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An Image Retrieval System Using Deep learning to Extract High-Level Features 14th

An Image Retrieval System Using Deep Learning to Extract Hig...

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14th international conference on Computational Collective Intelligence (ICCCI)

作者： Jabnoun, Jihed Haffar, Nafaa Zrigui, Ahmed Nsir, Sirine Nicolas, Henri Trigui, Aymen Univ Monastir Res Lab Algebra Numbers Theory & Intelligent Syst Monastir Tunisia Univ Bordeaux LaBRI Lab Talence France DB Consulting 4 Rue Simone de Beauvoir F-94140 Alfortville France

ISBN: (纸本)9783031162107;9783031162091

the usual procedure used in Content Based Image retrieval (CBIR), is to extract some useful low-level features such as color, texture and shape from the query image and retrieve images that have a similar set of features. However, the problem with using low-level features is the semantic gap between image feature representation and human visual understanding. that is why many researchers are devoted for improving content-based image retrieval methods with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. those researchers are mainly focused on combining low level features together to have a better representation of the content of an image, which make it closer to the human visual perception but still not close enough to reduce the semantic gap. In this paper we'll start by a comprehensive review on the recent researches in the field of Image Retrieval, then we propose a CBIR system based on convolutional neural network and transfer learning to extract high-level features, as an initiative part of a larger project that aims to retrieve and collect images containing the Arabic language for natural language processing tasks.

关键词： Image retrieval CNNs Features extraction Transfer learning

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Integrating Reinforcement learning for Behavioral Analysis in Interconnected Electricity and Carbon Markets

Integrating Reinforcement Learning for Behavioral Analysis i...

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IEEE international conference on Power and Energy Systems (ICPES)

作者： Zhe Chen Zeqi Zhang Yingjie Li Zhiwei Huang Shenzhen Power Supply Co. Ltd. Shenzhen China

ISBN: (数字)9798350391329

ISBN: (纸本)9798350391336

the integration of electricity and carbon markets plays a crucial role in promoting renewable energy adoption and achieving global carbon reduction targets. However, the interconnection of these markets introduces significant challenges due to their inherent complexity and dynamic nature. this paper proposes a hybrid experimental learning framework, combining realworld experimentation and reinforcement learning-based generative models, to analyze participant behavior and market dynamics in coupled electricity and carbon *** framework simulates trading processes by modeling strategic bidding behaviors using a reinforcement learning approach, formulated as a Markov Decision Process (MDP). Leveraging both historical and experimental data, the model generates bidding strategies that adapt to market conditions while balancing economic and environmental objectives. A case study based on the Guangdong electricity market, incorporating modified IEEE 6-bus systems and realworld carbon trading scenarios, demonstrates the effectiveness of this approach in capturing market complexities and uncovering distinct behavioral patterns among participants. Results highlight the ability of reinforcement learning-based generative models to replicate human decision-making and optimize bidding strategies in interconnected markets. the findings provide insights into market behaviors, regulatory impacts, and potential strategies for improving efficiency and sustainability in the energy sector. this study underscores the value of hybrid experimental learning as a tool for exploring coupled market dynamics and guiding the transition to low-carbon energy systems.

关键词： Adaptation models Analytical models Renewable energy sources Electricity Biological system modeling Computational modeling Reinforcement learning Complexity theory Carbon Sustainable development

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Modeling Country Economic Security: a Machine learning Approach

Modeling Country Economic Security: a Machine Learning Appro...

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international conference on Advanced Computer Information Technologies (ACIT)

作者： Olha Kovalchuk Kateryna Berezka Ludmila Babala Roman Ivanytskyy Nataliia Karpyshyn Natalia Zhuk Theory of Law and Constitutionalism Department West Ukrainian National University Ternopil UKRAINE Department of Applied Mathematics West Ukrainian National University Ternopil UKRAINE Department of Economic Cybernetics and Informatics West Ukrainian National University Ternopil UKRAINE Department of Physics and Mathematics Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil UKRAINE Department of International Relations and Diplomacy West Ukrainian National University Ternopil UKRAINE Department of Financial and Economic Security Educational and Scientific Humanitarian Institute National Academy of the Security Service of Ukraine Kyiv UKRAINE

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

this study employs a machine learning approach to forecast a country’s level of economic security and investigate the complex relationships between economic factors, security indicators, and the nation’s economic security. An innovative methodology is proposed that accounts for determinants of national security alongside traditional economic indicators. this allowed an assessment of the reverse impact of national security on economic security. the analysis shows that foreign direct investment and the net migration rate are critical factors for economic security. the Global Peace Index, which evaluates the peacefulness and safety of nations, demonstrates a more substantial impact on economic security than oil consumption and wattage consumption. the obtained results underscore nations’ dependence on foreign sources of capital, the importance of balanced migration policies, and the bidirectional interplay between economic and national security.

关键词： Economics Technological innovation Oils Machine learning Predictive models Safety Security Indexes National security Investment

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Modeling Creativity in Visual Programming: From theory to Practice 14

Modeling Creativity in Visual Programming: From Theory to Pr...

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14th international conference on Educational Data Mining, EDM 2023

作者： Kovalkov, Anastasia Paaßen, Benjamin Segal, Avi Gal, Kobi Pinkwart, Niels Ben-Gurion University of the Negev Israel Humboldt-University of Berlin Germany Ben-Gurion University of the Negev University of Edinburgh United Kingdom

ISBN: (纸本)9781733673624

Promoting creativity is considered an important goal of education, but creativity is notoriously hard to define and measure. In this paper, we make the journey from defining a formal creativity and applying the measure in a practical domain. the measure relies on core theoretical concepts in creativity theory, namely fluency, flexibility, and originality, We adapt the creativity measure for Scratch projects. We designed a machine learning model for predicting the creativity of Scratch projects, trained and evaluated on ratings collected from expert human raters. Our results show that the automatic creativity ratings achieved by the model aligned with the rankings of the projects of the expert raters more than the experts agreed with each other. this is a first step in providing computational models for describing creativity that can be applied to educational technologies, and to scale up the benefit of creativity education in schools. © EDM *** rights reserved.

关键词： Computation theory

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Interdisciplinary Competence Prediction for Engineering College Students Based on Decision Tree

Interdisciplinary Competence Prediction for Engineering Coll...

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international conference on Educational and Information Technology (ICEIT)

作者： Zhiwei Oi Wei Xu Yuanvuan Wang School of Education Yunnan University Yunnan China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

With the increasing complexity of engineering problems, it is difficult for single-discipline knowledge to solve complex engineering problems, and the cultivation of interdisciplinary ability has become the main goal of engineering education. However, the process of interdisciplinary competence development is affected by many factors, such as student factors, teacher factors, school factors, and artificial intelligence factors. Firstly, we collected data on the influencing factors of interdisciplinary competence, and secondly, we performed data processing and explored the importance of each influencing factor on interdisciplinary competence using the classification and regression trees algorithm. the prediction model and 7 decision rules were obtained after optimizing the decision tree using the cost complexity pruning algorithm, the model has an accuracy rate of 75.1%. Finally, we analyze the experimental results and conclude that teachers' teaching style is the main factor affecting interdisciplinary competence, followed by the frequency of using smart tools, students' motivation to be interdisciplinary, the availability of school facilities, teachers' interdisciplinary background, and school curriculum as important influencing factors. the results of this study will provide applied guidance for the development of interdisciplinary competence among engineering college students.

关键词： Knowledge engineering Machine learning Predictive models Prediction algorithms Data processing Complexity theory Classification algorithms Information technology Engineering education Regression tree analysis

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Welcome Message from the KSE 2022 General Committee

Welcome Message from the KSE 2022 General Committee

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international conference on Knowledge and Systems Engineering (KSE)

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Exposing the Attraction of Data- An Examination and Application of Teaching Data Visualization in Multimedia learning Mode From theory to Practice

Exposing the Attraction of Data- An Examination and Applicat...

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IEEE international Symposium on Information (IT) in Medicine and Education, ITME

作者： Yue Wang Mingyou Liu Yingxue Zhu Sitong Liu School of Biology and Engineering(Health Medicine Modern Industry College) Guizhou Medical University Guizhou China University of Nottingham Ningbo China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the advent of the big data era, data visualization technology has grown in significance and become an essential core technology in the fields of statistical analysis and decision support. However, the current approaches of teaching the Data Visualization course tend to downplay the significance of theoretical knowledge in favor of developing students' practical skills with various information visualization technologies. When students are faced with complex facts, this teaching style, which prioritizes practice over theory, may result in a lack of deep analytical skills and creative thinking. Meanwhile, in order to reveal the charm of data, the process of transforming data information into visual form can significantly improve the presentation effect of data, and observe and browse the data in an intuitive and interactive way, so that it is easier to find the hidden features, relationships, and patterns in the data, which can provide powerful support for data analysis and *** order to create a naturally occurring combination of knowledge and application and better prepare students for the difficulties of the big data era, teaching Data Visualization educational programs using a blended learning approach requires maintaining a balance between the two.

关键词： Visualization Educational programs Data analysis Statistical analysis Data visualization Big Data Hybrid learning Information technology

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Development and Validation of the Online Course User Experience Scale: A Multidimensional Assessment Framework

Development and Validation of the Online Course User Experie...

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international conference on Educational and Information Technology (ICEIT)

作者： Mei Wang Siva Shankar Ramasamy Ahmad Yahya Dawod Shuai Wang Haoyu Zhang International College of Digital Innovation Chiang Mai University Chiang Mai Thailand Xiangyangqiao Primary School Chengdu China Scholl of Information and Engineering Sichuan Tourism University Chengdu China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

this study, grounded in the theoretical framework of online course user experience (UX), developed and validated a comprehensive scale to evaluate learners' experiences in online courses and their influencing factors. through a systematic review of the literature and expert consultations, the dimensions of the scale were determined. Subsequently, exploratory factor analysis (EFA) was conducted on 157 pilot survey samples to establish the structure of the scale. Confirmatory factor analysis (CFA) was then performed on 457 formal survey samples using AMOS to construct a second-order structural equation model, assessing the reliability and validity of the scale. the final scale comprises six primary dimensions, 13 secondary factors, and 45 measurement items. the findings indicate that the scale demonstrates good reliability, validity, and discriminant validity, confirming the multidimensional nature of online course UX and the theoretical soundness of the proposed framework. this study provides a valuable tool for evaluating online course UX and offers a reference for optimizing course design and improving learning outcomes.

关键词： Surveys Numerical analysis Education Reliability theory Vocational training Mathematical models User experience Interviews Information technology Systematic literature review

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Robust Low-Rank Matrix Factorization via Block Iteratively Reweighted Least-Squares

Robust Low-Rank Matrix Factorization via Block Iteratively R...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Nanxi Li Zhen-Qing He Jianchi Zhu Xiaoming She Peng Chen Ying-Chang Liang China Telecom Research Institute Beijing China National Key Lab of Communications University of Electronic Science & Technology of China Chengdu China Yangtze Delta Region Institute (Huzhou) University of Science and Technology of China Huzhou China

ISBN: (纸本)9781665450867

In this paper, we concentrate on the problem of low-rank matrix factorization (LRMF) in the presence of outliers and missing observations. this problem is paramount in practice as a variety of applications, such as signal processing, wireless communications, and machine learning, fit such an LRMF paradigm. We introduces a general LRMF formulation of minimizing the outlier robust $\ell_{p}$ -based fitting term plus structure-promoting constraints, including nonnegativity and/or sparsity. To address this computationally intractable problem, we develop an algorithmic framework, named block iteratively reweighted least-squares (BIRLS). Each iteration of BIRLS is obtained by minimizing a regularized reweighted $\ell_{2}$ -based problem with a closed-form solution in an interweaved block update fashion. Meanwhile, we prove that the whole iterative sequence of BIRLS is convergent and converges to a stationary point of the original problem. Numerical experiments demonstrate the superiority of the proposed algorithm.

关键词： Wireless communication Resistance Machine learning algorithms Fitting Signal processing algorithms Machine learning Signal processing

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Machine learning Models For Information Support In the Justice System

Machine Learning Models For Information Support In The Justi...

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international conference on Advanced Computer Information Technologies (ACIT)

作者： Olha Kovalchuk Vladyslav Teremeckyi Andrii Kolesnikov Natalia Chudyk Valerii Kaniuka Theory of Law and Constitutionalism Department West Ukrainian National University Ternopil Ukraine National Academy of Legal Sciences of Ukraine Academician F.H. Burchak Scientific Research Institute of Private Law and Entrepreneurship Kyiv Ukraine Department of Security and Law Enforcement West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

the limited ability of people to efficiently and quickly process complex and diverse information from big data has led to the need for digital transformation, which has already become a reality for justice systems. the work presents an effective gradient-boosted decision tree model for predicting recidivism risk levels based on information about previous criminal activity, and demographic, and social characteristics of convicts. the dataset for applied research includes information about 13,010 individuals serving sentences in correctional facilities in Ukraine. Established that the number of previous convictions before serving sentences in correctional institutions and the number of suspended convictions imposed by court decisions in previous cases significantly influence the propensity of convicts to commit repeat offenses. the proposed machine learning model can be used to predict recidivism risk levels for new convicts, provide reliable information support for judicial decision-making, and become part of the judicial information system in Ukraine.

关键词： Law Decision making Predictive models Boosting Public security Reliability Decision trees Information technology Standards Information systems

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