TY - JOUR
T1 - Fuzzy Deep Neural Learning Based on Goodman and Kruskal's Gamma for Search Engine Optimization
AU - Jayaraman, Sethuraman
AU - Ramachandran, Manikandan
AU - Patan, Rizwan
AU - Daneshmand, Mahmoud
AU - Gandomi, Amir H.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - Search engine optimization (SEO) is a significant problem for enhancing a website's visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal's Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.
AB - Search engine optimization (SEO) is a significant problem for enhancing a website's visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal's Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.
KW - Content quality
KW - Deep structured learning
KW - Filtering
KW - Keyword density
KW - Publicity
KW - Ranking
KW - Search engine
KW - Site popularity
KW - Web pages
UR - http://www.scopus.com/inward/record.url?scp=85089766719&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089766719&partnerID=8YFLogxK
U2 - 10.1109/TBDATA.2020.2963982
DO - 10.1109/TBDATA.2020.2963982
M3 - Article
AN - SCOPUS:85089766719
VL - 8
SP - 268
EP - 277
JO - IEEE Transactions on Big Data
JF - IEEE Transactions on Big Data
IS - 1
ER -