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Suppression and synchronization of chaos in uncertain time-delay physical system

applied Mathematics(A Journal of Chinese Universities) 2024年第3期39卷 416-437页

作者： Israr Ahmad Muhammad Shafiq Department of Preparatory Studies Centre University of Technology and Applied SciencesNizwaOman Department of Electrical and Computer Engineering Sultan Qaboos UniversityOman

The mechanical horizontal platform(MHP)system exhibits a rich chaotic *** chaotic MHP system has applications in the earthquake and offshore *** article proposes a robust adaptive continuous control(RACC)*** investigates the control and synchronization of chaos in the uncertain MHP system with time-delay in the presence of unknown state-dependent and time-dependent *** closed-loop system contains most of the nonlinear terms that enhance the complexity of the dynamical system;it improves the efficiency of the *** proposed RACC approach(a)accomplishes faster convergence of the perturbed state variables(synchronization errors)to the desired steady-state,(b)eradicates the effect of unknown state-dependent and time-dependent disturbances,and(c)suppresses undesirable chattering in the feedback control *** paper describes a detailed closed-loop stability analysis based on the Lyapunov-Krasovskii functional theory and Lyapunov stability *** provides parameter adaptation laws that confirm the convergence of the uncertain parameters to some constant *** computer simulation results endorse the theoretical findings and provide a comparative performance.

关键词： chaotic horizontal platform system chaos suppression chaos synchronization robust adaptive control Lyapunov stability theory

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Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

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Computational Visual Media 2023年第3期9卷 513-529页

作者： Yang Yang Kecheng Lu Yu Wu Yunhai Wang Yi Cao Institute of Applied Physics and Computational Mathematics Beijing 100094China School of Computer Science and Technology Shandong UniversityQingdao 266237China

In this paper,we propose a correlationaware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific *** core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information,spatial location,and correlation distribution using Bayes’*** effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them,thus significantly reducing the computational ***,this enables reconstruction of the original data more accurately than existing *** demonstrate the effectiveness of our technique using six datasets,with the largest having one billion grid *** experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.

关键词： correlation-awareness large-scale data multi-block methods probabilistic data summarization

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Theoretical Models of Adoption and Acceptance of e-Learning Prior and During Covid19 4

Theoretical Models of Adoption and Acceptance of e-Learning ...

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Kolo, Ditlalane Irene Zuva, Tranos Applied Computer Sciences Vaal University of Technology Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

Prior to the Covid19 pandemic, the educational methods used were physical face to face teaching, e-learning, and blended learning, which is a combination of physical teaching and e-learning. During the Covid 19 pandemic, physical education was not possible, leading to e-learning becoming the favorable method of administering education. While e-learning became the compulsory method during the Covid 19 pandemic, there was a limited time for adoption and acceptance of e-learning. This study reviewed the models and/or frameworks for adoption and acceptance prior and during Covid19 in e-learning to identify necessary variables for a pandemic period. The inclusion and exclusion criteria were used to select the papers assessed. The assessment acknowledged 18 related studies published prior and during Covid19. The results showed that prior to the pandemic period, there were variables used which were not included during the pandemic period in adopting and accepting the e-learning system. Out of 52 variables discovered in this study, 7 variables are confirmed to be common/core variables which are exogenous and/or endogenous. The 7 common variables are Social Influence, Facilitating Condition, Behavioral Intention, Perceived Usefulness, Perceived Ease of Use, Attitude and Intension to use. It further showed that Perceived Ease of Use and Perceived Usefulness were the most crucial common variables used during the pandemic. The core variables are indicators of adoption and acceptance of technologies in any environment. Therefore, it is concluded that the research studies are influenced by outbreak of disease pandemic or any natural disaster that can occur, as seen during the Covid19 pandemic the new variables were initiated. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-strategy Hybrid Coati Optimizer:A Case Study of Prediction of Average Daily Electricity Consumption in China

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Journal of Bionic Engineering 2024年第5期21卷 2540-2568页

作者： Gang Hu Sa Wang Essam H.Houssein Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Faculty of Computers and Information Minia UniversityMinia61519Egypt

The power sector is an important factor in ensuring the development of the national *** simulation and prediction of power consumption help achieve the balance between power generation and power *** this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity ***,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning *** improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the ***,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test ***,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting *** experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.

关键词： Forecast of average daily electricity consumption in China Coati optimization algorithm Three-parameter combination optimization model Circle chaotic mapping

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Body Weight-Based Animal Laboratory Classification Utilizing Load Cell and IoT 12

Body Weight-Based Animal Laboratory Classification Utilizing...

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12th International Conference on Information and Communication technology, ICoICT 2024

作者： Mutiara, Giva Andriana Hapsari, Gita Indah Alfarisi, Muhammad Rizqy Periyadi Meisaroh, Lisda Adisty Hadian, Nadya Nanda Computer Technology Applied Science School Telkom University Bandung Indonesia

ISBN: (纸本)9798350363432

Animal laboratory experiments have important role in the development of specialist science related to health, biology, and pharmacy. To carry out the trial of medicines, the researchers require an animal laboratory experiment. In this case, mice and white rat which have the same basic properties, mammals as human used to find out whether the medicine reacts and suitable for curing various diseases. However, the research still experiences challenges in the process of selecting rats or mice that are suitable for experiments because it is still conducted manually, which still requires expertise from the researcher. This research proposes a system that can detect laboratory animals based on body weight, therefore it can help researchers in sorting out animal laboratory. This system is designed using load cell sensor, HX711 module, ESP32, LCD, motor servo, photodiode sensors as counter circuit. The system is linked to desktops and smartphones by utilizing IoT technology for reporting, monitoring, and manual control. The test results indicate that the accuracy of the weight measurement system still has a difference in the weight measurement range of 0.01 grams to 0.5 grams. Meanwhile, the percentage of success of the animal weight classification system reached 95% in 20 tests. © 2024 IEEE.

关键词： Rats

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Predicting Chess Puzzle Difficulty with Transformers

Predicting Chess Puzzle Difficulty with Transformers

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Milosz, Szymon Kapusta, Pawel Lodz University of Technology Institute of Applied Computer Science Lodz Poland

ISBN: (纸本)9798350362480

This study addresses the challenge of quantifying chess puzzle difficulty - a complex task that combines elements of game theory and human cognition and underscores its critical role in effective chess training. We present GlickFormer, a novel transformer-based architecture that predicts chess puzzle difficulty by approximating the Glicko-2 rating system. Unlike conventional chess engines that optimize for game outcomes, GlickFormer models human perception of tactical patterns and problem-solving *** proposed model utilizes a modified ChessFormer backbone for spatial feature extraction and incorporates temporal information via factorized transformer techniques. This approach enables the capture of both spatial chess piece arrangements and move sequences, effectively modeling spatio-temporal relationships relevant to difficulty *** evaluation was conducted on a dataset of over 4 million chess puzzles. Results demonstrate GlickFormer's superior performance compared to the state-of-the-art ChessFormer baseline across multiple metrics. The algorithm's performance has also been recognized through its competitive results in the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty competition, where it placed *** insights gained from this study have implications for personalized chess training and broader applications in educational technology and cognitive modeling. © 2024 IEEE.

关键词： Deep learning

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Research on Campus Public Opinion Registration and Management System Based on KMP Algorithm 24

Research on Campus Public Opinion Registration and Managemen...

引用

2nd International Conference on Educational Knowledge and Informatization, EKI 2024

作者： Su, Jianna Zhao, Lin Wei, Xinyao Department of Mathematics and Computer Engineering Ordos Institute of Applied Technology China

ISBN: (纸本)9798400710230

In the context of the digital age, online public opinion has rapidly emerged as a key manifestation of social sentiment. The spread of campus public opinion is primarily through the internet, characterized by high attention, rapid dissemination, and a special group, which can easily lead to secondary social consequences and has a profound impact on school management, student mental health, and the formulation of educational policies. This requires school administrators to be able to capture and effectively analyze public opinion dynamics in a timely manner to make appropriate responses. However, in the face of massive amounts of public opinion data, traditional monitoring and management methods are no longer sufficient. This paper proposes a campus public opinion registration and management system based on the Knuth-Morris-Pratt (KMP) algorithm, aiming to improve the efficiency and accuracy of public opinion monitoring. © 2024 Copyright held by the owner/author(s).

关键词： Economic and social effects

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Fog Computing - Desktop Review 4

Fog Computing - Desktop Review

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Sithole, Sicelo Prince Sonhera, Naume Zuva, Tranos Vaal University of Technology Faculty of Applied and Computer Sciences Department of Computer Science Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

Fog computing is an innovative approach that brings cloud computing capabilities to the edge of the network, and it has great potential for enterprises looking to improve real-time data processing and lower latency. However, the adoption of fog computing in organizations remains relatively unexplored. This review paper aims to investigate the factors influencing fog computing adoption in organizations. Through a systematic review of existing literature, case studies, and expert opinions, this paper identifies and analyses the unique adoption factors. The review examines challenges, characteristics, benefits, and Open Fog Computing Issues with the Internet of Things (IoT), shaping fog computing adoption decisions in organizations across various industries. This analysis highlights fog computing advantages over traditional cloud computing approaches, such as low latency, improved data privacy, and enhanced scalability. Furthermore, this review investigates the challenges hindering the adoption of fog computing, including security concerns, implementation complexity, connectivity, and lack of awareness, as well as open fog computing issues with IoT. Key findings indicate that while fog computing can offer low-latency solutions, organizations face considerable challenges, including security risks, implementation complexity, and a lack of skilled personnel. By synthesizing and analysing the findings, this paper provides valuable insights for decision-makers, researchers, and policymakers seeking to promote and facilitate fog computing adoption. © 2024 IEEE.

关键词： Fog computing

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A Review of Evaluation Metrics Algorithmic Used in Scholarly Research Recommender System 4

A Review of Evaluation Metrics Algorithmic Used in Scholarly...

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Ovono, Gerald Zuva, Travos Otunniyi, Temidayo Faculty of Applied and Computer Sciences Department of Computer South Africa VaalUniversity of Technology Faculty of Engineering and Technology Department of Process Control and Computer Systems South Africa

ISBN: (纸本)9798350387988

The exponential growth of academic publications has made scholarly research recommender systems indispensable tools for researchers. These systems rely on diverse evaluation metrics to assess their effectiveness and relevance. This paper provides a comprehensive review and analysis of these metrics, categorizing them into accuracy, ranking, diversity, user-centric, coverage, temporal, and computational metrics. By employing a mixed-methods approach, combining systematic literature review and empirical analysis, we identify the prevalence and application of these metrics across various scholarly domains such as literature, conferences, and reviewers. Our findings highlight the widespread use of traditional metrics like precision, recall, and Fl-score, while emphasizing the growing importance of novel metrics such as unexpectedness and serendipity, especially in literature and conference recommendations. We also underscore the domain-specific nature of metric selection, with user-centric evaluations more prominent in literature recommendations, and ranking-focused metrics dominant in conference and reviewer recommendations. Furthermore, the study reveals a significant gap in the use of temporal and computational metrics, suggesting key areas for future research. This review offers valuable insights for researchers and practitioners, guiding them in metric selection and highlighting potential directions for enhancing the effectiveness, adaptability, and user satisfaction of scholarly recommender systems. © 2024 IEEE.

关键词： Algorithms Evaluation Metrics Novelty Recommender Systems Scholarly Research Serendipity

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