CoMSeC: A Comparative Analysis of Various Service Classification Techniques
The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly impacted Web Service Classification, a critical task for service discovery, composition, and selection in various applications. Effective service classification improves security, scalability, cost-effe...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807280/ |
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| author | Malabika Das Ansh Sarkar Sujata Swain |
| author_facet | Malabika Das Ansh Sarkar Sujata Swain |
| author_sort | Malabika Das |
| collection | DOAJ |
| description | The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly impacted Web Service Classification, a critical task for service discovery, composition, and selection in various applications. Effective service classification improves security, scalability, cost-effectiveness, and other crucial parameters for service selection in specific applications. This paper aims to formulate a novel classification model and conduct a comparative study to analyze the performance of base models with various classical and modern clustering algorithms. We propose a unique approach based on natural language processing (NLP) combining Word2Vec and BERT models to generate high-dimensional embeddings from the service dataset. These embeddings are further processed using Dense Layers for dimensionality reduction and then used as inputs to different clustering algorithms. The results demonstrate that our approach significantly improves the accuracy and efficiency of classification, providing a comprehensive overview of performance across various combinations and highlighting the advantages of our method. |
| format | Article |
| id | doaj-art-67cfcfbfee0b43d2a1d8c44bdb39386a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-67cfcfbfee0b43d2a1d8c44bdb39386a2025-08-20T02:43:49ZengIEEEIEEE Access2169-35362024-01-011219533219534310.1109/ACCESS.2024.352034110807280CoMSeC: A Comparative Analysis of Various Service Classification TechniquesMalabika Das0https://orcid.org/0009-0001-7861-5764Ansh Sarkar1https://orcid.org/0009-0004-2819-3905Sujata Swain2https://orcid.org/0000-0001-7089-1863School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, IndiaSchool of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, IndiaSchool of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, IndiaThe rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly impacted Web Service Classification, a critical task for service discovery, composition, and selection in various applications. Effective service classification improves security, scalability, cost-effectiveness, and other crucial parameters for service selection in specific applications. This paper aims to formulate a novel classification model and conduct a comparative study to analyze the performance of base models with various classical and modern clustering algorithms. We propose a unique approach based on natural language processing (NLP) combining Word2Vec and BERT models to generate high-dimensional embeddings from the service dataset. These embeddings are further processed using Dense Layers for dimensionality reduction and then used as inputs to different clustering algorithms. The results demonstrate that our approach significantly improves the accuracy and efficiency of classification, providing a comprehensive overview of performance across various combinations and highlighting the advantages of our method.https://ieeexplore.ieee.org/document/10807280/Serviceclassificationnatural language processingWord2VecBERTtransformers |
| spellingShingle | Malabika Das Ansh Sarkar Sujata Swain CoMSeC: A Comparative Analysis of Various Service Classification Techniques IEEE Access Service classification natural language processing Word2Vec BERT transformers |
| title | CoMSeC: A Comparative Analysis of Various Service Classification Techniques |
| title_full | CoMSeC: A Comparative Analysis of Various Service Classification Techniques |
| title_fullStr | CoMSeC: A Comparative Analysis of Various Service Classification Techniques |
| title_full_unstemmed | CoMSeC: A Comparative Analysis of Various Service Classification Techniques |
| title_short | CoMSeC: A Comparative Analysis of Various Service Classification Techniques |
| title_sort | comsec a comparative analysis of various service classification techniques |
| topic | Service classification natural language processing Word2Vec BERT transformers |
| url | https://ieeexplore.ieee.org/document/10807280/ |
| work_keys_str_mv | AT malabikadas comsecacomparativeanalysisofvariousserviceclassificationtechniques AT anshsarkar comsecacomparativeanalysisofvariousserviceclassificationtechniques AT sujataswain comsecacomparativeanalysisofvariousserviceclassificationtechniques |