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1st International Conference on Global Aeronautical Engineering and Satellite technology, GAST 2024

作者： Ezzara, Abderrahim Ouadine, Ahmed Youssef Ayad, Hassan Cadi Ayyad University Faculty of Science and Technology Lseeet Laboratory Department of Applied Physics Marrakesh Morocco Ecole Royale de l'Air Marrakesh Morocco

ISBN: (纸本)9798350371598

This paper deals with the design of a novel Active Fault Tolerant Control (AFTC) algorithm for an underactuated quadrotor UAV system in the presence of actuator faults. A robust control strategy based on the backstepping technique is used to compensate for the fault effect in the system. In order to estimate the fault magnitude, an adaptive observer is developed based on a nonlinear dynamic model of the quadcopter. This dynamic model, considering high-order nonholonomic constraints as well as various physical phenomena that can influence its dynamics, is briefly presented. According to a Lyapunov-based stability study, the suggested control strategy design preserves the stability of the closed-loop dynamics of quadrotor aircraft even in the presence of actuator failures. As shown by the numerical simulation results, the suggested control laws exhibit excellent tracking performance. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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A new method for human activity identification using convolutional neural networks

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International Journal of Cloud Computing 2023年第2-4期12卷 191-200页

作者： Prakash, P.S. Balakrishnan, S. Venkatachalam, K. Balasubramanian, Saravana Balaji Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tiruchengode637205 India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Applied Cybernetics Faculty of Science University of Hradec Králové Hradec Králové50003 Czech Republic Department of Information Technology Lebanese French University Erbil Iraq

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Throughput Prediction Using LS-SVM For 4g-LTE Network and Beyond

Throughput Prediction Using LS-SVM For 4g-LTE Network and Be...

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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mostefa Hamdani Ahmed Sahnoune Mokhtar Merzougui Abdelmalek Douara Fouad Khatemi Department of Mathematics and Computer Science Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology University El Wancharissi Department of Science and Technology Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology University El Wancharissi

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

The integration of Artificial Intelligence(AI) with Long-Term Evolution (LTE) networks offers substantial potential for improving communication infrastructure. By harnessing AI algorithms, it is possible to dynamically optimize network parameters, proactively predict and mitigate equipment failures, and enhance Quality of Service (QoS) by prioritizing critical services through real-time analysis of key performance indicators. This study focuses on predicting network throughput using Least Square Support Vector Machine (LS-SVM). The predictive models developed can trigger adaptive network reconfigurations, ensuring that the network responds promptly to changing conditions. Simulation results reveal that the integration of LS-SVM significantly improves prediction accuracy, enabling more effective implementation of self-organizing network strategies in real-time.

关键词： Support vector machines Correlation coefficient Accuracy Quality of service Predictive models Throughput Real-time systems Telecommunications Analysis of variance Testing

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Leveraging Support Vector Machine for Sports Injury Classification 15

Leveraging Support Vector Machine for Sports Injury Classifi...

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15th International Conference on Information technology and Electrical Engineering, ICITEE 2023

作者： Surasak, Thattapon Praking, Phornnatee Kitchat, Kotcharat King Mongkut's University of Technology North Bangkok Faculty of Applied Science Dept. of Computer and Information Science Bangkok Thailand National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350304466

This study explores the utility of Support Vector Machines (SVM) for the classification of injuries in Athletes including tennis, table tennis, and badminton. The primary objective of this research was to develop an automated method to detect between normal and injury pose, providing a poten-tially powerful tool for early detection. In other words, this research is the pioneer study to represent one another solution for sports injury classification using the image data apart from the current approaches that might be focused on the sensor-based. Therefore, we collect a dataset from various social media across the three sports, labeled into two categories: 'Normal' and 'Injury'. The SVM model was trained on this dataset, and tuned to optimize its performance in distinguishing the two classes. Achieving an accuracy of 96.63%, the result demonstrates that the SVM model can effectively identify injury patterns in the considered sports. The implications of this study are sig-nificant, presenting the initial steps towards applying computer vision tasks in sports topics. By demonstrating the feasibility of an SVM-based approach to injury detection in these racket sports. © 2023 IEEE.

关键词： Sports

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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Influence of agglomerations on magnetic properties of polymer matrices filled with magnetic nanoparticles

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Materials Today: Proceedings 2022年 67卷 792-796页

作者： Blachowicz, Tomasz Grzybowski, Jacek Ehrmann, Andrea Institute of Physics – Centre for Science and Education Silesian University of Technology Gliwice 44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice 44-100 Poland Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences Bielefeld 33619 Germany

Magnetic nanoparticles can be embedded in electrospun nanofibers and other polymeric matrices to prepare magnetic composites with defined magnetic and mechanical properties. Metal-oxide nanoparticles, such as magnetite or nickel-ferrite, are of special interest since they do not need a coating to avoid oxidation. Like other nanoparticles, these metal-oxide nanoparticles tend to form agglomerations, in this way modifying the magnetic properties of the composites. After studying this effect for the magnetic elements Co, Fe, Ni as well as permalloy (Py) in a previous study, defining a new method to quantify the nanoparticle distribution in a polymer, here we concentrate on the influence of agglomerations on the magnetic properties of metal-oxide nanoparticles with different diameters in non-magnetic matrices. © 2022

关键词： Magnetite

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Optically Transparent 4-Port UWB Antenna for MIMO Applications 2

Optically Transparent 4-Port UWB Antenna for MIMO Applicatio...

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2nd International Microwave and Antenna Symposium, IMAS 2024

作者： Yahya, Rabia Yin, Haosen Bertuch, David El-Ghazaly, Amal Nouakchott University Department of Physics Faculty of Science and Technology Nouakchott Mauritania Cornell University Department of Electrical and Computer Engineering IthacaNY United States

ISBN: (纸本)9798350356359

Herein, we present a novel transparent antenna with ultra-wideband (UWB) performance along with polarization diversity through 4 ports allowing MIMO operation. The antenna design can be of a great interest for a vehicular applications and Internet of Things (IOT) where the dual polarization and multiple ports allows as well as medical glasses and vehicular windows where transparency is needed for visibility. The proposed antenna provides compact size with dimensions of 50 mm×50 mm, through the employment of CPW, dual orthogonal-polarization, isolation level of 20 dB, and envelope correlation coefficient (ECC) of 0.0016 as well as MIMO performance. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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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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Cybersecurity Revolution via Large Language Models and Explainable AI 17

Cybersecurity Revolution via Large Language Models and Expla...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Ihsan, Anuam Al-Dmour, Nidal A. College of Arts & Science Applied Science University Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of College of Engineering Mutah University Department of Computer Engineering Jordan

ISBN: (纸本)9798331509736

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted;hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity. © 2024 IEEE.

关键词： Cybersecurity Explainable AI (XAI) Large Language Models (LLMs) Machine Learning (ML) Reliability Threat Detection Transformer Architecture Transparency

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