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02 June 2023, Volume 38 Issue 3
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Abstract
A cutting-edge method for Quos metrification utilizing Hidden Markov Models(HMM),PHISHING DETECTION
IN SOCIAL MEDIA also provides the best method for executing user requests. The approach we demonstrate may be used to quantify and anticipate the response time of Phishing Web Services, allowing us rather than subjectively, to statistically rank services. By conducting experiments on actual data, we demonstrate the viability and usefulness of our approach.
The findings demonstrated how our suggested technique may assist the process by which the user chooses the most
Reliable Phishing Web Service depends on a variety of factors, such as response time variability and system predictability. When it comes to Internet services, having low- performance servers, having a lot of latency, or just having a generally bad experience can result in missed revenue, dissatisfied users, and losing customers. The testing outcomes show user click- through data from a paid search engine, which supports the efficacy of our recommended technique. Third, user search goal distributions can be helpful in applications like reranking online search results that take into account a variety of user search aims.
Keyword
Inclination prediction, categorization, machine learning, deep learning, Stock market.
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