRNet with Multi-level Attention
    A Dey, S Biswas, DN Le
    International Journal of Advanced Computer Science and Applications 14 (10 2023

  • Fuzzy Logic Applications in Computer Science and Mathematics
    R Kar, DN Le, G Mukherjee, BB Mallik, AK Shaw
    John Wiley & Sons 2023

  • Deep Learning for Healthcare Services IoT and Big Data Analytics
    P Nand, V Jain, DN Le, JM Chatterjee, R Kannan, AS Verma
    Bentham Science Publishers 2023

  • Recent Trends in Computational Intelligence and Its Application: Proceedings of the 1st International Conference on Recent Trends in Information Technology and its Application
    D Sugumaran, S Pal, DN Le, NZ Jhanjhi
    CRC Press 2023

  • Smart Autonomous Breaking System for Vehicles to Improve Road Safety in India
    PK Malik, A Rahim, DN Le
    Micro-Electronics and Telecommunication Engineering: Proceedings of 6th 2023

  • Evolving Networking Technologies: Developments and Future Directions
    KP Sharma, S Gupta, A Sharma, DN Le
    John Wiley & Sons 2023

  • Data Communication and Information Exchange in Distributed IoT Environment: Issues and Their Solutions
    R Jain, KP Sharma, R Majumdar, DN Le
    Evolving Networking Technologies: Developments and Future Directions, 41-54 2023

  • The State of CDNs Today and What AI‐Assisted CDN Means for the Future
    D Sarkar, R Majumdar, DN Le
    Evolving Networking Technologies: Developments and Future Directions, 97-110 2023

  • A Comparative Analysis of Performances of Different Ensemble Approaches for Classification of Android Malwares
    A Bhattacharya, S Dutta, MK Hasan, K Yadav, DN Le, P Arguelles Jr
    Emerging Technologies in Data Mining and Information Security: Proceedings 2023

  • A Framework for Hybrid WBSN-VANET-based Health Monitoring Systems
    P Singh, RS Raw, DN Le
    Computational Intelligent Security in Wireless Communications, 51-62 2023

  • A Deep Trash Classification Model on Raspberry Pi 4.
    TK Tran, KT Huynh, DN Le, M Arif, HM Dinh
    Intelligent Automation & Soft Computing 35 (2) 2023

  • John Colaco and Rajesh B. Lohani
    DN Le
    Printed Antennas: Design and Challenges, 187 2022

  • Design and study of compact bio-inspired-shaped smart MIMO array antenna for 5G-enabled healthcare systems, IoT systems, and environmental care systems
    J Colaco, RB Lohani, DN Le
    Printed Antennas, 187-218 2022

  • Nucleus Segmentation Using K-Means Clustering for Analysis of Microscopy Images
    S Singh, V Bhateja, S Gupta, S Verma, S Urooj, DN Le
    International Conference on Intelligent Computing and Communication, 105-113 2022

  • Adaptive neural backstepping control approach for tracker design of wheelchair upper-limb exoskeleton robot system
    AA Aly, KH Hsia, FFM El-Sousy, S Mobayen, A Alotaibi, G Mousa, DN Le
    Mathematics 10 (22), 4198 2022

  • Adaptive Neural Network-Based Fixed-Time Tracking Controller for Disabilities Exoskeleton Wheelchair Robotic System
    AA Aly, MT Vu, FFM El-Sousy, KH Hsia, A Alotaibi, G Mousa, DN Le, ...
    Mathematics 10 (20), 3853 2022


  • 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
    Citations: 214

  • 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
    Citations: 181

  • 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, L Van Chung, ...
    International Journal of Electrical & Computer Engineering (2088-8708) 6 (6) 2016
    Citations: 127

  • 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
    Citations: 123

  • Advances in swarm intelligence for optimizing problems in computer science
    A Nayyar, DN Le, NG Nguyen
    CRC press 2018
    Citations: 115

  • Internet of nano things (IoNT): Next evolutionary step in nanotechnology
    A Nayyar, V Puri, DN Le
    Nanoscience and Nanotechnology 7 (1), 4-8 2017
    Citations: 110

  • 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
    Citations: 92

  • A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images.
    MA Mohammed, KH Abdulkareem, B Garcia-Zapirain, SA Mostafa, ...
    Computers, Materials & Continua 66 (3) 2021
    Citations: 81

  • 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
    Citations: 80

  • Cloud computing and virtualization
    DN Le, R Kumar, GN Nguyen, JM Chatterjee
    John Wiley & Sons 2018
    Citations: 76

  • Synchronization phenomena investigation of a new nonlinear dynamical system 4D by Gardano’s and Lyapunov’s methods
    AA Hamad, AS Al-Obeidi, EH Al-Taiy, OI Khalaf, D Le
    Computers, Materials & Continua 66 (3), 3311-3327 2021
    Citations: 75

  • 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
    Citations: 73

  • 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
    Citations: 72

  • Map matching algorithm: real time location tracking for smart security application
    SK Prasad, J Rachna, OI Khalaf, DN Le
    Telecommunications and Radio Engineering 79 (13) 2020
    Citations: 68

  • 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
    Citations: 66

  • Nanoscale heat and mass transport of magnetized 3-D chemically radiative hybrid nanofluid with orthogonal/inclined magnetic field along rotating sheet
    A Ayub, Z Sabir, DN Le, AA Aly
    Case Studies in Thermal Engineering 26, 101193 2021
    Citations: 65

  • 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, 1-17 2017
    Citations: 64

  • 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
    Citations: 63

  • A hybrid approach of secret sharing with fragmentation and encryption in cloud environment for securing outsourced medical database: a revolutionary approach
    DN Le, B Seth, S Dalal
    Journal of Cyber Security and Mobility 7 (4), 379-408 2018
    Citations: 58

  • Optimizing Feature Selection in Video-based Recognition using Max-Min Ant System for the Online Video Contextual Advertisement User-Oriented System
    DNL Le Nguyen Bao, GN Nguyen, V Bhateja, SC Satapathy
    Journal of Computational Science, Elsevier 21, 361-370 2017
    Citations: 57