Dac-Nhuong Le
Verified @hus.edu.vn
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.
website: http://www.dhhp.edu.vn/~nhuongld
email: nhuongld@dhhp.edu.vn
RESEARCH INTERESTS
Computer Science, Evolutionary Multi-objective Optimization, Network Communication and Security, Biomedical Imaging
FUTURE PROJECTS
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
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
Arabian Journal for Science and Engineering, ISSN: 2193567X, eISSN: 21914281, Pages: 4123, Published: March 2023
Springer Science and Business Media LLC
Vraj Parikh, Jainil Shah, Chintan Bhatt, Juan M Corchado, and Dac-Nhuong Le
Lecture Notes in Networks and Systems, ISSN: 23673370, eISSN: 23673389, Volume: 603 LNNS, Pages: 1-12, Published: 2023
Springer International Publishing
Abhishek Bhattacharya, Soumi Dutta, Mohammad Kamrul Hasan, Kusum Yadav, Dac-Nhuong Le, and Pastor Arguelles
Lecture Notes in Networks and Systems, ISSN: 23673370, eISSN: 23673389, Volume: 491, Pages: 77-86, Published: 2023
Springer Nature Singapore
Thien Khai Tran, Kha Tu Huynh, Dac-Nhuong Le, Muhammad Arif, and Hoa Minh Dinh
Intelligent Automation and Soft Computing, ISSN: 10798587, eISSN: 2326005X, Pages: 2479-2491, Published: 2023
Computers, Materials and Continua (Tech Science Press)
Zulqurnain Sabir, Muhammad Asif Zahoor Raja, S. R. Mahmoud, Mohammed Balubaid, Ali Algarni, Abdulaziz H. Alghtani, Ayman A. Aly, and Dac-Nhuong Le
International Journal of Computational Intelligence Systems, ISSN: 18756891, eISSN: 18756883, Published: December 2022
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.
Ayman A. Aly, Kuo-Hsien Hsia, Fayez F. M. El-Sousy, Saleh Mobayen, Ahmed Alotaibi, Ghassan Mousa, and Dac-Nhuong Le
Mathematics, eISSN: 22277390, Published: November 2022
MDPI AG
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.
Ayman A. Aly, Mai The Vu, Fayez F. M. El-Sousy, Kuo-Hsien Hsia, Ahmed Alotaibi, Ghassan Mousa, Dac-Nhuong Le, and Saleh Mobayen
Mathematics, eISSN: 22277390, Published: October 2022
MDPI AG
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.
Ayman A. Aly, Mai The Vu, Fayez F. M. El-Sousy, Ahmed Alotaibi, Ghassan Mousa, Dac-Nhuong Le, and Saleh Mobayen
Mathematics, eISSN: 22277390, Published: September 2022
MDPI AG
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.
S. Sudhakar, B. L. Radhakrishnan, P. Karthikeyan, K. Martin Sagayam, and Dac-Nhuong Le
Journal of Communications Software and Systems, ISSN: 18456421, eISSN: 18466079, Pages: 217-227, Published: September 2022
Croatian Communications and Information Society
Sadia Ali, Yaser Hafeez, Mamoona Humayun, N. Z. Jhanjhi, and Dac-Nhuong Le
Information Technology and Management, ISSN: 1385951X, eISSN: 15737667, Pages: 151-165, Published: September 2022
Springer Science and Business Media LLC
Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Dac-Nhuong Le, and Ayman A. Aly
Complex and Intelligent Systems, ISSN: 21994536, eISSN: 21986053, Pages: 1987-2000, Published: June 2022
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.
Surjeet Dalal, Bijeta Seth, Vivek Jaglan, Meenakshi Malik, Surbhi, Neeraj Dahiya, Uma Rani, Dac-Nhuong Le, and Yu-Chen Hu
Soft Computing, ISSN: 14327643, eISSN: 14337479, Pages: 5377-5388, Published: June 2022
Springer Science and Business Media LLC
Bijeta Seth, Surjeet Dalal, Vivek Jaglan, Dac‐Nhuong Le, Senthilkumar Mohan, and Gautam Srivastava
Transactions on Emerging Telecommunications Technologies, eISSN: 21613915, Published: April 2022
Wiley
Dushyant Kumar Singh, Rajeev Sobti, Anuj Jain, Praveen Kumar Malik, and Dac‐Nhuong Le
IET Communications, ISSN: 17518628, eISSN: 17518636, Pages: 604-618, Published: March 2022
Institution of Engineering and Technology (IET)
Evolving Software Processes: Trends and Future Directions, Pages: xxiii-xxv, Published: 7 January 2022
Evolving Software Processes: Trends and Future Directions, Pages: 1-281, Published: 7 January 2022
Wiley
N. Karunya, V. A. Kanimozhi, N. Muthumani, M. P. Karthikeyan, and Dac-Nhuong Le
Lecture Notes in Electrical Engineering, ISSN: 18761100, eISSN: 18761119, Volume: 907, Pages: 141-151, Published: 2022
Springer Nature Singapore
Kanta Prasad Sharma, Vinesh Kumar, and Dac-Nhuong Le
EAI/Springer Innovations in Communication and Computing, ISSN: 25228595, eISSN: 25228609, Pages: 155-166, Published: 2022
Springer International Publishing
G. Naveen Sundar, Stalin Selvaraj, D. Narmadha, K. Martin Sagayam, A. Amir Anton Jone, Ayman A. Aly, and Dac-Nhuong Le
CMES - Computer Modeling in Engineering and Sciences, ISSN: 15261492, eISSN: 15261506, Volume: 133, Pages: 31-46, Published: 2022
Computers, Materials and Continua (Tech Science Press)
Meenakshi Malik, Rainu Nandal, Surjeet Dalal, Ujjawal Maan, and Dac-Nhuong Le
Journal of Intelligent and Fuzzy Systems, ISSN: 10641246, eISSN: 18758967, Pages: 3283-3292, Published: 2022
IOS Press
In recent years, driver behavior analysis plays a vital role to enhance passenger coverage and management resources in the smart transportation system. The real-world environment possesses the driver principles contains a lot of information like driving activities, acceleration, speed, and fuel consumption. In big data analysis, the driver pattern analyses are complex because mining information is not utilized to feature evaluations and classification. In this paper, a new efficient Fuzzy Logical-based driver behavioral pattern analysis has been proposed to offer effective recommendations to the drivers. Primarily, the feature selection can be carried out with the assist of fuzzy logical subset selection. The selected features are then evaluated using frequent pattern information and these measures will be optimized with a multilayer perception model to create behavioral weight. Afterward, the information weights are trained with a test through an optimized spectral neural network. Finally, the neurons are activated by a recurrent neural network to classify the behavioral approach for the superior recommendation. The proposed method will learn the characteristics of driving behaviors and model temporal features automatically without the need for specialized expertise in feature modelling or machine learning techniques. The simulation results manifest that the proposed framework attains better performance with 98.4% of prediction accuracy and 86.8% of precision rate as compared with existing state-of-the-art methods.
Dibyendu Mukherjee, Shivnath Ghosh, Souvik Pal, Ayman A. Aly, and Dac-Nhuong Le
IEEE Access, eISSN: 21693536, Pages: 49412-49421, Published: 2022
Institute of Electrical and Electronics Engineers (IEEE)
Ravindra Parshuram Bachate, Ashok Sharma, Amar Singh, Ayman A. Aly, Abdulaziz H. Alghtani, and Dac-Nhuong Le
Computer Systems Science and Engineering, ISSN: 02676192, Pages: 439-454, Published: 2022
Computers, Materials and Continua (Tech Science Press)
Hardev Mukeshbhai Khandhar, Chintan Bhatt, Dac-Nhuong Le, Harshil Sharaf, and Wathiq Mansoor
Lecture Notes in Electrical Engineering, ISSN: 18761100, eISSN: 18761119, Volume: 839, Pages: 215-224, Published: 2022
Springer Singapore
Varun Dogra, Sahil Verma, Kavita Verma, NZ Jhanjhi, Uttam Ghosh, and Dac-Nhuong Le
International Journal of Interactive Multimedia and Artificial Intelligence, eISSN: 19891660, Pages: 35-52, Published: 2022
Universidad Internacional de La Rioja
School of Computer Science and Engineering, Lovely Professional University, India Department of Computer Science and Engineering, Chandigarh University, Mohali, India School of Computer Science and Engineering, Taylor’s University, Malaysia Department of Computer Science and Data Science, Meharry School of Applied Computational Sciences, Nashville, TN, USA School of Computer Science, Duy Tan University, Danang, 550000, Vietnam Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam
Ayman A. Aly, Mohamed O. Elhabib, Bassem F. Felemban, B. Saleh, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 93-107, Published: 2022
Computers, Materials and Continua (Tech Science Press)