Abstract
The Internet is an extremely useful resource for education and research. The Internet has been experiencing broken connections issue in spite of its concurrent services. Broken links are common issues stirring in the area of the web. Sometimes the page which was pointing from another page has been disappeared forever or moved to some other location. There can be many reasons for broken links such as the target website is for all time not available, the target website page has been detaching, the target web page was changed or altered and also has misspellings in the link. The broken link itself contains a lot of information such as URL, mark content, encompassing content close to naming content and the content in the page. Every one of these assets of information is valuable for recovering the candidate pages relevance for broken links. The system returns the ranked lists of highly relevant candidate pages on submitting a query which has been extracted from different sources. In this paper, we explore the expression that is used for the proximity (position) connection in the terms of the label and full text in order to extract relative (good and bad) terms through Naïve Bayes classification model. This solves the problem by providing non-identical terms to inquire multiple broken connections and also enrich the accomplishment as the terms that are closely identical show relevancy.
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Acknowledgments
I would like to express my sincerest appreciation to my supervisor Dr. Shariq Bashir for his directions, assistance, and guidance. I sincerely thanked for his vigorous support, inspirational and technical advice in the research area. I am very thankful to him from the core of my heart for the final level, as he enabled me to develop an understanding of the subject. He has taught me, both consciously and unconsciously, how good experimental work is carried He always remained there whenever there was a need related to Experimental work (software support, dataset, and its understanding) and any other help required in step by step execution and completion of the paper.
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Khan, F.N., Ali, A., Hussain, I., Sarwar, N., Rafique, H. (2019). Repairing Broken Links Using Naive Bayes Classifier. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_40
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DOI: https://doi.org/10.1007/978-981-13-6052-7_40
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