A Hybrid Machine Learning Model for Efficient XML Parsing
The Extensible Markup Language (XML) files are extensively used for representing structured data on the web for file configuration, exchanging data between distinct applications, web development, and many other applications. Consequently, effective parsing techniques are necessary for XML files to e...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10810393/ |
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| author | Muhammad Ali Minhaj Ahmad Khan Raihan Ur Rasool |
| author_facet | Muhammad Ali Minhaj Ahmad Khan Raihan Ur Rasool |
| author_sort | Muhammad Ali |
| collection | DOAJ |
| description | The Extensible Markup Language (XML) files are extensively used for representing structured data on the web for file configuration, exchanging data between distinct applications, web development, and many other applications. Consequently, effective parsing techniques are necessary for XML files to enhance the performance of applications. The existing parsing techniques have their strengths and weaknesses affecting the performance of applications. Researchers point out that the selection of an efficient and appropriate parser is the most challenging issue regarding a particular condition. This paper proposes a framework XML Parsing Optimization using Hybrid Machine Learning (XPOHML) that makes use of Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning techniques for efficient XML parsing. The newly developed framework performs analysis and prediction of different XML parsers using profiling, classification, performance evaluation, and finally generates code for efficient parsing. The XML profiling phase of the XPOHML framework generates a dataset by evaluating the performance of PXTG, SAX, StAX, DOM, and JDOM parsing models on separate cores by applying numerous file sizes. The Classification phase produces the classification model by applying ANN and SVM techniques to identify the appropriate parsing model. The performance evaluation phase of XPOHML assesses the performance of both parsing models through classification metrics (accuracy). Additionally, based on evaluation outcomes, the code generation phase produces an efficient parsing model of XML files. The newly designed and developed XPOHML framework has shown a meaningful improvement in the performance of parsing XML files. |
| format | Article |
| id | doaj-art-9ec1b7457c0042e7a89c61c55a9b973c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9ec1b7457c0042e7a89c61c55a9b973c2025-01-03T00:02:02ZengIEEEIEEE Access2169-35362025-01-011338239310.1109/ACCESS.2024.352070610810393A Hybrid Machine Learning Model for Efficient XML ParsingMuhammad Ali0https://orcid.org/0009-0009-6646-0847Minhaj Ahmad Khan1https://orcid.org/0000-0002-2375-7829Raihan Ur Rasool2https://orcid.org/0000-0001-9966-2466Department of Computer Science, Bahauddin Zakariya University, Multan, PakistanDepartment of Computer Science, Bahauddin Zakariya University, Multan, PakistanIBM Technologies, Melbourne, VIC, AustraliaThe Extensible Markup Language (XML) files are extensively used for representing structured data on the web for file configuration, exchanging data between distinct applications, web development, and many other applications. Consequently, effective parsing techniques are necessary for XML files to enhance the performance of applications. The existing parsing techniques have their strengths and weaknesses affecting the performance of applications. Researchers point out that the selection of an efficient and appropriate parser is the most challenging issue regarding a particular condition. This paper proposes a framework XML Parsing Optimization using Hybrid Machine Learning (XPOHML) that makes use of Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning techniques for efficient XML parsing. The newly developed framework performs analysis and prediction of different XML parsers using profiling, classification, performance evaluation, and finally generates code for efficient parsing. The XML profiling phase of the XPOHML framework generates a dataset by evaluating the performance of PXTG, SAX, StAX, DOM, and JDOM parsing models on separate cores by applying numerous file sizes. The Classification phase produces the classification model by applying ANN and SVM techniques to identify the appropriate parsing model. The performance evaluation phase of XPOHML assesses the performance of both parsing models through classification metrics (accuracy). Additionally, based on evaluation outcomes, the code generation phase produces an efficient parsing model of XML files. The newly designed and developed XPOHML framework has shown a meaningful improvement in the performance of parsing XML files.https://ieeexplore.ieee.org/document/10810393/Artificial intelligenceartificial neural networkeXtensible markup languageframeworkmachine learningmodel |
| spellingShingle | Muhammad Ali Minhaj Ahmad Khan Raihan Ur Rasool A Hybrid Machine Learning Model for Efficient XML Parsing IEEE Access Artificial intelligence artificial neural network eXtensible markup language framework machine learning model |
| title | A Hybrid Machine Learning Model for Efficient XML Parsing |
| title_full | A Hybrid Machine Learning Model for Efficient XML Parsing |
| title_fullStr | A Hybrid Machine Learning Model for Efficient XML Parsing |
| title_full_unstemmed | A Hybrid Machine Learning Model for Efficient XML Parsing |
| title_short | A Hybrid Machine Learning Model for Efficient XML Parsing |
| title_sort | hybrid machine learning model for efficient xml parsing |
| topic | Artificial intelligence artificial neural network eXtensible markup language framework machine learning model |
| url | https://ieeexplore.ieee.org/document/10810393/ |
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