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Control the Interval Estimation to Enhance the Performance of Optimized Link State Routing 4

Control the Interval Estimation to Enhance the Performance o...

4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Ishqeem, Omar Ammar Sarreb, Rebah Daw Hatif Libya Company Libyan Post Telecommunications & Information Technology Company Libya Sabratha university Faculty of Engineering Rigdalin Dept. of Computer Engineering Libya

ISBN: (纸本)9798350372632

This paper investigates the creation of self-organizing Mobile Ad hoc Networks (MANETs) employing Optimized Link State Routing Protocol (OLSR) with Multipoint Relay (MPR). The routing protocol in MANET controls the management of node mobility by using metrics to evaluate the quality of link states. The primary concept of OLSR is to choose the MPR that identifies the route from the source to the destination node. The MPR relays Hello and Topology Control (TC) messages between nodes used to discover and distribute link state information. The Hello and TC messages interval plays a critical role in the link state in the network. Accordingly, link failure for a few seconds may lead to a significant performance deterioration. This work enhances the performance of OLSR by estimating the efficient Hello and TC message interval for faster route recovery. Many experiments are simulated and analyzed by a set of parameters such as MPR count, TC traffic sent, throughput, delay, and data dropped. All our results revealed that the proposed Control of the Interval Estimation-Optimized Link State Routing (CIE-OLSR) has better performance than the traditional OLSR. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Industrial Teleassistance with the Help of Augmented Reality 23

Industrial Teleassistance with the Help of Augmented Reality

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Bobeica, Valentin Popescu, Dan Angelescu, Nicoleta Ichim, Loretta National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Valahia University of Targoviste Faculty of Electrical Engineering Electronics and Information Technology Targoviste Romania

ISBN: (纸本)9798331540388

This paper details the integration of augmented reality (AR) in a new factory, focusing on enhancing maintenance efficiency and optimization. The innovative approach involves combining AR technology, an Android application, and Siemens PLCs (Programmable Logic Controller), for efficient automation. The robustness of the PLC enables seamless integration within manufacturing environments, accommodating diverse programming paradigms. The software aspect features Human Machine Interface (HMI) configuration using Siemens' WinCC software, offering dynamic visualizations, alarm handling, and data logging. WinCC's scalability suits applications of varying sizes. The HMI architecture is designed for easy parameter display. The Android application, developed with Java and Python, leverages their respective strengths in Android app development and image processing. Data exchange is facilitated by OPC-UA, supporting multiple programming languages for seamless communication. The AR Teleassistance is highlighted as a revolutionary solution, fostering real-Time communication among global experts, technicians, and operators. Beyond gaming origins, AR teleassistance enhances efficiency in various sectors, minimizing downtime and facilitating global knowledge transfer. The integration of Teleassistance promotes effective collaboration between human expertise and automated systems, crucial for optimal performance in modern industrial settings. Continuous development is suggested through the addition of more 3D mechanical drawings and real-Time stock information for parts in the application, aiming for future optimization and performance enhancement. © 2024 IEEE.

关键词： augmented reality data exchange global knowledge image processing manufacturing environment production processes smart industrial

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Image Contrast Enhancement using Fuzzy Logic with Metaheuristics 24

Image Contrast Enhancement using Fuzzy Logic with Metaheuris...

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11th International Conference on Networking, Systems and Security, NSysS 2024

作者： Kabir, Mohimenul Mobin, Jaiaid Hassanat, Ahmad Sohel Rahman, M. Department of Computer Science Bangladesh University of Engineering and Technology Dhaka Bangladesh Rochester Institute of Technology Rochester United States Faculty of Information Technology Mutah University Karak Mutah Jordan Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400711589

In this work, we present a method to enhance image contrast using a fuzzy logic-based transformation function. Our goal is to develop an image-specific transformation by tuning the fuzzy system. We employ Hill Climbing and Genetic Algorithms in various configurations to evolve the fuzzy system, conducting multiple experiments. Different variants of our method are tested on several images, and the two variants with the highest fitness scores are selected. Additionally, we compare against existing contrast enhancement technique and conduct a survey to evaluate the visual improvements made by these two variants. The survey results indicate that one method effectively enhances image contrast visually. © 2024 Copyright held by the owner/author(s).

关键词： computer circuits

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Expert Phishing Detection System

Expert Phishing Detection System

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Alsarhan, Ayoub Al-Aiash, Issa Al-Fraihat, Dimah Aljaidi, Mohammad Abu Laila, Dena Abd Al Hakem Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (纸本)9798350353464

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model’s performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts. ©2024 IEEE.

关键词： Cyber-security Phishing attacks Phishing URLs

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ATmega2560 MCU Space Vector Modulation Implementation on Three-phase Inverter and Validation via Typhoon HIL 402 5

ATmega2560 MCU Space Vector Modulation Implementation on Thr...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Brandis, Andrej Pelin, Denis Šimić, Zvonimir Vidošić, Ivan Department of Electromechanical Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

In this paper, a space vector modulation implementation is done via ATmega2560 microcontroller with the Arduino development board. The control parameters are defined by the user in MATLAB software and sent to Arduino to start or stop the controller or to change the controller parameters. Space vector switching times for chosen parameters are calculated and given. The implemented algorithm is validated via Typhoon HIL 402 system on a three-phase inverter model for two different operation points. Voltage and current waveforms are obtained in Typhoon HIL. Line and phase voltage, as well as the phase current spectrum analysis is done via Typhoon HIL and processed in MATLAB for given system parameters. The measurement results validates the implemented space vector control algorithm. © 2022 IEEE.

关键词： Microcontrollers

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Early Detection of Coronary Heart Disease Using Exploration Data Analysis and Gradient Boosting Method

Early Detection of Coronary Heart Disease Using Exploration ...

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2024 International Conference on information technology and Computing, ICITCOM 2024

作者： Riyadi, Slamet Iqbal Al-Habib, Muhammad Alfiana, Fitri Damarjati, Cahya Jusman, Yessi Ramli, Suzaimah Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Information Technology Yogyakarta Indonesia Universiti Pertahanan Nasional Malaysia Faculty of Defense Science and Technology Department of Computer Science Kuala Lumpur Malaysia

ISBN: (纸本)9798350379839

Coronary heart disease is a significant global health issue that contributes to high morbidity and mortality rates, including in Indonesia. Public awareness of risk factors for heart disease is a matter that has an impact on disease burden, prevention, and timely treatment. However, public awareness of coronary heart disease and its risk factors is still low. Many individuals continue to engage in unhealthy lifestyles, lack awareness of effective preventive actions, and neglect regular heart health check-ups. To address these challenges, one of the solutions that can be made is a valuable tool for identifying individuals at risk and enabling early intervention and preventive measures. This study proposes implementing a machine learning model for the early detection of coronary heart disease using Exploration Data Analysis (EDA) and Gradient Boosting methods. Integrates EDA techniques for preprocessing data, such as finding important features and patterns, and then Gradient Boosting techniques to capture complex relationships and make accurate predictions based on a wide range of factors, such as a person's disease history, daily habits, and body characteristics. To evaluate the performance of our model, we split the dataset with an 80:20 ratio for training and testing and achieved an impressive accuracy of 91,39%. These results showed that our model could reliably predict the presence or absence of cardiac disease. With such precise forecasts, early prevention, identification, and treatment might be aided, which would ultimately reduce heart disease-related mortality and morbidity. © 2024 IEEE.

关键词： Adaptive boosting

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Enhancing Event Extraction and Impact Analysis with Knowledge Graph Augmented Transformer 21

Enhancing Event Extraction and Impact Analysis with Knowledg...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Songklang, Kallaya Srisomboon, Kanabadee Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Understanding the impact of events reported in news articles plays pivotal roles in various tasks. In this study, we propose a framework for extracting event components from the news and analyzing their multifaceted impact. The framework employs transformers techniques for event extraction and leverages a biography knowledge base to assess the potential impact of events across social, political, economic, and culture (SPEC) dimensions. Our framework achieves an F1-score of 78.12% in event identification as well as significant accuracy score in evaluating the impact levels prediction. By integrating results from event extraction and impact analysis, the level of multifaceted influence of the event along with the event information are effectively visualized. This comprehensive approach enhances understanding of multi-perspective interpretation and supports insights for various applications. © 2024 IEEE.

关键词： Distribution transformers

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Defect Exposure in Vegetables and Fruits using Machine Learning

Defect Exposure in Vegetables and Fruits using Machine Learn...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Spandana, S. Sathiya, M. Krishnan, Kesavan Kiran, Ajmeera MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Karpagam Institute of Technology Department of Information Technology Coimbatore India Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350348217

Defects to agricultural food items like fruits and vegetables, results in decrease of quality and quantity of agricultural consuming items. This directly affects the financial source of agriculturists and the human health. So, exposing to this kind of defects in fruits and vegetables at early stages of developing products leads to minimization in loss of production and quality. The regular or most used day to day approach for defect detection is continuously watching and observation of crop either by farmers or further by some professionals. But, it is very time-consuming method. We have used image processing and classification techniques in machine learning to identify and detect the diseases on agricultural products like vegetables and fruits. The main goal of the system being proposed here is to monitor and detect diseases in fruits and vegetables in their early stages to farmers for a healthy yield. Our System can deliver the most accurate results in a very short time while consuming minimal computational resources. © 2023 IEEE.

关键词： Vegetables

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer Systems Science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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