Dac-Nhuong Le
@dhhp.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:
email: nhuongld@
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Networks and Communications
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
Nikhil Malik, Arpna Kalonia, Surjeet Dalal, and Dac-Nhuong Le
Springer Science and Business Media LLC
Surjeet Dalal, Neeraj Dahiya, Amit Verma, Neetu Faujdar, Sarita Rathee, Vivek Jaglan, Uma Rani, and Dac-Nhuong Le
Bentham Science Publishers Ltd.
Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data preprocessing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings.
M. P.Karthikeyan, K. Krishnaveni, and Dac-Nhuong Le
Deanship of Scientific Research
A M Viswa Bharathy, Dac-Nhuong Le, and P. Karthikeyan
CRC Press
Dao Phuc Minh Huy, Ho Thi Huong Thom, Nguyen Gia Nhu, and Dac-Nhuong Le
Springer Nature Singapore
Dao Phuc Minh Huy, Nguyen Gia Nhu, and Dac-Nhuong Le
IGI Global
This paper presents a comprehensive approach to the detection and prevention of cheating in online exams using AI. The authors employ various technical solutions to monitor proctors throughout all stages of the exam: before, during, and after. To address the formulations and ensure the continuous expansion of the database, the authors rely on a fast convolutional neural network (CNN) that utilizes a full-scope pattern matching algorithm (FSPM) to enhance the ability to match fingerprint formats using descriptive network cryptography. The authors anticipate reliable matching across the complete fingerprint image set through the utilization of deep-learning (DL) symbols. Furthermore, the authors demonstrate that solving image-matching problems does not necessitate tool training data, which is typically required for such problems. Thanks to the highly parallelizable nature of these tasks, the authors provide an efficient method with minimal computational cost during test time to detect cheating during some exams at School of Computer Science at Danang University of Technology (SCS-DTU) University, Vietnam.
Hemang A. Thakar, Vidisha Pradhan, Dac-Nhuong Le, and Pankaj Jain
CRC Press
Swati, Menu Vijarania, Vivek Jaglan, and Dac‐Nhuong Le
Wiley
Swati, Meenu Vijarania, Akshat Agarwal, and Dac‐Nhuong Le
Wiley
Arnab Dey, Samit Biswas, and Dac-Nhuong Le
Elsevier BV
Arnab Dey, Samit Biswas, and Dac-Nhuong Le
Tech Science Press
Alisha Sikri, Surjeet Dalal, N.P Singh, and Dac‐Nhuong Le
Wiley
Pawan Singh, Ram Shringar Raw, and Dac-Nhuong Le
CRC Press
Ngoc-Khuong Nguyen, Dac-Nhuong Le, Viet-Ha Nguyen, and Anh-Cuong Le
Computers, Materials and Continua (Tech Science Press)