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3rd International Conference on Advances in Information and communication Technology, ICTA 2024

作者： Tan, Ha Minh Quoc, Nguyen Kim Vu, Duc-Quang Dang, An Thi, Duyen Nguyen Faculty of Information Technology Nguyen Tat Thanh University Thai Nguyen Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Faculty of Computer Science Phenikaa University Ha Noi Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

The self-attention architecture is also considered to be a significant contribution in sequence processing tasks. It has the ability to highlight the distinctive features of a sequence, has been very successful in natural language processing and speech separation. In addition, the intra- and inter-architecture of the dual-path bi-directional long-short term memory framework has been demonstrated to be very effective for processing extremely long sequences. Based on these properties, we design a core architecture called intra- and inter-self-attention framework. Parallel models is more efficient and stable than a single model. In this research, we propose a parallel intra- and inter-self-attention framework (PI2SA) for the cocktail party effect task. The experimental results demonstrate that our PI2SA is remarkably effective. It outperforms both traditional self-attention models and a basic intra- and inter-architecture. Our PI2SA achieves competitive results with state-of-the-art deep learning models on the cocktail party effect problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Identification of Candidate Drugs and Molecular Pathways for Pulmonary Arterial Hypertension and Chronic Kidney Disease 5

Identification of Candidate Drugs and Molecular Pathways for...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Hosen, Md. Faruk Yasmin, Mst. Farjana Basar, Md. Abul Trisha, Arifa Ferdoushi Shadhin, Khairul Alam Mollah, Md. Sarwar Uddin, Muhammad Shahin Department of Computing and Information System Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Purbachal American CityKanchon 1460 Bangladesh Department of Software Engineering Ashulia Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798350394290

Pulmonary arterial hypertension (PAH) is biggest preventable cause of early deaths, and hence one of the global preferences of World Health Organization for determent. PAH also induces the vast majority of patients with chronic kidney disease (CKD). Both PAH and CKD are inherently connected, but the common molecular pathways and genes of these two disorders have not yet been demonstrated. In this study, we aimed to determine the similar molecular pathways and therapeutic hub proteins in PAH and CKD that may be used to anticipate the progression of the disease. We used Limma package to conduct differential analysis of gene transcripts of PAH and CKD obtained from the Gene Expression repository. The functional annotations of the genes were determined with the use of pathway analysis. Then proteinâprotein interaction (PPI) networks were constructed to determine the hub proteins, and a clustering technique was applied to determine the most significant PPI elements. We separately evaluated the PAH and CKD gene expression-based datasets and discovered 30 differentially expressed genes (DEGs) shared by both PAH and CKD. The pathways of KEGG revealed that the most prevalent DEGs are associated with the Malaria and African trypanosomiasis. Gene ontology (GO), TF and miRNA analysis and module investigation is treated the forthcoming work of this study. Finally, various drug compounds have been suggested based on the concordant DEGs. © 2023 IEEE.

关键词： Proteins

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Identification of Molecular Biomarkers and Key Pathways among Idiopathic pulmonary fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD) and Lung cancer 26

Identification of Molecular Biomarkers and Key Pathways amon...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Yasmin, Mst. Farjana Faruk Hosen, Md. Basar, Md. Abul Alam Shadhin, Khairul Trisha, Arifa Ferdoushi Shahin Uddin, Muhammad Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh Department of Computing and Information System Dhaka Ashulia Bangladesh Department of Computer Science and Engineering Kanchon Purbachal American City1460 Bangladesh Department of Software Engineering Dhaka Ashulia Bangladesh

ISBN: (纸本)9798350359015

Lung cancer (LC), idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the most fatal disorders in the globe, generating frequent human issues. Having IPF and COPD are the risk factors of the LC, but the molecular mechanisms that underlie among IPF, COPD, and LC are not yet elucidated. In this study, we looked for shared molecular indicators and pathways that might explain how individuals with IPF, COPD, and LC are related to one another. GSE24206, GSE76925 and GSE18842 microarray datasets are utilized for IPF, COPD and LC samples. The preprocessing of datasets has done using R language, and then concordant differentially expressed genes (DEGs) are discovered. After that the protein-protein interactions (PPIs) are built using the similar DEGs, and the hub genes are determined using topological analysis. ETS1, MSH2, SORD, RORA and NEDD9 are the PPI network's top 5 hub genes. The pathways of KEGG demonstrated that the concordant DEGs are related to the colorectal cancer and pathways in cancer. Future work for this project will focus on miRNA, TF, and gene ontology (GO) analyses, as well as module analysis networks. Finally, based on the concordant DEGs, a number of potential medications have been suggested. © 2023 IEEE.

关键词： COPD Differential expressed genes Drug compounds enrichment analysis Hub gene IPF Lung cancer Protein-protein network

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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

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Advancements in Wireless Networks: Transition from 5G to Beyond 2

Advancements in Wireless Networks: Transition from 5G to Bey...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Malarvizhi, C. Banu, M. Ameena Anthonisamy, Arun Hemalatha, M. Manikandan, Raja University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chembarambakkam India NPR College of Engineering and Technology Department of Electronics and Communication Engineering Natham India Panimalar Engineering College Department of Computer Science and Business System Tamil Nadu Chennai600123 India Nandha Arts and Science College Department of Information Technology Tamil Nadu Erode638052 India K. Ramakrishnan College of Technology Department of ECE Tamil Nadu Trichy India

ISBN: (纸本)9798331534950

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies' benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology's effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big Data 15

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Khan, Mohammad Ahmar Verma, Pushpendra Kumar Ghaldiyal, Shivangi Balaji, N.V. Francis, G. Arun Patil, Swati Bula Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman Oman School of Computer Science and Applications IIMT University Meerut India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India IT Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab's gift to the . We provide the whole text of the news that have already been applied based on human evaluation of the articles' authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study. ©2024 IEEE.

关键词： Data collection

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A Lightweight Attention Model for Face Recognition 1

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2nd International Conference on Advances in Information and communication Technology, ICTA 2023

作者： Vu, Duc-Quang Nguyen, Thu Hien Nguyen, Danh Vu Nguyen, Yen Quynh Phung, Trung-Nghia Thu, Trang Phung T. Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam Faculty of Basic Science School of Foreign Language Thai Nguyen University Thai Nguyen Viet Nam

ISBN: (数字)9783031508189

ISBN: (纸本)9783031508172

Facial recognition is one of the most popular biometric methods in the real world. To address this task, various methods have been proposed especially deep learning-based approaches. However, most of them usually focus on improving performance by building deeper and more complex networks. This makes an important limitation for the ability to deploy them on embedded or mobile devices without GPU. In this paper, we introduce an efficient network called SeesawAttentionFaceNet which is a hybrid of SeesawFaceNet and CBAM attention to improve the performance of face recognition while keeping the simple to suitable for edge devices. The experiments conducted on various datasets have shown that our proposed framework outperforms the other state-of-the-art lightweight models for facial recognition. Moreover, we also provide an ablation study to demonstrate that when the face area of the removed position increases, the recognition results typically are reduced. This has important implications in feature extraction and processing of facial images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Face recognition

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TNUE-Fight Detection: A New Challenge Benchmark for Fighting Recognition 1

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2nd International Conference on Advances in Information and communication Technology, ICTA 2023

作者： Vu, Duc-Quang Nguyen, Thu Hien Nguyen, Mai Nguyen, Bao Yen Phung, Trung-Nghia Thu, Trang Phung T. Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam Faculty of Basic Science School of Foreign Language Thai Nguyen University Thai Nguyen Viet Nam

ISBN: (数字)9783031508189

ISBN: (纸本)9783031508172

Today, violence is one of the most common abnormal actions that need to be monitored and detected early. Therefore, a violence recognition system is very necessary and has great practical significance. In recent years, deep learning has achieved remarkable achievements in various different problems, however, deep learning models for violent action recognition have not been properly studied. One of the main reasons is the lack of rich labeled datasets. Specifically, the current datasets are usually only collected from movies and sports, which makes the videos in the dataset far different from actual fight actions. To overcome this drawback, in this paper, we propose a new challenge dataset named TNUE-Fight Detection. In which, our proposed dataset is collected from many real-life fights from videos uploaded to social networks. Furthermore, the TNUE-Fight Detection dataset not only provides labels for each video but also provides bounding boxes for fighting and non-fighting objects, which help to solve the problem in both cases of classification and detection. The TNUE-Fight Detection dataset is available at https://***/vdquang1991/TNUE_FightDetection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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An Efficient Real-Time Moroccan Automatic License Plate Recognition system Based on the YOLO Object Detector 6th

An Efficient Real-Time Moroccan Automatic License Plate Reco...

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6th International Conference on Big Data Cloud and Internet of Things, BDIoT 2022

作者： Ouardirhi, Zainab Mahmoudi, Sidi Ahmed Zbakh, Mostapha El Ghmary, Mohamed Benjelloun, Mohammed Abdelali, Hamd Ait Derrouz, Hatim Communication Networks Department ENSIAS Mohammed V University in Rabat Rabat Morocco Mons Belgium Department of Computer Science FSDM Sidi Mohamed Ben Abdellah University Fez Morocco Microelectronics Embedded System Department Moroccan Foundation for Advanced Science Innovation and Research Rabat Morocco

ISBN: (纸本)9783031283864

In this paper, we adopt a powerful deep neural network (DNN) to tackle the problems of detecting car license plates (LPs) and recognizing Arabic letters in natural scene images. Our Automatic License Plate Recognition (ALPR) system is built using state-of-the-art methodologies and techniques to provide the optimal speed/accuracy trade-off at each level. Images of vehicles and Moroccan and European LPs were used to train the neural networks. The system was able to correctly detect and recognize in the test set all the characters existing in a LP with an accuracy of 91.11% and 98.89% taking into account that only one character is misread. Using a high-end GPU, our system likewise produced outstanding real-time execution results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Economic and social effects

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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