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
Arun Kumar, R. Dhanagopal, Mahmoud A. Albreem, and Dac-Nhuong Le
Alexandria Engineering Journal, ISSN: 11100168, Pages: 5527-5536, Published: December 2021
Elsevier BV
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.
Belal Al-Khateeb, Kawther Ahmed, Maha Mahmood, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 643-654, Published: 22 March 2021
Computers, Materials and Continua (Tech Science Press)
Zulqurnain Sabir, Kashif Nisar, Muhammad Asif Zahoor Raja, Muhammad Reazul Haque, Muhammad Umar, Ag Asri Ag Ibrahim, and Dac-Nhuong Le
IEEE Access, eISSN: 21693536, Published: 2021
Institute of Electrical and Electronics Engineers (IEEE)
Zulqurnain Sabir, , Muhammad Asif Zahoor Raja, Aldawoud Kamal, Juan L.G. Guirao, Dac-Nhuong Le, Tareq Saeed, Mohamad Salama, , , , , , and
Mathematical Biosciences and Engineering, ISSN: 15471063, eISSN: 15510018, Pages: 5285-5308, Published: 2021
American Institute of Mathematical Sciences (AIMS)
Meenakshi Malik, Rainu Nandal, Surjeet Dalal, Vivek Jalglan, and Dac-Nhuong Le
Intelligent Automation and Soft Computing, ISSN: 10798587, eISSN: 2326005X, Pages: 887-906, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Dac-Nhuong Le, Gia Nhu Nguyen, Trinh Ngoc Bao, Nguyen Ngoc Tuan, Huynh Quyet Thang, and Suresh Chandra Satapathy
Lecture Notes in Electrical Engineering, ISSN: 18761100, eISSN: 18761119, Volume: 708, Pages: 273-284, Published: 2021
Springer Singapore
Wijdan Jaber AL-kubaisy, Mohammed Yousif, Belal Al-Khateeb, Maha Mahmood, and Dac-Nhuong Le
International Journal of Computational Intelligence Systems, ISSN: 18756891, eISSN: 18756883, Pages: 1108-1118, Published: 2021
Atlantis Press
The presented study suggests a new nature–inspired metaheuristic optimization algorithm referred to as Red Colobuses Monkey (RCM) that can be used for optimization problems; this algorithm mimics the behavior related to red monkeys in nature. In preparation for proving the suggested algorithm’s advantages, a set of standard unconstrained and constrained test functions is employed, sixty–four of identified test functions utilized in optimization were applied as benchmarks for checking the RCM performance. The solutions have also been upgrading their positions based on the optimal solution, which was reached thus far. Also, RCM can replace the worst red monkey by the best child found so far to give an extra enhancement to the solutions. Also, comparative performance checks with Biogeography–Based Optimizer (BBO), Artificial–Bee–Colony (ABC), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) were done. The acquired results showed that RCM is competitive in comparison to the chosen metaheuristic algorithms.
Chung Le Van, Trinh Hiep Hoa, Nguyen Minh Duc, Vikram Puri, Tung Sanh Nguyen, and Dac-Nhuong Le
Intelligent Automation and Soft Computing, ISSN: 10798587, eISSN: 2326005X, Pages: 853-871, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Arun Kumar, Manoj Gupta, Dac-Nhuong Le, and Ayman A. Aly
Intelligent Automation and Soft Computing, ISSN: 10798587, eISSN: 2326005X, Pages: 713-722, Published: 2021
Computers, Materials and Continua (Tech Science Press)
A. S. Al-Waisy, Mazin Abed Mohammed, Shumoos Al-Fahdawi, M. S. Maashi, Begonya Garcia-Zapirain, Karrar Hameed Abdulkareem, S. A. Mostafa, Nallapaneni Manoj Kumar, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 2409-2429, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Pranati Rakshit, Sreeparna Ganguly, Souvik Pal, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 1207-1224, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Bijeta Seth, Surjeet Dalal, Dac-Nhuong Le, Vivek Jaglan, Neeraj Dahiya, Akshat Agrawal, Mayank Mohan Sharma, Deo Prakash, and K. D. Verma
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 779-798, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Begonya Garcia-Zapirain, Salama A. Mostafa, Mashael S. Maashi, Alaa S. Al-Waisy, Mohammed Ahmed Subhi, Ammar Awad Mutlag, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 3289-3310, Published: 2021
Computers, Materials and Continua (Tech Science Press)
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, Published: 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.
Abdulsattar Abdullah Hamad, Ahmed S. Al-Obeidi, Enas H. Al-Taiy, Osamah Ibrahim Khalaf, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 3311-3327, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Phong Thanh Nguyen, Vy Dang Bich Huynh, Khoa Dang Vo, Phuong Thanh Phan, Mohamed Elhoseny, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 2556-2571, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Srinath Doss, Jothi Paranthaman, Suseendran Gopalakrishnan, Akila Duraisamy, Souvik Pal, Balaganesh Duraisamy, Chung Le Van, *, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 1577-1594, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Chung Le Van, Le Nguyen Bao, Vikram Puri, Nguyen Thanh Thao, and Dac-Nhuong Le
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 17-33, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Dac-Nhuong Le, Gia Nhu Nguyen, Harish Garg, Quyet-Thang Huynh, Trinh Ngoc Bao, and Nguyen Ngoc Tuan
Computers, Materials and Continua, ISSN: 15462218, eISSN: 15462226, Pages: 993-1010, Published: 2021
Computers, Materials and Continua (Tech Science Press)
Chung Le Van, Gia Nhu Nguyen, Tri Huu Nguyen, Tung Sanh Nguyen, and Dac-Nhuong Le
International Journal of Electrical and Computer Engineering, ISSN: 20888708, Pages: 5951-5964, Published: December 2020
Institute of Advanced Engineering and Science
The goal of this project is to develop a complete, fully detailed 3D interactive model of the human body and systems in the human body, and allow the user to interacts in 3D with all the elements of that system, to teach students about human anatomy. Some organs, which contain a lot of details about a particular anatomy, need to be accurately and fully described in minute detail, such as the brain, lungs, liver and heart. These organs are need have all the detailed descriptions of the medical information needed to learn how to do surgery on them, and should allow the user to add careful and precise marking to indicate the operative landmarks on the surgery location. Adding so many different items of information is challenging when the area to which the information needs to be attached is very detailed and overlaps with all kinds of other medical information related to the area. Existing methods to tag areas was not allowing us sufficient locations to attach the information to. Our solution combines a variety of tagging methods, which use the marking method by selecting the RGB color area that is drawn in the texture, on the complex 3D object structure. Then, it relies on those RGB color codes to tag IDs and create relational tables that store the related information about the specific areas of the anatomy. With this method of marking, it is possible to use the entire set of color values (R, G, B) to identify a set of anatomic regions, and this also makes it possible to define multiple overlapping regions.