Nguyen Gia Nhu
@duytan.edu.vn
School of Computer Science
Duy Tan Univeristy
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Harish Garg, Tahir Mahmood, Ubaid ur Rehman, and Gia Nhu Nguyen
Elsevier BV
Harish Garg, Tehreem, Gia Nhu Nguyen, Tmader Alballa, and Hamiden Abd El-Wahed Khalifa
MDPI AG
Human activity recognition (HAR) is the process of interpreting human activities with the help of electronic devices such as computer and machine version technology. Humans can be explained or clarified as gestures, behavior, and activities that are recorded by sensors. In this manuscript, we concentrate on studying the problem of HAR; for this, we use the proposed theory of Aczel and Alsina, such as Aczel–Alsina (AA) norms, and the derived theory of Choquet, such as the Choquet integral in the presence of Atanassov interval-valued intuitionistic fuzzy (AIVIF) set theory for evaluating the novel concept of AIVIF Choquet integral AA averaging (AIVIFC-IAAA), AIVIF Choquet integral AA ordered averaging (AIVIFC-IAAOA), AIVIF Choquet integral AA hybrid averaging (AIVIFC-IAAHA), AIVIF Choquet integral AA geometric (AIVIFC-IAAG), AIVIF Choquet integral AA ordered geometric (AIVIFC-IAAOG), and AIVIF Choquet integral AA hybrid geometric (AIVIFC-IAAHG) operators. Many essential characteristics of the presented techniques are shown, and we also identify their properties with some results. Additionally, we take advantage of the above techniques to produce a technique to evaluate the HAR multiattribute decision-making complications. We derive a functional model for HAR problems to justify the evaluated approaches and to demonstrate their supremacy and practicality. Finally, we conduct a comparison between the proposed and prevailing techniques for the legitimacy of the invented methodologies.
Anand Nayyar, Nhu Gia Nguyen, Sakshi Natani, Ashish Sharma, and Sandeep Vyas
Springer Nature Switzerland
Siddharth Verma, Vikrant Bhateja, Sourabh Singh, Sparshi Gupta, Ayush Dogra, and Nguyen Gia Nhu
Springer Nature Singapore
Denis A. Pustokhin, Irina V. Pustokhina, Phuoc Nguyen Dinh, Son Van Phan, Gia Nhu Nguyen, Gyanendra Prasad Joshi, and Shankar K.
Informa UK Limited
ABSTRACT In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%.
Rajkumar Soundrapandiyan, Suresh Chandra Satapathy, Chandra Mouli P.V.S.S.R., and Nguyen Gia Nhu
Springer Science and Business Media LLC
P. R. Anisha, C. Kishor Kumar Reddy, and Nhu Gia Nguyen
Springer International Publishing
Kishor Kumar Reddy C, P R Anisha, Nhu Gia Nguyen, and G Sreelatha
IOP Publishing
Abstract This research involves the usage of Machine Learning technology and Natural Language Processing (NLP) along with the Natural Language Tool-Kit (NLTK). This helps develop a logical Text Summarization tool, which uses the Extractive approach to generate an accurate and a fluent summary. The aim of this tool is to efficiently extract a concise and a coherent version, having only the main needed outline points from the long text or the input document avoiding any type of repetitions of the same text or information that has already been mentioned earlier in the text. The text to be summarized can be inherited from the web using the process of web scraping or entering the textual data manually on the platform i.e., the tool. The summarization process can be quite beneficial for the users as these long texts, needs to be shortened to help them to refer to the input quickly and understand points that might be out of their scope to understand.
Gia Nhu Nguyen, Nin Ho Le Viet, Mohamed Elhoseny, K. Shankar, B.B. Gupta, and Ahmed A. Abd El-Latif
Elsevier BV
V. Rajinikanth, R. Sivakumar, D. Jude Hemanth, Seifedine Kadry, J. R. Mohanty, S. Arunmozhi, N. Sri Madhava Raja, and Nguyen Gia Nhu
Springer Science and Business Media LLC
Dac-Nhuong Le, Gia Nhu Nguyen, Trinh Ngoc Bao, Nguyen Ngoc Tuan, Huynh Quyet Thang, and Suresh Chandra Satapathy
Springer Singapore
Gia Nhu Nguyen, Nin Ho Le Viet, Gyanendra Prasad Joshi, and Bhanu Shrestha
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 (Tech Science Press)
Gia Nhu Nguyen, Nin Ho Le Viet, A. Francis Saviour Devaraj, R. Gobi, and K. Shankar
Elsevier BV
Chung Le Van, Gia Nhu Nguyen, Tri Huu Nguyen, Tung Sanh Nguyen, and Dac-Nhuong Le
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.
Bao Le Nguyen, E. Laxmi Lydia, Mohamed Elhoseny, Irina V. Pustokhina, Denis A. Pustokhin, Mahmoud Mohamed Selim, Gia Nhu Nguyen, and K. Shankar
Computers, Materials and Continua (Tech Science Press)
Isha Batra, Sahil Verma, Arun Malik, Kavita, Uttam Ghosh, Joel J. P. C. Rodrigues, Gia Nhu Nguyen, A. S. M. Sanwar Hosen, and Vinayagam Mariappan
MDPI AG
Lately, the Internet of Things (IoT) has opened up new opportunities to business and enterprises; however, the cost of providing security and privacy best practices is preventing numerous organizations from adopting this innovation. With the proliferation of connecting devices in IoT, significant increases have been recorded in energy use, harmful contamination and e-waste. A new paradigm of green IoT is aimed at designing environmentally friendly protocols by reducing the carbon impact and promote efficient techniques for energy use. There is a consistent effort of designing distinctive security structures to address vulnerabilities and attacks. However, most of the existing schemes are not energy efficient. To bridge the gap, we propose the hybrid logical security framework (HLSF), which offers authentication and data confidentiality in IoT. HLSF uses a lightweight cryptographic mechanism for unique authentication. It enhances the level of security and provides better network functionalities using energy-efficient schemes. With extensive simulation, we compare HLSF with two existing popular security schemes, namely, constrained application protocol (CoAP) and object security architecture for IoT (OSCAR). The result shows that HLSF outperforms CoAP and OSCAR in terms of throughput with low computational, storage and energy overhead, even in the presence of attackers.
Chung Le Van, Gia Nhu Nguyen, Tung Sanh Nguyen, Tri Huu Nguyen, and Dac‐Nhuong Le
Wiley
Osamah Ibrahim Khalaf, F. Ajesh, Abdulsattar Abdullah Hamad, Gia Nhu Nguyen, and Dac-Nhuong Le
IEEE Access 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
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
Diem-Phuc Tran, Gia-Nhu Nguyen, and Van-Dung Hoang
Institute of Electrical and Electronics Engineers (IEEE)
Today, several studies have been concretized in the areas of robotics, self-driving cars, intelligent assistance systems, and so on. Developing an increasingly optimal neural network in terms of accuracy and processing speed for resource-limited systems has become a major research trend. Some research orientations include focusing on developing solutions to optimize machine learning models and learning parameters. In this study, we investigated an optimization solution for learning hyperparameters of adaptive learning systems for improving object recognition accuracy. The proposed method was developed from a framework searching a set of learning hyperparameters based on the evaluation of the previous CNN model with the collected dataset during the movement of advanced driver assistance systems (ADAS) equipment. The proposed solution consists of some major steps in a loop of adaptive learning system, such as (1) training an initial recognition model, (2) locating and receiving image data of different cases of the object during ADAS movement based on object tracking process, (3) finding optimal hyperparameters on the found dataset based on the previous recognition model, and (4) using the trained recognition model to update the current recognition model. The experimental results proved that the trained recognition model was capable of being more intelligent and displayed more diverse recognition than the previous model. The updated task for the recognition model was continuously repeated throughout the ADAS life. This approach supports and enables the recognition system to be self-adaptive and more intelligent in real life settings without manually processing.