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

作者： Al-Omari, Omaia Omari, Asem Othman, Esam Alrashed, Atheer Ahmed Prince Sultan University College of Computer and Information Sciences Information Systems Department Riyadh Saudi Arabia Higher Colleges of Technology Higher Colleges of Technology Higher Colleges of Technology Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates Applied Science University Department of Business Administration East Al-Ekir Bahrain

ISBN: (纸本)9798350369106

This paper introduces a novel Multimodal Aspect-Based Sentiment Analysis (MABSA) methodology by creating a Multimodal Sentiment Analysis model utilizing Recurrent Attention Networks (MuSeRAN). The proposed framework uses the synergy between textual and visual data to improve sentiment classification. We use BERT and Bottom-Up Attention (BUA) models as encoders for text and images to guarantee efficient feature extraction. The primary innovation resides in the interactive fusion module, which amalgamates semantic representations at the token level and employs GRU cells to eliminate noise from the image data. Our iterative attention mechanism enhances aspect-specific sentiment features, facilitating progressive learning for improved classification efficacy. We assessed the proposed model using the Twitter-17 dataset with the Twitter-15 dataset, attaining results surpassing leading baselines. Findings demonstrate that multimodal models, including ours, surpass single-modal methods, significantly depending on text-based attributes for precise sentiment prediction. The T-test verifies the enhancements' statistical significance, affirming our model's robustness. This research underscores the importance of multimodal fusion methodologies in enhancing sentiment analysis applications. © 2024 IEEE.

关键词： Semantics

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Vehicle Management System: Fuel, Mileage, Cost, and Maintenance Tracker

Vehicle Management System: Fuel, Mileage, Cost, and Maintena...

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2023 International Conference on Cyber Management and Engineering, CyMaEn 2023

作者： Kamolsin, Chiranun Rungkul, Woramet Limpalasuk, Chananon Pensiri, Fuangfar Ratanavilisagul, Chiabwoot Visutsak, Porawat Chaopraya University Faculty of Science and Technology Nakhon Sawan Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand Kasetsart University Faculty of Science at Sriracha Chonburi Thailand

ISBN: (纸本)9781665493291

According to the supply chain disruptions induced by the COVID-19 pandemic and the situation between NATO and Russia that led to the rising of fuel costs. Rising fuel costs are a massive problem for business and consumers. People must pay more money for their living because higher fuel costs affect costs of products, services, and transportation. This paper aims to develop the vehicle management system to track the fuel usage, mileage, cost, and maintenance of cars in an organization. The system is developed in a form of a web application for reporting a fuel usage and requesting permission to reserve a car. We used HTML, PHP, and Laravel Jetstream to develop this web application for employees and administrators. Employee can choose a car and submit a reservation form through a website. While administrator can manage cars information through the dashboard. User can use the mobile application to check in to get a car when a car is ready to use and check out a car when he/she wants to return a car to a company. © 2023 IEEE.

关键词： Supply chains

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Utilizing Machine Learning Algorithms for Effectively Detection IoT DDoS Attacks 1st

Utilizing Machine Learning Algorithms for Effectively Detect...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Mughaid, Ala Alqahtani, Ali AlZu’bi, Shadi Obaidat, Ibrahim Alqura’n, Rabee AlJamal, Mahmoud AL-Marayah, Raid Department of Information Technology Faculty of Prince Al-Hussien Bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Networks and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Department Faculty of Science and Information Technology Al Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9783031337420

One of the most critical features of the Internet of Things (IoT) is that it allows a massive number of devices to communicate with each other at the same time, for example (cars, smoke alarms, watches, glasses, web cameras, etc.). Given that they can connect to the Internet, the huge demand to convert systems into smart systems that can be controlled remotely increases the chances of cyber-attacks that threaten security and availability becoming more frequent, severe, and in a different form. Distributed Denial of Service (DDoS) attacks have become the most common threats in IoT. These attacks make IoT systems unreliable because complex DDoS attacks cannot be detected by traditional security countermeasures such as firewalls and intrusion detection systems (IDS). In this research, we proposed a model dedicated to work on prediction and detection of DDoS attacks on networks IoT, where we run this model on a NF-BoT-IoT-v2 dataset, which contains 37,763,497 records divided into five categories (Benign, Reconnaissance, DDoS, DoS, and Theft), and then we process our dataset to be more business friendly so it only has two categories (100,000 records broken down into 23,906 benign attacks and 76,094 DDoS attacks). This study used WEKA and MATLAB tools to perform the classification efficiently and find the most important features through feature selection. We used a decision tree (J48) machine learning classifier, a Naive Bayes (NB) classifier, and a random forest (RF) classifier. However, the results were as expected and improved accuracy and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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Designing a hybrid neural network framework for real-time anomaly detection in cybersecurity applications

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International Journal of information Technology (Singapore) 2025年 1-7页

作者： Jain, Aaditya Ead, Waleed M. Alshahrani, Mohammed Altamimi, Jawaher Suliman Aklan, Fawaz Rihan, Shaza Dawood College of Computing Sciences and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Computers Department Applied College Najran University Najran Saudi Arabia Applied College Najran University Najran Saudi Arabia Department of Computer Science Faculty of Applied Science Taiz University Taiz P.O. Box 6803 Taiz Yemen

As cybersecurity is becoming a prominent domain, it is an imperative to provide real-time anomaly detection for early threat detection and to keep networks safe. In this paper, an optimized hybrid neural network-framework is proposed to detect anomalies at several attack vectors with high efficacy. The framework comprises convolutional neural network equipped with long-short term memory (CNN-LSTM) for temporal and spatial representation of data. As the system accommodates new data and keeps on learning, it adjusts itself becoming a significant mean in counteracting against emerging threats with less latency in detection. In order to verify the efficiency and robustness of the proposed framework, a series of real-time simulation is designed using key performance metrics such as detection rate, false alarm rate and processing time. The results show higher improvement in accuracy and speed of anomaly detection, which makes the framework a useful instrument to reinforce cybersecurity defense during real-time scenarios. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Anomaly detection Convolutional neural network (CNN) Cybersecurity Hybrid approach Long-short term memory (LSTM)

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On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sensor based Classification of Table-Tennis Strokes 1

On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sen...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Ezzeldin, Mustafa Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Helwan University Faculty of Computers AI Software Engineering Cairo Egypt Helwan University Faculty of Computers and AI Computer Science Cairo Egypt Helwan University Faculty of Computers and AI Information Systems Cairo Egypt Helwan Univeristy HCI-LAB Faculty of Computers and AI Computer Science Cairo Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Table Tennis is a renowned competitive and recreational sport. An Olympic sport since 1988, table tennis in-cludes several movements, shots (i.e., strokes), and positions. Consequently, many factors can affect the strokes' accuracy and strength. This paper aims twofold: to classify the type of shot and to enhance the performance of the classification by improving the recognition accuracy and reducing the computational time. To track the movement of the body parts and extract table tennis stroke information, sensors (both Accelerometers and Gyroscopes) were utilized. The extracted data are processed and passed to a P-Dollar classifier to recognize six different table tennis strokes. This work utilizes two classification approaches to evaluate the performance, both linear and hierarchical. The presented results demonstrate that the hierarchical classification yields a better accuracy of 88%, achieved in 4 seconds, while traditional linear classification achieved 83% within 6 seconds. © 2023 IEEE.

关键词： Gyroscopes

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Incremental Learning Framework for Mining Big Data Stream

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computers, Materials & Continua 2022年第5期71卷 2901-2921页

作者： Alaa Eisa Nora E.L-Rashidy Mohammad Dahman Alshehri Hazem M.El-bakry Samir Abdelrazek Information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Machine Learning and Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

At this current time,data stream classification plays a key role in big data analytics due to its enormous *** of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are *** weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of *** main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network *** proposed model is implemented in spark *** each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound *** method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and *** results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).

关键词： Ant lion optimization(ALO) big data stream generative adversarial network(GAN) incremental learning renyi entropy

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Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Tbenawey, Fahdah M. Alotaibi, Shoayee Salem, Marwa Mir, Shamaila Mir, Zahida Computer Science and Engineering College University of Ha'il Department of Computer Science and Information Hail Saudi Arabia Faculty of Engineering Department of Electrical Communication and Electronics Systems Engineering Cairo Egypt

ISBN: (纸本)9798350347050

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB. © 2023 IEEE.

关键词： MATLAB

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Statistical Investigation of Scientific Journals Impact Factors in Relation to Benford's Law 6

Statistical Investigation of Scientific Journals Impact Fact...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Sorour, Marwa A. Marey, Yassmine A. Halim, Islam Tharwat Abdel Kasem, Mohamed Mahran School of Information Technology and Computer Science Nile University Giza Egypt Nile University Giza Egypt Alexandria University Faculty of Engineering Egypt School of Engineering and Applied Science Nile University Giza Egypt

ISBN: (纸本)9798350378511

Benford's law (BL) has been used as a tool for the detection of possible data manipulation in datasets. The statistics of the first digit(s) of the observed values are compared to a set of expected values given by BL, and violations signal possible data anomalies. Though this method is first used for economical purposes, it has been recently used in bibliometric statistical analyses. The method provides alert that records may have certain unusual trends. In the present work, we investigate the impact factor statistics for 24 years starting from 1997 up to 2021, for open access and subscribed journals. We study conformity to BL using the basic first digit(s) violation test and the mantissa arc test, besides using the relative size factor test. Results show that open access journals adhere to Benford's Law more closely than subscribed journals. © 2024 IEEE.

关键词： Open access

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Beyond Traditional Motion Planning: A Proximal Policy Optimization Reinforcement Learning Approach for Robotics 5th

Beyond Traditional Motion Planning: A Proximal Policy Optimi...

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5th Joint International Conference on AI, Big Data and Blockchain, ABB 2024

作者： Rjoub, Gaith Drawel, Nagat Dssouli, Rachida Bentahar, Jamal Kassaymeh, Sofian Alweshah, Mohammed Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9783031731501

In today’s technologically-driven era, robotic systems have been integrated into various aspects of our daily lives, handling tasks from household chores to complex industrial applications. With these robotic systems becoming increasingly commonplace in homes, offices, and public spaces, the demand for effective and adaptable motion planning becomes paramount. Traditional motion planning methods, although having made noteworthy progress, still have difficulties with the challenges posed by dynamic environments characterized by unpredictable elements and rapid changes. Recognizing this gap, this paper introduces a novel reinforcement learning-based path planning algorithm specifically designed for dynamic scenarios. Through the integration of Proximal Policy Optimization (PPO) reinforcement algorithm, our approach empowers robots to actively chart their paths, adapting in real-time to unforeseen obstacles and shifts in the environment. To validate our methodology, we undertook extensive experiments comparing our approach to established benchmark methods, notably SARSA, A∗, and Regular Reinforcement Learning (RL) (i.e., non PPO-based). The results not only highlight the effectiveness of our proposed solution but also emphasize its potential to reshape how robotic systems navigate complex, ever-evolving environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Robot programming

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