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Educational data mining: a 10-year review

Discover Computing 2025年第1期28卷 1-25页

作者： Kalita, Emi Oyelere, Solomon Sunday Gaftandzhieva, Silvia Rajesh, Kandala N. V. P. S. Jagatheesaperumal, Senthil Kumar Mohamed, Asmaa Elbarawy, Yomna M. Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif Theodorou, Paraskevi Hilčenko, Slavoljub Hazarika, Jiten Ali, Tazid Centre for Computer Science and Applications Dibrugarh University Dibrugarh India Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå Sweden Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter England Research Group On Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School (JBS) University of Johannesburg Johannesburg South Africa Faculty of Mathematics and Informatics University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria School of Electronics Engineering VIT-AP University Vijayawada India Department of Electronics & Communication Engineering Mepco Schlenk Engineering College Sivakasi India Department of Mathematics Faculty of Science Al-Azhar University (Girls Branch) Cairo Egypt Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir Türkiye Engineering and Informatics Department Klaipėdos Valstybinė Kolegija/Higher Education Institution Klaipeda Lithuania Department of Digital Systems University of Piraeus Piraeus Greece College for Vocational Education of Preschool Teachers and Coaches Subotica Serbia Department of Statistics Dibrugarh University Dibrugarh India

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models

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PeerJ computer science 2025年 11卷 e2646-e2646页

作者： Zga, Adel Zitouni, Farouq Harous, Saad Sallam, Karam Almazyad, Abdulaziz S. Xiong, Guojiang Mohamed, Ali Wagdy Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang China Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt Applied Science Research Center Applied Science Private University Amman Jordan

This study conducts a comparative analysis of the performance of ten novel and wellperforming metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. These algorithms are applied to solve four well-established photovoltaic models: the singlediode model, the double-diode model, the triple-diode model, and different photovoltaic module models. The study focuses on key performance metrics such as execution time, number of function evaluations, and solution optimality. The results reveal significant differences in the efficiency and accuracy of the algorithms, with some algorithms demonstrating superior performance in specific models. The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. This optimizer achieved a root mean square error of 9.8602187789E−04 for the single-diode model, 9.8248487610E−04 for both the double-diode and triplediode models and 1.2307306856E−02 for the photovoltaic module model. This consistent success indicates that the Growth Optimizer is a strong contender for future enhancements aimed at further boosting its efficiency and effectiveness. Its

关键词： Mean square error

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VLM-Enabled Network Experimentation: Case Study of Chanel Capacity in System Limited by η-μ Fading

VLM-Enabled Network Experimentation: Case Study of Chanel Ca...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Dejan Milić Nenad Petrović Milan Jović Radiša Stefanović Mejdin Saliji Elida Suljović Faculty of Electronic Engineering University of Niš Niš Serbia Applied Engineering Sciences Academy of Vocational Studies Polytechnic Belgrade Serbia Faculty of Mathematics and Natural Sciences Cyril and Methodius University Skopje Republic of North Macedonia Informatics and Computer Science Technical School in Novi Pazar Novi Pazar Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

In this paper, a micro-MRC receiver with L branches operating in a correlated gamma-shadowed η-μ fading channel is considered. For such a system, the channel capacity (CC) is determined based on the maximum signal-to-noise ratio (SNR) at the L branches at the input of the MRC receiver. The results are presented graphically to illustrate the impact of different system parameters on performance and the improvements that result from the advantages of combined diversity. Additionally, we introduce the application of Large Language Models (LLMs) to make network experimentation more practical, using the previously presented expression as a case study. Moreover, a Vision Language Model (VLM) aided method aiming to make model-driven network design and experimentation more convenient is introduced and evaluated for the previously considered case study

关键词： Fading channels Wireless communication Visualization Channel capacity System performance Scalability Large language models Receivers Signal to noise ratio

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Software Vulnerabilities Overview:A Descriptive Study

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Tsinghua science and Technology 2020年第2期25卷 270-280页

作者： Mario Calín Sánchez Juan Manuel Carrillo de Gea José Luis Fernández-Alemán Jesús Garcerán Ambrosio Toval the Department of Informatics and Systems Faculty of Computer ScienceUniversity of Murcia

computer security is a matter of great *** the last decade there have been numerous cases of cybercrime based on the exploitation of software *** fact has generated a great social concern and a greater importance of computer security as a *** this work,the most important vulnerabilities of recent years are identified,classified,and categorized individually.A measure of the impact of each vulnerability is used to carry out this classification,considering the number of products affected by each vulnerability,as well as its *** addition,the categories of vulnerabilities that have the greatest presence are *** on the results obtained in this study,we can understand the consequences of the most common vulnerabilities,which software products are affected,how to counteract these vulnerabilities,and what their current trend is.

关键词： descriptive study software security software vulnerabilities vulnerability databases

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Exploring digital interventions for business resilience during crisis-a systematic literature review 16

Exploring digital interventions for business resilience duri...

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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Dzandu, Michael D. De Cesare, Sergio Evans, Richard Tang, Yinshan School of Applied Management Westminster Business School University of Westminster 35 Marylebone Road LondonNW1 5LS United Kingdom Faculty of Computer Science Dalhousie University 6050 University Avenue HalifaxB3H 4R2 Canada Business Informatics Systems and Accounting Henley Business School University of Reading ReadingRG6 6UD United Kingdom

Enterprises responded to the COVID-19 outbreak by deploying various digital technology interventions to achieve business resilience. However, little is known about the nature of enterprise digital technology resilience that emerged during the pandemic and how these can inform businesses to build resilience for future pandemics. The purpose of this study therefore is to explore digital technology interventions and enterprise resilience during the pandemic to inform the development of a pandemic resilience framework for future pandemics. A systematic literature review is being conducted on twenty-three relevant articles sourced from the Scopus database on digital technology intervention and resilience during the COVID-19 pandemic. The analysis of the relevant articles using VOSviewer visualisation revealed five key thematic areas namely information systems, digital health, digital intervention and social media;COVID-19 and wellbeing, and digital resilience. The nature, scope and type of digital technology interventions were mainly data resources, information systems and online platforms. Generally, businesses faced technical, motivational and governance challenges deploying digital technology intervention to emerge resilient from the pandemic. The study concluded that the need for future pandemic preparedness is now, and the lessons learned from businesses in using digital interventions during the recent pandemic are useful. The implications of the study for businesses, policymaking and theorizing in the area of digital intervention and resilience have been highlighted. © 2025 The Author(s).

关键词： business digital technologies Enterprises interventions pandemic resilience

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Tradutor: Building a Variety Specific Translation Model

arXiv

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arXiv 2025年

作者： Sousa, Hugo Almasian, Satya Campos, Ricardo Jorge, Alípio Faculty of Sciences University of Porto Porto Portugal INESC TEC Porto Portugal Institute of Computer Science Heidelberg University Germany Department of Informatics University of Beira Interior Covilhã Portugal Ci2 - Smart Cities Research Center Tomar Portugal

Language models have become foundational to many widely used systems. However, these seemingly advantageous models are double-edged swords. While they excel in tasks related to resource-rich languages like English, they often lose the fine nuances of language forms, dialects, and varieties that are inherent to languages spoken in multiple regions of the world. Languages like European Portuguese are neglected in favor of their more popular counterpart, Brazilian Portuguese, leading to suboptimal performance in various linguistic tasks. To address this gap, we introduce the first open-source translation model specifically tailored for European Portuguese, along with a novel dataset specifically designed for this task. Results from automatic evaluations on two benchmark datasets demonstrate that our best model surpasses existing open-source translation systems for Portuguese and approaches the performance of industry-leading closed-source systems for European Portuguese. By making our dataset, models, and code publicly available, we aim to support and encourage further research, fostering advancements in the representation of underrepresented language varieties. © 2025, CC BY.

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Persona-L has Entered the Chat: Leveraging LLMs and Ability-based Framework for Personas of People with Complex Needs 25

Persona-L has Entered the Chat: Leveraging LLMs and Ability-...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Lipeipei Sun Tianzi Qin Anran Hu Jiale Zhang Shuojia Lin Jianyan Chen Mona Ali Mirjana Prpa Khoury College of Computer Sciences Northeastern University Seattle Washington USA Khoury College of Computer Sciences Northeastern University Vancouver British Columbia Canada Faculty of Computer Science and Informatics Suez Canal University Ismailia Egypt Khoury College of Computer Sciences Northeastern University Vancouver British Columbia Canada and School of Interactive Arts+Technology Simon Fraser University Vancouver British Columbia Canada

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Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review

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PeerJ computer science 2025年 11卷 e2476-e2476页

作者： Dave, Daksh Akhunzada, Adnan Ivković, Nikola Gyawali, Sujan Cengiz, Korhan Ahmed, Adeel Al-Shamayleh, Ahmad Sami Department of Electrical Electronics Birla Institute of Technology and Science Birla Institute of Technology and Science Pilani India College of Computing IT University of Doha for Science Technology Doha Qatar Faculty of Organization and Informatics University of Zagreb Pavlinska Varazdin Croatia Department of Computer Science Lamar University TX United States Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Department of Information Technology The University of Haripur Haripur Pakistan Faculty of Information Technology Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan

The integration of artificial intelligence into healthcare, particularly in mammography, holds immense potential for improving breast cancer diagnosis. Artificial intelligence (AI), with its ability to process vast amounts of data and detect intricate patterns, offers a solution to the limitations of traditional mammography, including missed diagnoses and false positives. This review focuses on the diagnostic accuracy of AI-assisted mammography, synthesizing findings from studies across different clinical settings and algorithms. The motivation for this research lies in addressing the need for enhanced diagnostic tools in breast cancer screening, where early detection can significantly impact patient outcomes. Although AI models have shown promising improvements in sensitivity and specificity, challenges such as algorithmic bias, interpretability, and the generalizability of models across diverse populations remain. The review concludes that while AI holds transformative potential in breast cancer screening, collaborative efforts between radiologists, AI developers, and policymakers are crucial for ensuring ethical, reliable, and inclusive integration into clinical practice. Copyright 2025 Dave et al.

关键词： Diseases

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An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

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computers, Materials & Continua 2025年第5期83卷 1791-1820页

作者： Mehran Tarif Mohammadhossein Homaei Amir Mosavi Department of Computer Science University of VeronaVerona37134Italy Department of Computer Systems Engineering and Telematics University of ExtremaduraCaceres10003ExtremaduraSpain Institute of the Information Society Ludovika University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical UniversityKaraganda100000Kazakhstan

Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater ***,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network *** challenges are coped with by designing advanced routing *** this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network *** method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and *** protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue *** also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs *** to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).

关键词： Underwater sensor networks(UWSNs) routing energy fuzzy logic multipath load balancing

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