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arXiv 2025年

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2025, CC BY-NC-ND.

关键词： Edge computing

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Advancing Text-to-Image Generation: A Comparative Study of StyleGAN-T and Stable Diffusion 3 under Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 85卷 784-800页

作者： Sadek, Mohamed G. Hassan, A.Y. Diab, Tamer O. Abdelhafeez, Ahmed Benha Engineering Faculty Egypt Electrical Engineering Department Benha Engineering Faculty Benha University 13511 Egypt Computer Science Department Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Recent advances in generative models have revolutionized the technology employed for image synthesis quite significantly, and two paradigms—GANs and diffusion-based models—are leading the pack of innovation. This paper outlines an extensive comparison and analysis of some of the best models across both paradigms, namely StyleGAN-T, DF-GAN, AttnGAN, and BigGAN on the GAN side and Stable Diffusion 3 (SD3), DALL·E 3, Midjourney v6, and Imagen 2 on the diffusion side. We systematically inspect the architectural design, training protocols, text-conditioning processes, and domain adaptability of each model, highlighting how they address text-to-image generation challenges differently. Through qualitative and quantitative measurements—such as FID, CLIP Score, human preference surveys, and compositional accuracy, the work reveals performance tradeoffs concerning speed, control, creativity, semantic alignment, and photorealism. We use the Neutrosophic Set model to select the best model based on these evaluation matrices. We have different scores for each model based on evaluation matrices. So, the neutrosophic set is used to overcome the uncertainty information. We use the COPRAS method to rank the models and select the best one based on the evaluation matrix weights. © 2025, University of New Mexico. All rights reserved.

关键词： Photointerpretation

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An Overview of Artificial Intelligence Ethics

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 799-819页

作者： Huang, Changwu Zhang, Zeqi Mao, Bifei Yao, Xin Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Shenzhen518055 China Huawei Technologies Company Ltd. Trustworthiness Theory Research Center Shenzhen518055 China University of Birmingham School of Computer Science BirminghamB15 2TT United Kingdom

Artificial intelligence (AI) has profoundly changed and will continue to change our lives. AI is being applied in more and more fields and scenarios such as autonomous driving, medical care, media, finance, industrial robots, and internet services. The widespread application of AI and its deep integration with the economy and society have improved efficiency and produced benefits. At the same time, it will inevitably impact the existing social order and raise ethical concerns. Ethical issues, such as privacy leakage, discrimination, unemployment, and security risks, brought about by AI systems have caused great trouble to people. Therefore, AI ethics, which is a field related to the study of ethical issues in AI, has become not only an important research topic in academia, but also an important topic of common concern for individuals, organizations, countries, and society. This article will give a comprehensive overview of this field by summarizing and analyzing the ethical risks and issues raised by AI, ethical guidelines and principles issued by different organizations, approaches for addressing ethical issues in AI, and methods for evaluating the ethics of AI. Additionally, challenges in implementing ethics in AI and some future perspectives are pointed out. We hope our work will provide a systematic and comprehensive overview of AI ethics for researchers and practitioners in this field, especially the beginners of this research discipline. © 2020 IEEE.

关键词： Industrial robots

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Software Framework and Graph-based Methodology for Optimal Patient Appointment Planning

Software Framework and Graph-based Methodology for Optimal P...

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2022 International Conference on ENTERprise Information systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information systems and Technologies, HCist 2022

作者： Söle, Zoltan Baumgartner, Janos Leitold, Daniel Dulai, Tibor Orosz, Akos Vathy-Fogarassy, Agnes University of Pannonia Department of Computer Science and Systems Technology Egyetem str. 10 VeszpremH-8200 Hungary University of Pannonia Department of Electrical Engineering and Information Systems Egyetem str. 10 VeszpremH-8200 Hungary

Increased demands on healthcare make it more and more important to schedule the appointments of the patients' treatments and care optimally. The optimised patient scheduling may decrease the patients' waiting times, reduce the length of the treatments, and utilise healthcare resources more effectively. However, as the patient scheduling problems differ significantly, it raises the question of how a general patient scheduling problem can be formulated and solved independently from the application areas. This study suggests a general way to define and solve the patient scheduling problem. The proposed method utilises the standard elements of the Fast Healthcare Interoperability Resource standard to define the input elements of the patient scheduling problem, then automatically converts the task to a process optimisation problem and solves it using the P-graph methodology. The proposed method can be applied to solve any patient scheduling problem, and it does not require a specific mathematical description and formulation defined for each problem separately. © 2023 Elsevier B.V.. All rights reserved.

关键词： Optimization

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Decision Tree-Based IoT Botnet Attack Detection

Decision Tree-Based IoT Botnet Attack Detection

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors.

关键词： Training Logistic regression Botnet Predictive models Bayes methods Internet of Things Decision trees

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DSCE: Comparative analysis of heuristic computational techniques

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IEIE Transactions on Smart Processing and Computing 2021年第3期10卷 259-272页

作者： Muhammad, Nooh Bany Ghauri, Sajjad A. Sarfraz, Mubashar Aftab, Adnan Department of Computer Science & Information Systems American University of Kuwait Kuwait Department of Electrical Engineering ISRA University Islamabad Campus Pakistan Department of Electrical Engineering NUML Islamabad Pakistan

A frequency-selective fast-fading channel degrades the performance of a signal as it introduces inter-symbol interference (ISI) and inter-carrier interference (ICI). Equalization is the process of eliminating ISI and ICI by adaptively updating the tap weights of the FIR filter. The work presented utilizes various heuristic computational techniques (HCTs) to adaptively update the weights for doubly selective channel equalization (DSCE). Various HCTs are also compared, including genetic algorithms (GAs), particle swarm optimization (PSO), differential evolution (DE), cat swarm optimization (CSO), bee colony optimization (BCO), hybrid GA-PSO (HGP), and the cuckoo search algorithm (CSA). Extensive simulations were performed in MATLAB, and the simulation results show that PSO outperforms the other HCTs in terms of computational time complexity and FoM. Copyrights © 2021 The Institute of Electronics and Information Engineers

关键词： Genetic algorithms

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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A review on current trends on optimal EVCS location 12

A review on current trends on optimal EVCS location

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12th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2024

作者： Carpitella, Silvia Jia, Xudong Narimani, Rasoul Department of Manufacturing Systems Engineering and Management California State University Northridge 18111 Nordhoff St NorthridgeCA91330 United States College of Engineering and Computer Science California State University Northridge 18111 Nordhoff St NorthridgeCA91330 United States Department of Electrical and Computer Engineering California State University Northridge 18111 Nordhoff St NorthridgeCA91330 United States

ISBN: (纸本)9791281988101

This paper presents a comprehensive literature review aimed at analyzing recent advancements in research concerning the optimal location of Electric Vehicle Charging Stations (EVCS) over the past year. The rapid proliferation of electric vehicles needs a thorough understanding of the strategic placement of charging infrastructure to support their widespread adoption. By examining the latest studies and findings in this field, we aim to synthesize evolving methodologies, technologies, and useful considerations associated with determining the most effective locations for EVCS deployment. This review examines current methodologies assessing economic, environmental, and societal implications of EVCS placement strategies, providing insights to improve energy efficiency and sustainability in transportation ecosystems. Through a critical analysis of 54 published papers published in 2023 and 2024, we explore various factors influencing optimal EVCS placement, including geographical, demographic, and urban planning considerations, as well as emerging trends and research gaps. By analyzing the current state of research in this area, this research aims to inform stakeholders in making informed decisions to support the continued growth and integration of electric vehicles into global transportation infrastructures. © 2024 The Authors.

关键词： EVCS Location Literature Review Optimization Techniques

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Enhancing Fault Detection in Stochastic Environments Using Interval-Valued KPCA: A Cement Rotary Kiln Case Study

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IEEE Access 2025年 13卷 102951-102963页

作者： Abdelhalim Louifi Abdelmalek Kouadri Mohamed-Faouzi Harkat Abderazak Bensmail Majdi Mansouri Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M’Hamed Bougara of Boumerdes Boumerdes Algeria Systems and Advanced Material Laboratory Badji Mokhtar–Annaba University Annaba Algeria Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman

Fault detection in industrial processes is challenging due to significant data uncertainty, which complicates the accurate modeling of interval-valued data and the quantification of errors necessary for reliable detection. Existing approaches, such as kernel principal component analysis (KPCA), struggle with these challenges because they rely on single-valued data representations and are unable to effectively handle interval-based variability. To address these limitations, this paper introduces the interval-valued model KPCA (IV-KPCA), which extends KPCA by redefining similarity measures and kernel functions to accommodate interval-valued uncertainty. IV-KPCA preserves the interval structure throughout the modeling process, enhancing robustness to dynamic uncertainties and improving fault detection in complex nonlinear systems. Within this framework, fault detection statistics ( $T^{2}$ , Q, and $\Phi $ ) are developed to enable precise error quantification. The proposed method is validated on a cement rotary kiln process, a highly stochastic industrial system characterized by significant uncertainties. Experimental results demonstrate that IV-KPCA reduces false alarms, missed detections, and detection delays by over 100%, 90%, and 95%, respectively, compared to traditional methods. These findings underscore the potential of IV-KPCA in enhancing fault detection performance in complex, uncertain environments.

关键词： Fault detection Uncertainty Kernel Principal component analysis Data models Monitoring Computational modeling Accuracy Kilns Cement industry

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Improving the Efficiency of Smart Grid Scheduling: A Linear Regression Model with Meta Job Information

Improving the Efficiency of Smart Grid Scheduling: A Linear ...

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of electrical Engineering and information systems German Jordianian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In order to forecast the run time of the jobs that were submitted, this research provides two linear regression prediction models that include continuous and categorical factors. A continuous predictor is built using the number of CPUs, average CPU speed, and memory size, whereas a categorical predictor is built using the user ID, group ID, and executable ID. According to the findings, the prediction rates for categorical and continuous predictors are 61% and 1%, respectively. sixty times better than the earlier models, which used continuous variables as a foundational model to determine task complexity and weight. The effectiveness of the categorical predictor in enhancing a job scheduling problem is then evaluated by combining it with three suggested job scheduling strategies. The suggested algorithms incorporated metrics—predicted run time, waiting time, and resource requirement—to select the smallest jobs. According to the results, Algorithm 3 performs better than earlier models in both performance metrics. The variation in total execution time and average waiting time is 1.14 to 1.76 and 4.5, respectively compared to previous models, Additionally, Algorithms 1 and 2 demonstrating superior performance in every scenario.

关键词： Measurement Processor scheduling Computational modeling Linear regression Predictive models Prediction algorithms Smart grids

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