Efficient Deep Learning for Reforming Authentic Content Searching on Big Data

Abstract

With the advancement of search engines, a major change has occurred in the way people are accessing data on the net. Search engines have made access to data efficient and easier as billions of pages on the net (or called big data) are suggested at once. The pages with the most significant rank generally have a higher visibility rate to people and hence every webmaster wants to push their page to higher rank. As a result, Search Engine Optimization (SEO) has become a massive business which strives in enhancing the ranking of clients' webpage. But there are many myths and misconceptions about the ranking algorithms due to inadequate knowledge about SEO's methods. Still there is a need to propose a verified algorithm that is efficient in retrieving pages from Google. The link analysis algorithm is in accordance with the link structure of any document as the page which has many links also has many connections to it and hence can increase retrieval capacity. There is another approach called the integrated ranking approach which comes under personalized web research. In the integrated ranking approach, both content and link are used as parameters to improve retrieval efficiency. This approach is used by

Comparison and analysis of the machine learning in the movie subtitle document classification

“…Internal quantity measurement and external quality measurement are two respectively two methods evaluating cluster quality in unsupervised machine learning [6]. On the one hand, the measurement of external quality requires purity as a quality standard based on labelled movie title documents, meanwhile, F-measure and entropy are two regularly quantifications of external quality in data mining [7].…”

Enabling Health Information Recommendation Using Crowdsourced Refinement in Web-Based Health Information Applications: User-Centered Design Approach and EndoZone Informatics Case Study

“…Members of the general public predominantly resort to search engines such as Google or social media platforms such as Facebook, YouTube, and TikTok as their initial source of health information [ 1 - 7 ]. These platforms use intricate recommendation algorithms to curate the information made accessible to users [ 8 ]. The algorithms are designed to rank information based on certain criteria, presenting it in the order of the ranking score.…”

“…From billions of web pages and videos on the internet, commercial recommendation algorithms of search engines and social media platforms show those with the highest rank first, where the ranking criteria often have nothing to do with whether the content could meet people’s medical needs [ 16 ]. To obtain a higher rank, which leads to a higher visibility rate and eventually a better commercial outcome, billions of dollars have been invested by companies for search engine optimization [ 8 ]. This compounds the situation because trusted health information sources such as research organizations and noncommercial health organizations often do not have the financial capacity to compete with commercial companies.…”