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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Dimitrios Meimetis Ioannis Daramouskas Vaios Lappas Isidoros Perikos Vaggelis Kapoulas Michael Paraskevas Department of Aerospace Science & Technology NKUA Athens Greece Computer Technology Institute and Press “Diophantus” Patras Greece Applied Mechanics Lab UoP Patras Greece Computer Engineering and Informatics Department UoP Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach.

关键词： Simulation Autonomous aerial vehicles Aircraft navigation Sensor systems Robustness Real-time systems Trajectory Safety Sensors Drones

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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International Conference on Electrical engineering and informatics, ICEEI

作者： Jason Jeremy Egi Muhammad Idris Hidayat Simon Siregar Bambang Riyanto Trilaksono Natsir Habibullah School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department Computer Engineering Faculty of Applied Science Telkom University Bandung Indonesia

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG.

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Robust Control of Continuous Casting Process for Various Technological Conditions

Robust Control of Continuous Casting Process for Various Tec...

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International Carpathian Control Conference (ICCC)

作者： Sohaibullah Zarghoon Luká š Bartalský Cyril Belavý Institute of Automation Measurement and applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9781665466363

ISBN: (纸本)9781665466370

The process of continuous steel casting, specifically the part of secondary cooling, is a typical representative of a system with distributed parameters. The paper deal with the synthesis and simulation of robust control of continuous steel casting in the secondary cooling zone. During steel production, there is a smooth change between the types of steel produced (or steel grades), to which the control system must react flexibly. Therefore, a robust approach based on simple PID controllers will be chosen for control synthesis. Based on the model obtained from the ProCast software, the uncertainty limits for the simulation model will be determined. Robust control simulations will be performed on a time-space circuit, within the simulations a possible machine failure will be tested, thus a change in cooling power in individual zones. The results will be presented as temperature fields, representing the system with distributed parameters, taking into account the time and space distribution of the controlled temerature.

关键词： Robust control Casting Uncertainty Cooling Process control Production Aerospace electronics

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Selection of a Simple 5G Handover Algorithm: Preparation for Named Data Networking

Selection of a Simple 5G Handover Algorithm: Preparation for...

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International Conference on Wireless and Telematics (ICWT)

作者： Ade Nurhayati Syaiful Ahdan Tutun Juhana Nana Rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Applied Science Telkom University Jakarta Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

Named Data Networking as an alternative network for 5G network traffic is required to be able to provide better performance compared to other networks such as internet protocol networks. In NDN wireless, it is known that there is producer mobility and consumer mobility, so optimal mobility management is needed, including the handover process. In this paper the author proposes a simple RSSI-based 5G handover algorithm with distance as a reference RSSI value. This algorithm is a preparation for the handover algorithm on the NDN wireless network. By using the assumed distance value MS to eNB as the basic value for calculating RSSI, handover determination is determined by comparing the RSSI value to the RSSI threshold value. With the scenario of different numbers of users and cluster sizes, from the handover process involving 8 eNB. It can be calculated how big the handover probability is for each user. From the simulation results, it can be concluded that the more users and the larger the cluster size, the greater the handover probability value. This is linear, with the wider the cluster, the probability of getting an RSSI signal from the original eNB becomes smaller, making it possible to get an RSSI signal from neighboring eNB, so that a handover occurs.

关键词： 5G mobile communication Wireless networks Simulation Clustering algorithms Telecommunication traffic Handover Probability

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Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack Tree and Fuzzy Logic 3

Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack T...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naik, Nitin Jenkins, Paul Grace, Paul Naik, Dishita Song, Jingping Prajapat, Shaligram Mishra, Durgesh Yang, Longzhi Boongoen, Tossapon Iam-On, Natthakan School of Informatics and Digital Engineering Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Birmingham City University United Kingdom Software College Northeastern University China International Institute of Professional Studies Devi Ahilya University India School of Computer Science & Information Technology Symbiosis University of Applied Sciences India Northumbria University Department of Computer and Information Sciences United Kingdom Aberystwyth University Department of Computer Science United Kingdom

ISBN: (纸本)9798350343274

Organisations and users have been experiencing significant rises in cyberattacks and their severity, which means that they require a greater awareness and understanding of the anatomy of cyberattacks, to prevent and mitigate their effects. In analysing cyberattacks, there are a number of different approaches that may be used to assess their potential risks and effects. However, these are utilised in specific types of cyberattacks and their analysis, which means they cannot be applied in every situation or cyberattack. Moreover, several other factors may influence the decision to use these approaches, such as cost, complexity, skills and adaptability. As a result, continuous research to design and enhance these approaches is undertaken to produce a generic, cost-effective, easy and adaptable approach. This paper presents one such approach to assess the risk of cyberattacks utilising an attack tree and fuzzy logic. An attack tree is a systematic and illustrative method for describing an attack on a system and analysing its taxonomy and other aspects. Subsequently, the probability and risk of each leaf node in the attack tree are calculated using the proposed formulas. Finally, fuzzy logic enables decision making based on imprecise data and heuristics to obtain the overall risk of attack. This proposed approach comprises systematic steps to accomplish an assessment of any cyberattack and its associated risks in an uncomplicated and effective manner, enabling its prevention and mitigation to be determined. The paper illustrates an application of the proposed approach to assess the risk of an information theft attack on an organisation, which can then be utilised to assess the risk of other cyberattacks. © 2023 IEEE.

关键词： Decision making

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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