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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Alqaraleh, Muhyeeddin Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa13110 Jordan

ISBN: (纸本)9798350375190

Distributed systems based on the Internet allow resources widely dispersed throughout the network to be utilized cooperatively and shared. This allows the end-user to obtain massive computational power to perform a computationally intensive task. This is where the essential function of resource finding comes into play. Even though different procedures have been presented to identify multi-attribute resources, these processes have been plagued by many problems, including significant overhead and inefficiency as the number of resources increases. Distributed Quadtree Overlay is the innovative technology presented in this academic study. With DOTO, distance-aware querying is used. Moreover, since DOTO is constructed via the local network, it does not need any communication. As a result of the trials, the Distributed Quadtree Overlay performed better than the Chord. According to the findings, Distributed Quadtree Overlay is superior to Chord in terms of communication time since it needs fewer hopes and messages, uses less memory, and produces better (shortened) communication times. © 2024 IEEE.

关键词： Distributed computer systems

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Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 198-214页

作者： Fazhi Song Ning Cui Shuaiqi Chen Kai Zhang Yang Liu Xinkai Chen Jiubin Tan IEEE the Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology the State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology the Center of Ultraprecision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of UltraPrecision Intelligent Instrumentation Ministry of Industry and Information Technology the Department of Electronic and Information Systems Shibaura Institute of Technology

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency *** being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

关键词： Extended state observer learning control model uncertainties motion control stochastic noise

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A Development Approach for Electronic Flight Bag software 43

A Development Approach for Electronic Flight Bag Software

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Evangelista Matilde, Felipe Rodrigo Marques, Johnny Aeronautics Institute of Technology Software and Information Systems Department São José dos Campos Brazil

ISBN: (纸本)9798350349610

Operating an airplane is not an easy task. The pilots must check for weight and balance, communicate with towers, flight crew and passengers, fly the airplane, navigate from origin to destination, sometimes deal with system failures, and perform many procedures. Electronic Flight Bags (EFB) are applications pilots use inside the airplane cabin to make operating an airplane easier. This type of software application has been growing in the last twenty years. Most of these applications developed for tablets help pilots considerably operate airplanes by reducing workloads, optimizing airplane performance, and removing paper manuals from airplanes. In 2021, EUROCAE published the standard ED-273 for developing EFBs and introduced some requirements related to software development assurance, among other relevant points. The EUROCAE ED-273, as expected, made EFB manufacturers concerned about how to follow the new requirements. On the other hand, a 30-year-old software standard, the RTCA DO-178C, establishes considerations for developers, installers, and users when designing embedded equipment using software. Several aircraft manufacturers have well-established RTCA DO-178C software processes. We believe it would be very beneficial for organizations with an RTCA DO-178C compliant process to take advantage of it as a means of compliance for the new ED-273. Our work proposes to create an EFB development approach compliant with ED-273 guidelines but using the original RTCA DO-178C processes defined by the aircraft manufacturer. A mapping between the two standards was performed. The development approach was thoroughly assessed by a panel of highly experienced experts in aeronautical software. The main contributions of this paper are (i) the mapping between EUROCAE ED-273 and RTCA DO-178C, (ii) the development approach, and (iii) the evaluation processes defined and executed with the participation of 5 experts in RTCA DO-178C or EFB software development. © 2024 IEEE.

关键词： Application programs

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Ontology Evaluation Tools: Current and Future Research

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Recent Advances in Computer Science and Communications 2023年第6期16卷 2-15页

作者： Debnath, Narayan C. Patel, Archana Department of Software Engineering School of Computing and Information Technology Eastern International University Thu Dau Mot Viet Nam

In recent years, the increasing interest in ontologies resulted in the developing and publishing of many ontologies in the same or different domains. When users try to reuse the existing ontologies in their applications, they may encounter problems determining the most appropriate ontology based on their needs. Ontology evaluation is a process that helps users to examine the quality of the ontology based on different attributes. Many accessible and usable tools for ontology evaluation have been studied in the literature. However, finding an efficient ontology evaluation tool, following ontology specifications and their requirements (advantages/disadvantages), is still missing, limiting the researchers from determining possible future research. This paper aims to help new researchers and practitioners identify appropriate ontology evaluation tools based on their requirements and provide guidelines for future research directions on the same topic. This paper provides a detailed description of the different types of ontologies and classifies the available ontology evaluation tools into two categories, namely domain dependant ontology evaluation tools and domain-independent ontology evaluation tools. © 2023 Bentham Science Publishers.

关键词： Ontology

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Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

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Computer Modeling in engineering & Sciences 2024年第11期141卷 1651-1671页

作者： Mohamed A.Mahdi Suliman Mohamed Fati Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Information Systems Department College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia

Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in *** model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection *** model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing *** results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

关键词： Cyberbullying offensive detection Bidirectional Encoder Representations from the Transformers(BERT) continuous bag of words Social Media natural language processing

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Synergetic Innovation in Gherkin Cultivation Based on Machine Learning Algorithms

Synergetic Innovation in Gherkin Cultivation Based on Machin...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Jinasena, H.D.S.S. Dayarathna, T.W.K.D.A. Perera, H.M.A.G. Nissanka, K.G.T.R.K. Kasthurirathna, D. Gunathilake, M.P. Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Sri Lanka

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367560

Gherkin cultivation, while a significant contributor to the agricultural sector, remained underutilized in Sri Lanka despite high international demand, particularly for pickles. Gherkin farmers faced many challenges from pests, diseases, and unpredictable harvests. The main reasons identified were farmers' lack of knowledge, different weather conditions, and inability to identify the danger. The proposed system harnesses cutting-edge machine learning algorithms, computer vision, and object detection to address key challenges in Gherkin production. The solution enabled pest identification and control, disease diagnosis, and accurate harvest estimation addressing the utilization of gherkin cultivation in Sri Lanka despite high international demand, especially for pickles. By leveraging YOLOv8, Random Forest, and CNN architectures, the system returned reliable predictions and recommendations for the farmers. The unique approach of this research lies in its capability to deliver high-accuracy predictions, practical recommendations provide quick solutions for pest and disease problems that arise during cultivation and give an overview of the yield at the start of cultivation to the farmers. The innovations presented not only address the progression of Gherkin cultivation but also establish a foundation for future smart agriculture advancements. © 2024 IEEE.

关键词： Adversarial machine learning

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Visualization of Professional Cyclists Analytics

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SN Computer Science 2025年第3期6卷 1-6页

作者： Rize, Denis Sinai, Perry Holohan, Liam Saldanha, Paulo Moskovitch, Robert Software and Information Systems Engineering Ben Gurion University of the Negev Beer Sheva Israel Department of Kinesiology McGill University Montreal Canada

Cycling is an important field of sport and a great example of a sport in which athletes are highly measured due to cycling computers that monitor and document workouts in detail. Leveraging this variety of data, we developed The Velodrome, a web-based analytics tool in collaboration with the Israel Premier Tech pro-cycling team to support decision-making. Unlike traditional tools that focus on individual cyclists, The Velodrome enables comparative analysis of multiple cyclists, assisting coaches and directeur sportifs in race selection, strategic preparation, and training decisions. The Velodrome integrates both objective metrics (e.g., relative power, elevation gain) and subjective metrics (e.g., sleep quality, fatigue level) to provide a holistic view of each cyclist’s physical and mental state. The platform offers various visualizations, including radar and line charts, facilitating multi-cyclist and time-based comparisons. These features enable detailed insights into training loads, performance trends, and readiness for competition, supporting team-level decision-making. © The Author(s) 2025.

关键词： Coaching support Cycling Data visualization Sports analytics

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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

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Computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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