Dac-Nhuong Le
Verified email at 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
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.
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
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)
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)
Nguyen A. Tuan, D. Akila, Souvik Pal, Bikramjit Sarkar, Thien Khai Tran, G. Mothilal Nehru, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 1357-1372, Published: 2022
Computers, Materials and Continua (Tech Science Press)
V. N. Lakshmana Kumar, M. Satyanarayana, Sohanpal Singh, and Dac-Nhuong Le
EAI/Springer Innovations in Communication and Computing, ISSN: 25228595, eISSN: 25228609, Pages: 85-95, Published: 2022
Springer International Publishing
Meenakshi Malik, Rainu Nandal, Surjeet Dalal, Vivek Jalglan, and Dac-Nhuong Le
Intelligent Automation and Soft Computing, ISSN: 10798587, eISSN: 2326005X, Pages: 87-99, Published: 2022
Computers, Materials and Continua (Tech Science Press)
Pawan Singh, Ram Shringar Raw, Suhel Ahmad Khan, Mazin Abed Mohammed, Ayman A. Aly, and Dac-Nhuong Le
IEEE Access, eISSN: 21693536, Pages: 38850-38869, Published: 2022
Institute of Electrical and Electronics Engineers (IEEE)
Arun Kumar, R. Dhanagopal, Mahmoud A. Albreem, and Dac-Nhuong Le
Alexandria Engineering Journal, ISSN: 11100168, Pages: 5527-5536, Published: December 2021
Elsevier BV
Dac-Nhuong Le, Velmurugan Subbiah Parvathy, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues, and K. Shankar
International Journal of Machine Learning and Cybernetics, ISSN: 18688071, eISSN: 1868808X, Pages: 3235-3248, Published: November 2021
Springer Science and Business Media LLC
At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.
Thien Khai Tran, Hoa Dinh, Hien Nguyen, Dac-Nhuong Le, Dong-Ky Nguyen, An C. Tran, Viet Nguyen-Hoang, Ha Nguyen Thi Thu, Dinh Hung, Suong Tieu, Canh Khuu, and Tuan A. Nguyen
Sustainability (Switzerland), eISSN: 20711050, Published: October-1 2021
MDPI AG
The COVID-19 pandemic, since its beginning in December 2019, has altered every aspect of human life. In Vietnam, the pandemic is in its fourth peak and is the most serious so far, putting Vietnam in the list of top 30 countries with the highest daily cases. In this paper, we wish to identify the magnitude of its impact on college students in Vietnam. As far as we’re concerned, college students belong to the most affected groups in the population, especially in big cities that have been hitting hard by the virus. We conducted an online survey from 31 May 2021 to 9 June 2021, asking students from four representative regions in Vietnam to describe how the pandemic has changed their lifestyle and studying environment, as well as their awareness, compliance, and psychological state. The collected answers were processed to eliminate unreliable ones then prepared for sentiment analysis. To analyze the relationship among the variables, we performed a variety of statistical tests, including Shapiro–Wilk, Mc Nemar, Mann–Whitney–Wilcoxon, Kruskal–Wallis, and Pearson’s Chi-square tests. Among 1875 students who participated, many did not embrace online education. A total of 64.53% of them refused to think that online education would be the upcoming trend. During the pandemic, nearly one quarter of students were in a negative mood. About the same number showed signs of depression. We also observed that there were increasing patterns in sleeping time, body weight, and sedentary lifestyle. However, they maintained a positive attitude toward health protection and compliance with government regulations (65.81%). As far as we know, this is the first project to conduct such a large-scale survey analysis on students in Vietnam. The findings of the paper help us take notice of financial and mental needs and perspective issues for indigent students, which contributes to reducing the pandemic’s negative effects and going forwards to a better and more sustainable life.
Loveleen Gaur, Anam Afaq, Arun Solanki, Gurmeet Singh, Shavneet Sharma, N.Z. Jhanjhi, Hoang Thi My, and Dac-Nhuong Le
Computers and Electrical Engineering, ISSN: 00457906, Published: October 2021
Elsevier BV
Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Hafiz Abdul Wahab, Gilder Cieza Altamirano, Yu-Dong Zhang, and Dac-Nhuong Le
Mathematics and Computers in Simulation, ISSN: 03784754, Volume: 188, Pages: 87-101, Published: October 2021
Elsevier BV
Assad Ayub, Zulqurnain Sabir, Dac-Nhuong Le, and Ayman A. Aly
Case Studies in Thermal Engineering, ISSN: 2214157X, Published: August 2021
Elsevier BV
Muhammad Umar, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Manoj Gupta, Dac-Nhuong Le, Ayman A. Aly, and Yolanda Guerrero-Sánchez
Symmetry, eISSN: 20738994, Published: April 2021
MDPI AG
The current study aims to design an integrated numerical computing-based scheme by applying the Levenberg–Marquardt backpropagation (LMB) neural network to solve the nonlinear susceptible (S), infected (I) and recovered (R) (SIR) system of differential equations, representing the spreading of infection along with its treatment. The solutions of both the categories of spreading infection and its treatment are presented by taking six different cases of SIR models using the designed LMB neural network. A reference dataset of the designed LMB neural network is established with the Adam numerical scheme for each case of the spreading infection and its treatment. The approximate outcomes of the SIR system based on the spreading infection and its treatment are presented in the training, authentication and testing procedures to adapt the neural network by reducing the mean square error (MSE) function using the LMB. Studies based on the proportional performance and inquiries based on correlation, error histograms, regression and MSE results establish the efficiency, correctness and effectiveness of the proposed LMB neural network scheme.