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2024 10th International Conference on Education and Training Technologies, ICETT 2024

作者： Woo, Bary Cheong, Ngai Peng, Li Liu, Gaojian Faculty of Applied Sciences Macao Polytechnic University China Computer Science and Technology Hunan University of Science and Technology China

ISBN: (纸本)9798400717895

This research endeavors to scrutinize the influence of courses on students' final year project (FYP) scores and prognosticate FYP scores by applying methodologies such as clustering analysis, decision trees, logistic regression, and support vector machines. By scrutinizing the correlation between students' course grades and FYP scores, the objective is to gain deeper insights into the impact of diverse courses on FYP performance and offer tailored learning recommendations. Initially, clustering analysis is employed to categorize students into distinct groups, discerning the potential impact of specific course combinations on FYP outcomes. Subsequently, machine learning algorithms, including decision trees, logistic regression, and support vector machines, are leveraged to forecast FYP scores based on students' course grades. Through these analyses, customized learning recommendations and guidance can be provided to augment students' FYP performance, fostering their academic advancement and maturation. The findings of this research are pivotal for educational institutions and individual students, contributing to the enhancement of curriculum design and pedagogical methodologies, thereby elevating the standard of education and enriching students' learning encounters. Furthermore, drawing upon assessment data spanning over 16 years and encompassing more than 2,000 non-CS majors at MPU, we delve into the factors influencing student learning quality and proffer timely learning recommendations based on amassed student subject grades and FYP scores. © 2024 ACM.

关键词： Contrastive Learning

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A Methodology Based on Quality Gates for Certifiable AI in Medicine: Towards a Reliable Application of Metrics in Machine Learning

A Methodology Based on Quality Gates for Certifiable AI in M...

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18th International Conference on Software Technologies, ICSOFT 2023

作者： Elia, Miriam Bauer, Bernhard Faculty of Applied Computer Science University of Augsburg Germany

ISBN: (纸本)9789897586651

As of now, intelligent technologies experience a rapid growth. For a reliable adoption of those new and powerful systems into day-to-day life, especially with respect to high-risk settings such as medicine, technical means to realize legal requirements correctly, are indispensible. Our proposed methodology comprises an approach to translate such partly more abstract concepts into concrete instructions - it is based on Quality Gates along the intelligent system’s complete life cycle, which are composed of use-case adapted Criteria that need to be addressed with respect to certification. Also, the underlying philosophy regarding stakeholder inclusion, domain embedding and risk analysis is illustrated. In the present paper, the Quality Gate Metrics is outlined for the application of machine learning performance metrics focused on binary classification. © 2023 by SCITEPRESS-science and Technology Publications, Lda.

关键词： Adversarial machine learning

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An Adaptive Framework for Classification and Detection of Android Malware

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 59-73页

作者： Al Sharah, Ashraf Alrub, Yousef Abu Owida, Hamza Abu Elsoud, Esraa Abu Alshdaifat, Nawaf Khtatnaha, Hamzah Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. © 2024 by the authors of this article.

关键词： Android malware

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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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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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Partial Differential Equation Solving Model of TgU-Net Based on Golden Spiral Water Flow Optimizer 7

Partial Differential Equation Solving Model of TgU-Net Based...

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Yan, Jin Wang, Jian Xue, Xiaofeng Ablameyko, Sergey V. Bai, Junting Qingdao China Belarusian State University Faculty of Applied Mathematics and Computer Science Minsk220030 Belarus

ISBN: (纸本)9798350370782

Recently, theory-guided neural networks have attracted significant attention in solving partial differential equations due to their minimal data requirements and alignment with physical laws. However, selecting the penalty coefficient by incorporating physical laws as penalizing terms in the loss function undoubtedly affects the model's performance. In this paper, we utilize physical laws to guide U-Net neural networks and employ bilevel programming to tune the loss function's hyperparameters, thereby improving the overall model performance. The upper-level variables within the framework are optimized using a water flow optimizer based on the golden spiral eddying function, which enhances population diversity and broadens the search range in the turbulent flow operator. Experimental results confirm the effectiveness of this approach in handling stochastic partial differential equations. © 2024 IEEE.

关键词： Stochastic systems

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Enhanced Form-Based SPARQL Builder: Effortless Access to Temporal Medical Data for Users Without Technology Knowledge 17

Enhanced Form-Based SPARQL Builder: Effortless Access to Tem...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Vcelak, Petr Mika, Ondrej Kryl, Martin Kleckova, Jana University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Plzen Czech Republic

ISBN: (纸本)9798331507398

Accessing complex medical data, especially temporal information, presents a significant challenge for non-technical users, including healthcare professionals not versed in technology or query languages like SPARQL. This paper introduces an enhanced form-based SPARQL query builder that offers effortless access to temporal medical data. The described system simplifies constructing SPARQL queries into an intuitive, form-driven interface that abstracts and hides the technical complexities. Novel enhancement is primarily the user's ability to filter and order data with column constraints. By focusing on usability, the system empowers users to retrieve temporal medical information without needing in-depth knowledge of database systems or SPARQL syntax. We evaluate the system's usability and performance by testing it with non-technical healthcare professionals. Results demonstrate that users can easily navigate the interface, significantly reduce the time spent querying the database, and effectively retrieve data. This approach benefits from FAIR data principles and enhances data reusability in healthcare settings for reaching accuracy and precision for follow-up analysis in evidence-based research, facilitating better decision-making that can improve future patient outcomes. © 2024 IEEE.

关键词： Spatio-temporal data

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Radial solution of the Logarithmic Laplacian system

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applied Mathematics(A Journal of Chinese Universities) 2024年第1期39卷 114-124页

作者： ZHANG Li-hong NIE Xiao-feng WANG Guo-tao Bashir Ahmad School of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041004China Nonlinear Analysis and Applied Mathematics(NAAM)Research Group Department of MathematicsFaculty of ScienceKing Abdulaziz UniversityJeddah 21589Saudi Arabia.

The paper generalizes the direct method of moving planes to the Logarithmic Laplacian ***,some key ingredients of the method are discussed,for example,Narrow region principle and Decay at ***,the radial symmetry of th... 详细信息

关键词： the Logarithmic Laplacian system radial symmetry the direct method of moving planes

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AsPeer: Method to Self and Peer Assessment in Large Online Design Classes

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第10期18卷 19-38页

作者： Sankaran, Hariesh K. Shanmugam, Raju Shah, Bhavesh Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Karnavati University Gujrat Gandhinagar India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan

Peer and self-assessment open opportunities to scale assessments in online classrooms. This article reports our experiences of using AsPeer-peer assessment system, with two iterations of a university online class. We observed that peer grades highly correlated with staff assigned grades. It was recorded that, the peer grade of all student submissions within range of instructor grade averaged to 21.0% and that within the next 2 ranges was 49.0%. We performed three experiments to improve accuracy of peer grading. First, we observed grading bias and introduced a data driven feedback mechanism to inform peers of it. Students aided by feedback were mindful and performed grading with better accuracy. Second, we observed that the rubric lacked efficiency in translating intent to students. Simplified guiding questions improved accuracy in assessment by 89% students. Third, we encouraged peers to provide personalized qualitative feedback along with rating. We provided them with feedback snippets that addressed common issues. 64% students responded that the snippets helped them to critically look at submissions before rating © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

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