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2nd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2022

作者： Chang, Chengyan Yu, Tao Chen, Zhenping Zhang, Huajian Kuang, Lanjun College of Computer Science School University of South China Hengyang China University of South China Virtual Simulation Experiment Teaching Center on Nuclear Energy and Technology Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9798350345759

In nuclear reactor design analysis, Monte Carlo (MC) programs are widely used for simulation calculations of particle transport. Traditionally, MC program input files are constructed by manual creation, but with the increasing complexity of advanced nuclear reactor models, the traditional input file construction method can no longer meet the application requirements, and it is important to develop an auxiliary modeling program for MC programs. In this paper, based on the open source platform SALOME, we conducted a study on the conversion of CAD 3D geometric model to MC computational model using decomposition-based BREP-CSG conversion algorithm and cavity generation algorithm, and developed an auxiliary modeling program MOSRT. Based on the MOSRT program, the user can create or import CAD 3D geometry models and convert them into MC program input files corresponding to the geometry through the program. To verify the effectiveness of the MOSRT program model conversion function, the NEA published benchmark example C5G7 model and the critical experimental device model were used for geometric verification. The results show that the MOSRT program developed based on the method of this paper can accurately convert the CAD 3D geometric model into the corresponding MC program calculation model, which verifies the correctness and reliability of the methods and program of this paper. © 2022 IEEE.

关键词： computer aided design

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis 27

Exploring the Impact of Random Distribution Choices on Parti...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Kolodziejczyk, Joanna Tarasenko, Yuliia Sulikowski, Piotr Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Basic concepts and stages of research development on artificial intelligence

Basic concepts and stages of research development on artific...

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2021 International Conference on Information science and Communications Technologies, ICISCT 2021

作者： Raximov, Nodir Primqulov, Oybek Daminova, Barno Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. of Software of Information Technologies Tashkent Uzbekistan Karshi State University Methods of Teaching Computer Science Karshi Uzbekistan

ISBN: (纸本)9781665432580

The article describes artificial intelligence, which is now widely used and developed in the field of information technology and is a part of our life. The basic concepts and classifications of artificial intelligence, its features, areas of research, analytical data on the stages of development are described. At the same time, the role and functions of artificial intelligence in intelligent systems, as well as approaches in the field of application are presented. © 2021 IEEE.

关键词： Intelligent systems

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Predictive Modelling of Diabetes Using Ensemble Classifiers: Findings from a Large and Integrated Data Set

Predictive Modelling of Diabetes Using Ensemble Classifiers:...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Muhamad Azam Rashid Ali Ranya Muqaddas Muhammad Ashraf Ammara Zahoor Department of Computer Science and Information Technology The Superior Universty Lahore Pakistan Department of Computer Science and Information Technology The Superior University Lahore Pakistan Information Engineering Technology Department The Superior Universty Lahore Pakistan Information Technology Department Gulab Devi Teaching Hospital Lahore Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

In this research paper, we examine which of a number of machine learning classifiers are able to predict diabetes in a dataset of 100,000 cases where features include gender, age, state, binary health indicators, smoking, body mass index (BMI), and glucose level, one of which was known to be diabetic. Extensive exploratory data analysis was undertaken to explore the distribution and the relationships between features of the data set and these predictions. (Prajapati, (2023, July).We then evaluated the model output using metrics like as the Accuracy, AUC-ROC, Precision-Recall curves. Support Vector Machine (SVM), Voting Classifier (VC), and Stacking Classifier (SC). The output of these models was then evaluated with the help of measures using metrics like accuracy, AUC-ROC, and Precision-Recall curves. (Hasan, 2020). The highest accuracy of 98.75% was achieved by the voting classifier (VC), Random Forest (RF) was second with 97.18%, and XGBoost was 3rd with 97.15%. The results of this research can be applied in diabetes prediction, with an important conclusion on how ensemble methods could improve the accuracy indicators for healthcare applications.

关键词： Support vector machines Measurement Accuracy Stacking Medical services Predictive models Data models Diabetes Glucose Random forests

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Digital Competence of Specialists: Development Technology in a Higher Education Institution

Digital Competence of Specialists: Development Technology in...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Halyna Henseruk Serhii Martyniuk Oksana Vasylenko Yuliia Henseruk Viktor Henseruk Valerii Habrusiev Department of Computer Science and Teaching Methods Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine Department of Theory and Practice of Translation Anhalt University of Applied Sciences Köthen Germany Department of English Philology and Methods of Teaching English Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine Department of Pedagogy and Management of Education Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

In a digital society, professionals who are professionally competent and have a variety of skills are increasingly in demand. Digital skills are important and form the basis of the digital competence of specialists. The purpose of the study is to analyse regulatory documents in the context of the development of digital competence of specialists, to develop, substantiate and implement the trajectory of its development in a higher education institution. A questionnaire for self-assessment by bachelor’s and master’s students and practitioners of their own level of digital competence has been developed, which contains 12 descriptors and their detailed description. Based on the analysis of regulatory documents and the survey results, the author outlines the need to include disciplines in the training programmes for specialists that would contribute to the development of their digital competence. The authors have developed a technology for the system of development of digital competence of specialists. The design stage is substantiated, which includes three blocks: academic disciplines, self-education and professional self-development. The role of professional disciplines in digital technologies in the development of digital competence of specialists is determined. The study emphasises that continuity and continuity in the training of specialists in the context of the use of digital technologies in various aspects of activity is important in this development.

关键词： Training Surveys Text analysis Trajectory Information technology

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Economic and Mathematical Model of a Group of Gas Fields 14

Economic and Mathematical Model of a Group of Gas Fields

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Solomatin, A.N. Federal Research Center 'Computer Science and Control Russian Academy of Sciences Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (纸本)9781665412308

Economic and mathematical model of a group of gas fields is considered allowing forming the group development strategies for the long-term perspective. An algorithm for calculating the dynamics of gas production by fields and methods for calculating numerous technical and economic indicators of gas production and transport are presented. In addition, information is provided about the software implementation and use of the developed mathematical apparatus. © 2021 IEEE.

关键词： Gas industry

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STEM course on circuit design accomplished to enhance scientific and technological capabilities of students 3

STEM course on circuit design accomplished to enhance scient...

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3rd International Conference on sciences and Technology Applications, ICoSTA 2021

作者： Vexler, V. Aibashev, M. Saipbekova, A. Fominykh, I. Bazhenov, R. Department of Information Systems and Technologies in Education Saratov State University 83 Astrakhanskaya Saratov410012 Russia Department of Accounting and Economic Analysis Osh State University 331 Lenina Osh723500 Kyrgyzstan Department of Applied Mathematics Computer Science Osh State University 331 Lenina Osh723500 Kyrgyzstan Department of Methods of Teaching Mathematics Computer Science and Natural Science Disciplines Mari State University 6 Eshkinina Yoshkar-Ola424033 Russia Department of Information Systems and Mathematics Sholom-Aleichem Priamursky State University 70A Shirokaya Birobidzhan679015 Russia

The aim of the research is to develop an interdisciplinary course of extracurricular activities integrating knowledge in science, technology, engineering and mathematics for students to develop their scientific and technological skills. 52 students from secondary schools of Engels, Yoshkar-Ola (Russia), Sholom-Aleichem Priamursky State University (Birobidzhan, Russia), Osh State University College (Osh, Kyrgyz Republic) took part in the educational experiment. The theme-based course Basic Concepts of Arduino Systems Engineering is developed based on the proprietary technology of simple projects of practical relevance. The projects render the general concept from knowledge to competence. The course consists of four modules: Start, Big Workshop/Hands-on Training, Go Ahead/Step forward, Group Design. Arduino applied in design modeling allows students to create electrical devices that would be productive in one or another area of life at a low cost. The course program is aimed at engaging students in innovative design technologies, computer programming and using robotic devices. The findings of the study were observed as part of an interview with students, educational achievement, subsequent STEM literacy test activities and the performance of game summarizing projects. The recommended method has shown the efficiency in mastering the required core set of skills of circuit design and computer programming. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence

关键词： Students

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Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

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computer Systems science & Engineering 2023年第12期47卷 3049-3065页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Jaber S.Alzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Abdullah Mohamed Ishfaq Yaseen Gouse Pasha Mohammed Mohammed Rizwanullah Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAflaj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined *** affect Analysis has become an active research zone *** still,the Arabic language lags behind adequate language sources for enabling the SA ***,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct *** has gained lesser effort than the other *** paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic *** presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic ***,the MVODRL-AA model follows data pre-processing and word ***,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic *** last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA *** simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.

关键词： Arabic language Arabic corpus natural language processing affect analysis deep learning

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A note on a ode model of micro rna-dependent regulation of messenger rna levels

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2021年第4期83卷 171-176页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

We study the qualitative behavior (invariance, boundedness and equilibria) of the solutions of a mathematical model describing the dynamics of microRNA AGO complexes and mRNA targets interaction. © 2021, Politech... 详细信息

关键词： Ordinary differential equations

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Single Image 3D Reconstruction Algorithm Based on Parallel Double-Branch Pyramid Stacking Network 5

Single Image 3D Reconstruction Algorithm Based on Parallel D...

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2023 5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Ku, Yintao Zhang, Yumei Yang, Honghong Zhao, Yanqing Li, Jinshan Zhou, Yulin Wu, Xiaojun Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi'an China School of Computer Science Shaanxi Normal University Xi'an China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an China

Limited visual information contained in single images and complex motion models of objects may lead to severe fragmentation and confusion of model backbone in the 3D reconstruction of objects. In order to fully extract feature information in a single image and reduce noise interference caused by environmental factors in 3D reconstruction, a Parallel Dual-Branch Pyramid Stacking Network (PDB-PSN) model is proposed. A parallel dual-branch network model is used to construct an encoder-decoder framework based on cascaded feature extraction, encode/decode high and low-resolution features in a single image, and then convert from 2D to 3D through implicit functions to achieve the 3D reconstruction of target objects in a single image. A cascaded feature extraction network is used as a low-resolution feature extraction network to extract global features of objects. In the high-resolution feature extraction branch, three concatenated hourglass networks and dilated convolutions are used in the hourglass network to increase the receptive field and obtain more global information in order to maintain the integrity of the reconstructed object limbs. A threshold processing module is set to remove irrelevant information and ensure the integrity of global information, and meanwhile, to reduce the interference of irrelevant noise information. Simulation experiments on a self-built terracotta dataset show that the PDB-PSN model can completely reconstruct the 3D model in a single image and effectively eliminate model fragmentation in the reconstruction results. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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