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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Sawaged, Layan Khabour, Safaa M. The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan

ISBN: (纸本)9798350335590

Jordan has a high and growing level of traffic accidents reaching 160,600) accidents in 2021, (11,241) of them had human injuries, (589) deaths, and (320) million JOD losses. Road traffic accidents are currently the 8th leading cause of death worldwide, accounting for about 1.35 million fatalities annually. Understanding the primary causes of these accidents and the circumstances in which they occur is essential if governments throughout the world are to put policies in place to limit the number of fatalities brought on by traffic accidents. This project has two objectives: (i) to learn more about Jordan's present road accident situation by performing an exploratory study on several road accident datasets, and (ii) to investigate the effectiveness of various machine-learning techniques in predicting the severity of road accidents in Jordan. The dataset entities were collected, collated, explored, and prepared for use in the model. To assess their predictive performance, five different classification algorithms were trained and tested. The findings of the present study demonstrate that the best algorithm was Logistic Regression, which had an accuracy of 98.1%. © 2023 IEEE.

关键词： Machine learning

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Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

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computer Systems Science & engineering 2023年第5期45卷 1343-1356页

作者： Fadl Dahan Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology AlturbahTaiz UniversityTaiz9674Yemen

Web services are provided as reusable software components in the services-oriented *** complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service(QoS)*** workflow consists of tasks where many services can be considered for each *** for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial(NP)-hard *** work focuses on the Web Service Composition(WSC)problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the *** proposed algorithm determines the optimal combination of the web services to satisfy the complex user *** also addresses the Bat Algorithm(BA)shortcomings,such as the tradeoff among exploration and exploitation searching mechanisms,local optima,and convergence *** proposed enhancement includes a developed cooperative and adaptive population initialization *** elitist mechanism is utilized to address the BA convergence *** tradeoff between exploration and exploitation is handled through a neighborhood search *** benchmark datasets are selected to evaluate the proposed bat algorithm’s *** simulation results are estimated using the average fitness value,the standard deviation of the fitness value,and an average of the execution time and compared with four bat-inspired *** is observed from the simulation results that introduced enhancement obtains significant results.

关键词： Cloud computing web service composition bat algorithm serviceoriented architecture

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Enhancing Extractive Text Summarization of Long Documents Using Features Dimensionality Reduction 3

Enhancing Extractive Text Summarization of Long Documents Us...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelrahman, Amany A. El-Kassas, Wafaa S. Mohamed, Hoda K. Ain Shams University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Faculty of Computers and Information Technology Giza Egypt

ISBN: (纸本)9798350306231

Automatic Text Summarization (ATS) task is considered one of the most challenging tasks in Natural Language Processing field, especially for long documents. There are many techniques for ATS, including Artificial Intelligence (AI) approaches, statistical-based approaches, graph-based approaches, etc., but the generated summaries are still less accurate than human summaries. This research presents an extractive Neural-based model to summarize long documents using both attentive global and local contexts, Word2Vec embeddings, and a bidirectional Gated Recurrent Unit (GRU) as a Sentence Encoder. The proposed model uses a Multi-Layer Perceptron (MLP) that consists of two linear layers with Rectified Linear Activation Units (RELUs) to reduce features dimensionality. The proposed model is evaluated on PubMed and arXiv datasets using ROUGE metrics, and the evaluation results show remarkable and promising improvements for ROUGE-1, ROUGE-2, and ROUGE-L. © 2023 IEEE.

关键词： Artificial intelligence

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning 16

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Shrestha, Sujan Taniguchi, Yoji Tanaka, Tetsuo Graduate school of Engineering Kanagawa Institute of Tehnology Course of Information and computer sciences Atsugi Japan Daiichi Institute of Technology Artificial Intelligence and Data Science Department of Information Tokyo Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan

ISBN: (纸本)9798350377903

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence;however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Assessing the Effects of Blackhole Attacks on MANET Reliability and Security

Assessing the Effects of Blackhole Attacks on MANET Reliabil...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Fadhil, Saif Aamer Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad10011 Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks. © 2024 IEEE.

关键词： Mobile ad hoc networks

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

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computers, Materials & Continua 2023年第2期74卷 2839-2852页

作者： Slim Ben Chaabane Anas Bushnag Computer Engineering Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk 47512Saudi Arabia Electrical Engineering Department University of Tunis CEREPENSIT 5 AvTaha Hussein1008TunisTunisia

A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion *** secondorder neighborhood shows the connection between the current pixel and the surroundings of this *** connection is for each RGB component color of the input *** the image edges are detected for the three primary colors:red,green,and blue,these colors are merged using the combination ***,the final decision is applied to obtain the *** process allows different data sources to be combined,which is essential to improve the image information quality and have an optimal image ***,the segmentation results of the proposed model are ***,the classification accuracy of the tested data is assessed,and a comparison with other current models is *** comparison results show that the proposed model outperforms the existing models in image segmentation.

关键词： Segmentation edge detection second derivative operators data fusion technique fuzzy fusion classification

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Optimizing Algorithms for Human Behavior Recognition: Single-Modality Spatiotemporal Convolutional Networks 11

Optimizing Algorithms for Human Behavior Recognition: Single...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Zhao, Rui Hao, Jutao Feng, Zhikun Wang, Xianzhi Prasad, Mukesh Huo, Huan School of Computer Science University of Technology Sydney Faculty of Engineering and IT Sydney Australia School of Electric Information Engineering Shanghai Dianji University Shanghai China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798331531904

Behavior recognition constitutes a pivotal area of research within computer vision and serves as an integral technology in video surveillance and related domains, thereby possessing substantial research value. Convolutional neural networks (CNNs) have been proven to possess robust learning capabilities. However, due to the high complexity and variability of human behavior, recognizing human behaviors in videos remains challenging due to the difficulty of capturing useful features over long time series and the redundancy of multi-frame information. To address these issues and improve the utilization of continuous frame information, we have designed a cross-frame multiscale information fusion module. This module allows the backbone network to acquire multiscale temporal features from different frames, thereby reducing information redundancy across multiple frames while minimizing additional computational costs. Furthermore, to mitigate the problem of video temporal feature loss, we introduce an easily integrable attention mechanism module, the Easy-Attention Block. This module facilitates rapid capture of long-range dependencies, enabling the model to focus on the most relative parts of continuous video sequences and suppress irrelevant temporal information. Consequently, the model can extract more discriminative spatial-temporal features, thereby enhancing its efficiency in handling long sequence data. Experimental results express that this model can achieve high recognition accuracy and effectively extract the temporal features of video, suitable for real-time applications in critical infrastructure surveillance, which enhancing safety and reducing potential risks in energy power systems. © 2024 IEEE.

关键词： Spatio-temporal data

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MDNET: A Novel Neural Network Based on CNN and Fuzzy Rough Set with Adaptive Parameters for Intrusion Detection in the Internet of Things

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International Journal of engineering, Transactions B: Applications 2025年第12期38卷 2965-2993页

作者： Zendehdel, M. DehakiToroghi, A. Hamidzadeh, J. Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

The internet of things (IoT) has become increasingly widespread, finding applications in various fields like transportation fleet management, smart homes, traffic management, and smart greenhouses. However, a significant concern with IoT devices is the vast amount of data they store. Sensitive data, including personal information, requires strong security, as unauthorized access can lead to cyberattacks. Attack scenarios, including data theft, system hijacking, and unauthorized access, pose significant threats, making early detection crucial to minimize potential damage. This paper proposes the MDNET neural network for enhanced IoT intrusion detection. The MDNET network aims to achieve high accuracy in detecting IoT network intrusions without requiring manual hyperparameter tuning. To achieve this, the CEO Optimization Algorithm was employed to adaptively select the hyperparameters for the proposed network. Our proposed method employs fuzzy rough set theory to select appropriate features from the network traffic dataset. This process removes features significantly impacted by noise, thereby enhancing the overall efficiency of the *** proposed approach was evaluated using five standard metrics accuracy, average precision, Kappa coefficient, Hamming loss, and Jaccard similarity;compared with seven existing models (FWP-SVM-GA, FBiNN, GA-FR-CNN, APSO-CNN, LSTM, GRU, ANN). The proposed method achieved an impressive 99.57% accuracy, 99.7% average-precision, and 0.99 Kappa coefficient, significantly outperforming the best-performing existing algorithm (FBiNN) with 97.28% accuracy and 0.96 Kappa coefficient. Additionally, MDNET exhibited lower Hamming loss (0.003) and a higher Jaccard similarity (0.99), confirming its superior classification accuracy and robustness in IoT attack detection. ©2025 The author(s).

关键词： Network intrusion

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The role of data governance in a high-level approach of data migration to open data

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Business information Review 2024年第3期41卷 94-103页

作者： Yulfitri, Alivia Sensuse, Dana Indra Sumirat Hidayat, Deden Suryono, Ryan Randy Kautsarina Purwaningsih, Erisva Hakiki Prabuwono, Anton Satria Faculty of Computer Science Universitas Indonesia Depok Indonesia Faculty of Computer Science Universitas Esa Unggul West Jakarta Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Central Jakarta and Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Information and Communications Technology Human Resources Development Agency Ministry of Communication and Information Technology of the Republic of Indonesia Jakarta Central Jakarta Indonesia Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia

Data quality issues are still a challenge in data migration. Existing methods mainly focus on technical issues. However, the problem of data quality is not only a technical problem, but the main one is the non-technical aspect. This paper aims to identify the role of data governance in the data migration process. The qualitative research method uses two primary references, namely the high-level data migration approach from Kazimir et al. and the DAMA-DMBOK framework. Both are highly compatible with data migration processes focusing on data quality and referencing governance roles. The case study was conducted in a central government working on data migration to the new One Data application. The results show that eight data governance roles are involved in 20 migration processes (65%). Non-technical activities related to data quality involve more than half of the data migration process. This paper proposes the role of data governance for non-technical aspects of the migration process. This paper presents a case study of the high-level data migration process and how data governance comes into play. This research can help the government and practitioners overcome problems that often occur in data migration, especially related to data quality. © The Author(s) 2024.

关键词： DAMA-DMBOK framework data governance data migration Indonesia’s one data Kazimir et al.’s approach open data

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