scholarly journals SEO ANALYSIS FOR WEBSITES

2016 ◽  
Vol 2 (5) ◽  
Author(s):  
PANKAJ ,

This survey paper is for net search engines to induce a decent organic rank over the net. This analysis is about  SEO analysis for websites wherever search engines use keyword-element relationship outline that succinctly reflect interaction between keywords and also the information components mentioning them. A construction marking mechanism is planned for computing the relevancy of routing plans supported scores at the amount of keywords, information, components, component sets, and sub graphs that connect these components., This process of the keywords from the user question helps to induce the URLs with all relevant sources of net information. Further, results of questions and analysis for the list of program result page (SERP) provides similar practicality as program improvement methodology.

Author(s):  
Antonius Antonius ◽  
Bernard Renaldy Suteja

Current development of the internet world has been growing rapidly, especially in the field of website. People use search engines to find the news or information they needed on a website. One of the many indications of the success of a website is traffic. Traffic could be received from various factors, one of which is website rank in Search Engine Result Page (SERP). To improve the SERP, SEO methods are required. This research will implement SEO to website especially on the image, and then analyzed by using a tester tools, for example SEOptimer, Pingdom Tools, and SEO Site Checkup. After the website has been optimized, tested with the same tester tools. From the research results can be seen whether image optimization can affect SERP.


Vertical search engines are meant for answering a user's web query within a specific domain such as news, media, and academic web searching. One main difference between vertical and horizontal web searching is that in vertical web searching, unlike horizontal web searching, a subset of entire web is engaged. The chapter investigates the state-of-the-art in academic web searching and points out shortcomings in this particular domain. Lastly, the authors aimed to propose a summary-based recommender to respond to a user's query by retrieving and ranking them according to their similarity merits on the basis of papers' summaries. Results of the evaluations revealed the fact that the proposed framework has outperformed the state-of-the-art in different metrics such as unanimous ranks and F1 measures.


2006 ◽  
Vol 1 (3) ◽  
pp. 67
Author(s):  
David Hook

A review of: Jansen, Bernard J., and Amanda Spink. “How Are We Searching the World Wide Web? A Comparison of Nine Search Engine Transaction Logs.” Information Processing & Management 42.1 (2006): 248-263. Objective – To examine the interactions between users and search engines, and how they have changed over time. Design – Comparative analysis of search engine transaction logs. Setting – Nine major analyses of search engine transaction logs. Subjects – Nine web search engine studies (4 European, 5 American) over a seven-year period, covering the search engines Excite, Fireball, AltaVista, BWIE and AllTheWeb. Methods – The results from individual studies are compared by year of study for percentages of single query sessions, one-term queries, operator (and, or, not, etc.) usage and single result page viewing. As well, the authors group the search queries into eleven different topical categories and compare how the breakdown has changed over time. Main Results – Based on the percentage of single query sessions, it does not appear that the complexity of interactions has changed significantly for either the U.S.-based or the European-based search engines. As well, there was little change observed in the percentage of one-term queries over the years of study for either the U.S.-based or the European-based search engines. Few users (generally less than 20%) use Boolean or other operators in their queries, and these percentages have remained relatively stable. One area of noticeable change is in the percentage of users viewing only one results page, which has increased over the years of study. Based on the studies of the U.S.-based search engines, the topical categories of ‘People, Place or Things’ and ‘Commerce, Travel, Employment or Economy’ are becoming more popular, while the categories of ‘Sex and Pornography’ and ‘Entertainment or Recreation’ are declining. Conclusions – The percentage of users viewing only one results page increased during the years of the study, while the percentages of single query sessions, one-term sessions and operator usage remained stable. The increase in single result page viewing implies that users are tending to view fewer results per web query. There was also a significant difference in the percentage of queries using Boolean operators between the US-based and the European-based search engines. One of the study’s findings was that results from a study of a particular search engine cannot necessarily be applied to all search engines. Finally, web search topics show a trend towards information or commerce searching rather than entertainment.


Author(s):  
Rimpal Unadkat

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. However, to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper the authors present survey on the role of search engines in intelligent web, Background, Challenges and some issues.


Data ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Will Serrano

As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.


Author(s):  
Rimpal Unadkat

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. However, to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper the authors present survey on the role of search engines in intelligent web, Background, Challenges and some issues.


By the time web engines were developed, the number of queries prompted by users had grown exponentially. This fast growth shows the high demand of users from web search engines. This high demand made search engines responsible for the users' satisfaction during a search session. One way to improve a user's satisfaction is to visualize search engine result page (SERP). Recent studies for meeting this aim focused on a whole page thumbnail for assisting users to remember recently visited web pages. This chapter explores how a specific visual content of a page can allow users to distinguish between a useful and worthless page within results in SERP especially in an ambiguous search task.


Web Services ◽  
2019 ◽  
pp. 2138-2143
Author(s):  
Rimpal Unadkat

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. However, to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper the authors present survey on the role of search engines in intelligent web, Background, Challenges and some issues.


Crisis ◽  
2015 ◽  
Vol 36 (2) ◽  
pp. 102-109 ◽  
Author(s):  
Annette Shtivelband ◽  
Patricia A. Aloise-Young ◽  
Peter Y. Chen

Background: Gatekeeper training is a promising suicide prevention strategy that is growing in popularity. Although gatekeeper training programs have been found to improve trainee knowledge, self-efficacy, and perceived skills, researchers have found that the benefit of gatekeeper training may not last over time. Aims: The purpose of this study was to identify strategies for strengthening the long-term effects of suicide prevention gatekeeper training. Method: In-depth interviews and focus groups were conducted with gatekeepers (N = 44) and data were analyzed using a qualitative research approach. Results: The results of this study suggest that posttraining interventions may be more effective if they include the following seven themes: (a) social network – connecting with other gatekeepers; (b) continued learning – further education; (c) community outreach – building awareness; (d) accessibility – convenience; (e) reminders – ongoing communication; (f) program improvement –- enhancing previous training; and (g) certification – accreditation. Conclusion: Posttraining interventions that incorporate the themes from this study offer a promising direction in which to sustain the effects of gatekeeper suicide prevention training.


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