A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms
This study introduces a novel evaluation framework for predicting web page performance, utilizing state-of-the-art machine learning algorithms to enhance the accuracy and efficiency of web quality assessment. We systematically identify and analyze 59 key attributes that influence website performance...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/2/19 |
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| author | Mohammad Ghattas Antonio M. Mora Suhail Odeh |
| author_facet | Mohammad Ghattas Antonio M. Mora Suhail Odeh |
| author_sort | Mohammad Ghattas |
| collection | DOAJ |
| description | This study introduces a novel evaluation framework for predicting web page performance, utilizing state-of-the-art machine learning algorithms to enhance the accuracy and efficiency of web quality assessment. We systematically identify and analyze 59 key attributes that influence website performance, derived from an extensive literature review spanning from 2010 to 2024. By integrating a comprehensive set of performance metrics—encompassing usability, accessibility, content relevance, visual appeal, and technical performance—our framework transcends traditional methods that often rely on limited indicators. Employing various classification algorithms, including Support Vector Machines (SVMs), Logistic Regression, and Random Forest, we compare their effectiveness on both original and feature-selected datasets. Our findings reveal that SVMs achieved the highest predictive accuracy of 89% with feature selection, compared to 87% without feature selection. Similarly, Random Forest models showed a slight improvement, reaching 81% with feature selection versus 80% without. The application of feature selection techniques significantly enhances model performance, demonstrating the importance of focusing on impactful predictors. This research addresses critical gaps in the existing literature by proposing a methodology that utilizes newly extracted features, making it adaptable for evaluating the performance of various website types. The integration of automated tools for evaluation and predictive capabilities allows for proactive identification of potential performance issues, facilitating informed decision-making during the design and development phases. By bridging the gap between predictive modeling and optimization, this study contributes valuable insights to practitioners and researchers alike, establishing new benchmarks for future investigations in web page performance evaluation. |
| format | Article |
| id | doaj-art-e4caeef074e34dcda1b7d76ebef8c6e7 |
| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-e4caeef074e34dcda1b7d76ebef8c6e72025-08-20T02:44:40ZengMDPI AGAI2673-26882025-01-01621910.3390/ai6020019A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization AlgorithmsMohammad Ghattas0Antonio M. Mora1Suhail Odeh2Department of Signal Theory, Telematics and Communications, School of Computer Sciences and Telecommunications (ETSIIT) and Research Center on Information and Communication Technologies (CITIC-UGR), University of Granada, 18071 Granada, SpainDepartment of Signal Theory, Telematics and Communications, School of Computer Sciences and Telecommunications (ETSIIT) and Research Center on Information and Communication Technologies (CITIC-UGR), University of Granada, 18071 Granada, SpainDepartment of Software Engineering, Faculty of Science, Bethlehem University, Bethlehem P1520468, PalestineThis study introduces a novel evaluation framework for predicting web page performance, utilizing state-of-the-art machine learning algorithms to enhance the accuracy and efficiency of web quality assessment. We systematically identify and analyze 59 key attributes that influence website performance, derived from an extensive literature review spanning from 2010 to 2024. By integrating a comprehensive set of performance metrics—encompassing usability, accessibility, content relevance, visual appeal, and technical performance—our framework transcends traditional methods that often rely on limited indicators. Employing various classification algorithms, including Support Vector Machines (SVMs), Logistic Regression, and Random Forest, we compare their effectiveness on both original and feature-selected datasets. Our findings reveal that SVMs achieved the highest predictive accuracy of 89% with feature selection, compared to 87% without feature selection. Similarly, Random Forest models showed a slight improvement, reaching 81% with feature selection versus 80% without. The application of feature selection techniques significantly enhances model performance, demonstrating the importance of focusing on impactful predictors. This research addresses critical gaps in the existing literature by proposing a methodology that utilizes newly extracted features, making it adaptable for evaluating the performance of various website types. The integration of automated tools for evaluation and predictive capabilities allows for proactive identification of potential performance issues, facilitating informed decision-making during the design and development phases. By bridging the gap between predictive modeling and optimization, this study contributes valuable insights to practitioners and researchers alike, establishing new benchmarks for future investigations in web page performance evaluation.https://www.mdpi.com/2673-2688/6/2/19machine learningweb pagestatistic modelpage load timeperformanceprediction models |
| spellingShingle | Mohammad Ghattas Antonio M. Mora Suhail Odeh A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms AI machine learning web page statistic model page load time performance prediction models |
| title | A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms |
| title_full | A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms |
| title_fullStr | A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms |
| title_full_unstemmed | A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms |
| title_short | A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms |
| title_sort | novel approach for evaluating web page performance based on machine learning algorithms and optimization algorithms |
| topic | machine learning web page statistic model page load time performance prediction models |
| url | https://www.mdpi.com/2673-2688/6/2/19 |
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