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.
Computer Science, Evolutionary Multi-objective Optimization, Network Communication and Security, Biomedical Imaging
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.
Arun Kumar, R. Dhanagopal, Mahmoud A. Albreem, and Dac-Nhuong Le
Alexandria Engineering Journal, ISSN: 11100168, Pages: 5527-5536, Published: December 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, , 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.
Debabrata Dansana, Raghvendra Kumar, Janmejoy Das Adhikari, Mans Mohapatra, Rohit Sharma, Ishaani Priyadarshini, and Dac-Nhuong Le
Frontiers in Public Health, eISSN: 22962565, Published: 29 October 2020 Frontiers Media SA
The world health organization (WHO) formally proclaimed the novel coronavirus, called COVID-19, a worldwide pandemic on March 11 2020. In December 2019, COVID-19 was first identified in Wuhan city, China, and now coronavirus has spread across various nations infecting more than 198 countries. As the cities around China started getting contaminated, the number of cases increased exponentially. As of March 18 2020, the number of confirmed cases worldwide was more than 250,000, and Asia alone had more than 81,000 cases. The proposed model uses time series analysis to forecast the outbreak of COVID-19 around the world in the upcoming days by using an autoregressive integrated moving average (ARIMA). We analyze data from February 1 2020 to April 1 2020. The result shows that 120,000 confirmed fatal cases are forecasted using ARIMA by April 1 2020. Moreover, we have also evaluated the total confirmed cases, the total fatal cases, autocorrelation function, and white noise time-series for both confirmed cases and fatalities in the COVID-19 outbreak.
Osamah Ibrahim Khalaf, F. Ajesh, Abdulsattar Abdullah Hamad, Gia Nhu Nguyen, and Dac-Nhuong Le
IEEE Access, eISSN: 21693536, Published: 2020 Institute of Electrical and Electronics Engineers (IEEE)
Security and correspondence happening between network central point will be an instance for principal issues in Mobile Ad-hoc Networks (MANETs). Due to some ideas created by the organization leading to avoid attacks but may end in failure due to inappropriate way and thus attacks need recognized and cleared. The Dual-Cooperative Bait Detection Scheme (D-CBDS) is one of the ways that is in the stake for the discovery of MANET-dark/dim opening assailants. The current CBDS calculation consolidates the intensity of proactive and responsive security advancements to characterize lure mode assailants as proactive and receptive engineering. In CBDS, an adjacent source node is randomly selected as a bait target for searching. By reverse tracking as a reactive method, the attackers are identified. However, in some time, the chosen bait destination node may be an intruder that is not handled in the current CBDS approach. This paper therefore reinforces the CBDS with the dual mode of selecting two nearby nodes as two bait destinations. Dual reverse tracking enables effective collaborative assailants in MANET. Finally, when we analyze D-CBDS with respect to Routing overhead, End-End delay and throughput it gives much productivity than other methods like DSR, CBDS.
Trinh Ngoc Bao, Quyet-Thang Huynh, Xuan-Thang Nguyen, Gia Nhu Nguyen, and Dac-Nhuong Le
International Journal of Computational Intelligence Systems, ISSN: 18756891, eISSN: 18756883, Pages: 1447-1463, Published: 2020 Atlantis Press
Hanoi University, Hanoi 100000, Vietnam Hanoi University of Science and Technology, Hanoi 100000, Vietnam Graduate School, Duy Tan University, Da Nang 550000, Vietnam Faculty of Information Technology, Duy Tan University, Da Nang 550000, Vietnam Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam Faculty of Information Technology, Haiphong University, Haiphong 180000, Vietnam