Performance Of Search Engines Research Articles

A proposed semantic keywords search engine for Indonesian Qur’an translation based on word embedding

Obtaining relevant information from the Holy Qur’an can be really challenging for people who cannot speak Arabic, such as the Indonesian people. One technology implementation which is commonly used to tackle this problem is to develop a search engine application for Al-Qur’an verses. This paper proposes a search engine based on semantic representation keywords for the Indonesian translation of the Al-Qur’an which consists of 3 phases i.e., data preparation, document representation, and search engine development. In the first stage, the Al-Qur’an dataset was built using the official translation of the Al-Qur’an from the Ministry of Religion and then enriched with the Wikipedia corpus. The second phase is document representation which produces feature vectors by utilizing the Word2Vec algorithm. Finally, the development of a search engine that can find the most relevant verses by calculating the cosine similarity between the document and the keywords. It was found that the proposed search engine succeeded in exceeding the performance of ordinary search engines by finding wider information due to the use of semantic keywords. Apart from that, the proposed search engine succeeded in maintaining the relevance of search results by achieving precision and recall levels of 98.7% and 97.3% respectively.

Comparison of open science portals of India: web accessibility study

PurposeThe purpose of this paper is to compare ten Open Science Portals in India. The comparison is made using the three primary parameters of accessibility, search engine optimization (SEO) and performance. This study aims to investigate whether the ten selected web portals efficiently meet the web accessibility requirements of the large population at an acceptable rate.Design/methodology/approachThis study was performed by using the Siteimprove web tool to collect information about Web Content Accessibility Guidelines (WCAG) 2.0. The online accessibility tools provided by Siteimprove make it simple to comply with and follow the global WCAG at all compliance levels. This study selected ten web portals taking into account the open accessibility and the web portals offered by the Indian Government to promote research and innovation.FindingsThe findings of this research will be beneficial for the government website designers, managers and administrators to assess their website’s level of compliance with accepted accessibility standards so they can make tactical choices to increase accessibility.Originality/valueThis study highlights the portals’ robust characteristic features and downsides, where improvement may be required. It also focused on how these portals help in promoting citizen science and public understanding of science nationwide.

Search engine Performance optimization: methods and techniques.

With the rapid advancement of information technology, search engine optimisation (SEO) has become crucial for enhancing the visibility and relevance of online content. In this context, the use of cloud platforms like Microsoft Azure is being explored to bolster SEO capabilities. This scientific article offers an in-depth study of search engine optimisation. It explores the different methods and techniques used to improve the performance and efficiency of a search engine, focusing on key aspects such as result relevance, search speed and user experience. The article also presents case studies and concrete examples to illustrate the practical application of optimisation techniques. The results demonstrate the importance of optimisation in delivering high quality search results and meeting the increasing demands of users. The article addresses the enhancement of search engines through the Microsoft Azure infrastructure and its associated components. It highlights methods such as indexing, semantic analysis, parallel searches, and caching to strengthen the relevance of results, speed up searches, and optimise the user experience. Following the application of these methods, a marked improvement was observed in these areas, thereby showcasing the capability of Microsoft Azure in enhancing search engines. The study sheds light on the implementation and analysis of these Azure-focused techniques, introduces a methodology for assessing their efficacy, and details the specific benefits of each method. Looking forward, the article suggests integrating artificial intelligence to elevate the relevance of results, venturing into other cloud infrastructures to boost performance, and evaluating these methods in specific scenarios, such as multimedia information search. In summary, with Microsoft Azure, the enhancement of search engines appears promising, with increased relevance and a heightened user experience in a rapidly evolving sector.

A socio-technical approach to trustworthy semantic biomedical content generation and sharing

The rapid growth of online biomedical content has presented a notable challenge in delivering timely and precise semantic annotations. Semantic annotations play a crucial role in contextually indexing data, thereby enhancing search accuracy. This intricate process involves the utilization of multiple coded ontologies, requiring extensive technical expertise and domain knowledge. While automated ontologies face limitations in balancing accuracy and speed, expert knowledge generation is also scarce and expensive. In response to these conflicting challenges, we propose a socio-technical content generation and sharing approach named ‘Semantically,’ which actively involves biomedical experts and scientists. Additionally, ‘Semantically’ leverages schema.org for incorporating additional semantic tags to enhance search engine performance. The outcome is high-quality, machine-understandable content that not only facilitates fast and accurate searches but also instills trust due to the collaborative nature of the annotation process. ‘Semantically’ generated biomedical content was evaluated in two scenarios: (1) search based solely on initial-level annotations and (2) search incorporating additional expert-recommended annotations. ‘Semantically’ enhances the user search experience when compared to benchmark data. In the first scenario, the unigram to 5-grams strategy efficiently recognizes biomedical terms, resulting in high precision, recall, F1, and accuracy scores, all around 0.9. ‘Semantically’ code is openly accessible at https://github.com/bukharilab/Semantically.

Development of Distributed Search Algorithm for Improving Efficiency in Web Search Engines

This research article presents the development of a distributed search algorithm aimed at enhancing the efficiency of web search engines. Through a qualitative methodology involving literature review and library research, the study seeks to address the growing demand for improved search engine performance in handling large-scale data while maintaining fast response times. The literature review explores existing search algorithms and their limitations in processing vast amounts of information distributed across various servers. By analyzing prior research, the article identifies the need for a distributed approach to optimize search engine functionality and reduce latency. The developed distributed search algorithm employs parallel processing techniques to distribute search queries across multiple nodes, thereby improving overall system efficiency and response time. By leveraging distributed computing resources, the algorithm enhances the scalability and reliability of web search engines, enabling them to handle increased user traffic and data volume. The findings underscore the effectiveness of the proposed algorithm in significantly reducing search latency and improving search engine performance, as evidenced by experimental results. Furthermore, the article discusses potential applications of the distributed search algorithm in various domains, including e-commerce, information retrieval, and data analytics.

Search engine Performance optimization: methods and techniques

Background With the rapid advancement of information technology, search engine optimisation (SEO) has become crucial for enhancing the visibility and relevance of online content. In this context, the use of cloud platforms like Microsoft Azure is being explored to bolster SEO capabilities. Methods This scientific article offers an in-depth study of search engine optimisation. It explores the different methods and techniques used to improve the performance and efficiency of a search engine, focusing on key aspects such as result relevance, search speed and user experience. The article also presents case studies and concrete examples to illustrate the practical application of optimisation techniques. Results The results demonstrate the importance of optimisation in delivering high quality search results and meeting the increasing demands of users. Conclusions The article addresses the enhancement of search engines through the Microsoft Azure infrastructure and its associated components. It highlights methods such as indexing, semantic analysis, parallel searches, and caching to strengthen the relevance of results, speed up searches, and optimise the user experience. Following the application of these methods, a marked improvement was observed in these areas, thereby showcasing the capability of Microsoft Azure in enhancing search engines. The study sheds light on the implementation and analysis of these Azure-focused techniques, introduces a methodology for assessing their efficacy, and details the specific benefits of each method. Looking forward, the article suggests integrating artificial intelligence to elevate the relevance of results, venturing into other cloud infrastructures to boost performance, and evaluating these methods in specific scenarios, such as multimedia information search. In summary, with Microsoft Azure, the enhancement of search engines appears promising, with increased relevance and a heightened user experience in a rapidly evolving sector.

Analysis of the Enhanced Boyer-Moore Search Algorithm for a Desktop PC Search Engine

In the digital era, the volume of digital contents stored on personal computers has been rapidly and continuously increasing. Finding documents among this bulk of files has become tedious and stressful. In this regard, string searching and pattern matching algorithms must be employed to alleviate the performance of search engines, making it easier to find the desired documents. This paper aims to analyze the performance of an enhanced Boyer-Moore (BM) search algorithm to assist in finding the intended information or files. A series of tests have been conducted in order to compare the enhanced BM algorithm with the traditional BM algorithm. The results have shown that the Enhanced Boyer-Moore Algorithm performed the search and managed to speed up the searching process by reducing the time duration and the accuracy of the overall performance as the input text increased.

Efficient information retrieval model: overcoming challenges in search engines-an overview

<span>Search engines play a vital role in information retrieval (IR) indexing and processing vast and diverse data, which now encompasses the ever-expanding wealth of multimedia content. However, search engine performance relies on the efficiency and effectiveness of their information retrieval systems (IRS). To enhance search engine performance, there is a need to develop more efficient and accurate IRS that retrieves relevant information quickly and accurately. To address this challenge, various approaches, including inverted indexing, query expansion, and relevance feedback, have been proposed for IR. Although these approaches have shown promising results, but their effectiveness and limitations require a comprehensive examination This research aims to investigate the challenges and opportunities in designing an efficient IRS for search engines and identify key areas for improvement and future research. The study involves a comprehensive literature review on information retrieval impacting academia, industry, healthcare, e-commerce, and other domains. Researchers rely on search engines to access relevant scientific papers, professionals use them to gather market intelligence, and consumers utilize them for product research and decision-making. The findings of this study will contribute to the development of more efficient and effective information retrieval systems, leading to improved search engine performance and user satisfaction.</span>

A Hybrid Unsupervised Density-based Approach with Mutual Information for Text Outlier Detection

The detection of outliers in text documents is a highly challenging task, primarily due to the unstructured nature of documents and the curse of dimensionality. Text document outliers refer to text data that deviates from the text found in other documents belonging to the same category. Mining text document outliers has wide applications in various domains, including spam email identification, digital libraries, medical archives, enhancing the performance of web search engines, and cleaning corpora used in document classification. To address the issue of dimensionality, it is crucial to employ feature selection techniques that reduce the large number of features without compromising their representativeness of the domain. In this paper, we propose a hybrid density-based approach that incorporates mutual information for text document outlier detection. The proposed approach utilizes normalized mutual information to identify the most distinct features that characterize the target domain. Subsequently, we customize the well-known density-based local outlier factor algorithm to suit text document datasets. To evaluate the effectiveness of the proposed approach, we conduct experiments on synthetic and real datasets comprising twelve high-dimensional datasets. The results demonstrate that the proposed approach consistently outperforms conventional methods, achieving an average improvement of 5.73% in terms of the AUC metric. These findings highlight the remarkable enhancements achieved by leveraging normalized mutual information in conjunction with a density-based algorithm, particularly in high-dimensional datasets.

Novelty in News Search: A Longitudinal Study of the 2020 US Elections

The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. Using virtual agents that simulate human web browsing behavior to collect search engine result pages, we conduct a longitudinal study of news results of five search engines collected in short bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries (“joe biden,” “donald trump,” and “us elections”) compared to topical (e.g., “coronavirus”) or stable (e.g., “holocaust”) queries. We demonstrate that our method captures sudden changes in highly covered news topics as well as multiple differences across search engines and regions over time. We highlight novelty imbalances between candidate queries which affect their visibility during electoral periods, and conclude that, when it comes to news, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources that they rely on.

Evaluation of English Language Learning (ELL) Websites using Automated Evaluation Tool

English Language Learning (ELL) websites provide excellent opportunities for language learning. In order to provide interactive, fun learning experiences, many teachers opt to use ELL websites and end up facing problems regarding the websites’ accessibility and usability. Websites’ quality is a key factor for users’ acceptance and satisfaction. The proliferation of a variety of free and unique website testing tools has made it possible to quickly assess the quality of websites. This quantitative study aims at evaluating 5 free ELL websites in terms of Search Engine Optimization (SEO), performance, usability, social aspects and links using an automated evaluation tool i.e., SEOptimer. All in all, none of the 5 websites evaluated scored the overall grade A+ which indicates a critical need to improve the 5 aspects evaluated in order to increase their quality. This study contributes to the improvement of selected free ELL websites. Besides, it substantially advances the online learning trend in Malaysia by accentuating the potential free ELL websites to be explored and utilised by teachers to provide authentic English language learning experiences. Above all, the 5 aspects evaluated by SEOptimer form a comprehensive guide which will be useful for the curriculum developer and Ministry of Education to develop personalised online resources and websites for Malaysian primary students in ensuring quality education for all.

Дистанционный мониторинг событий, вызывающих снижение производительности информационных и информационно-поисковых систем

The object of research is events that randomly or intentionally exploit the built-in locking mechanism of SQL Server, which significantly reduce the performance of information and information search engines. The subject of the research is the remote monitoring of blocking processes. The purpose of the research is to develop a secure remote monitoring method that can quickly and reliably detect events that cause the operation of the built-in locking mechanism of SQL Server. The use of monitoring is necessary to maintain the required level of system performance, as well as to promptly detect impacts on the information system that destroy the integrity of databases. This article is aimed at solving the main problems of remote monitoring: ensuring the completeness of the assessment of the condition and reliable, prompt transmission of the received information through an open channel. The key feature of the proposed remote monitoring method is the identification of SQL Server objects and SQL language constructs used to implement remote monitoring, taking into account the completeness of the information received. A high degree of security of the remote monitoring information environment is provided by the system means of implementing the author's client-server architecture. It is shown that the efficiency and security of the transfer of valuable control data is ensured by the use of stored procedures. Known stored procedures are redundant, so the authors needed to create their own monitoring tool, which is the main result of this article. The results of the research can be applied in the design of reliable and secure information and information search systems for various purposes.

A Novel Architecture for Search Engine using Domain Based Web Log Data

Search engines, an information retrieval tool are the main source of information for users’ information need now a day. For every query, the search engine explores its repository and/or indexer to find the relevant documents/URLs for that query. Page ranking algorithms rank the Uniform Resource Locator in abstract section (URLs) according to its relevancy with respect to users’ query. It is analyzed that many of the queries fired by users on search engines are duplicate. There is a scope to improve the performance of search engine to reduce its efforts for duplicate queries. In this paper a proxy server is created that keep store the search results of user queries in web log. The proposed proxy server uses this web log to find results faster for duplicate queries fired next time. The proposed scheme has been tested and found prominent. The proposed architecture tested for ten duplicate user queries. it return all relevant web pages for duplicate user query (if query is found in web log at proxy server) from a particular domain instead of entire database. It reduces the perceived latency for duplicate query and also improves the value of precession and accuracy up to 81.8% and 99% respectively for all duplicate user queries.

Insights into Search Engine Optimization using Natural Language Processing and Machine Learning

Among the potential tools in digital marketing, Search Engine Optimization (SEO) facilitates the use of appropriate data by providing appropriate results according to the search priority of the user. Various research-based approaches have been developed to improve the optimization performance of search engines over the past decade; however, it is still unclear what the strengths and weaknesses of these methods are. As a result of the increased proliferation of Machine Learning (ML) and Natural Language Processing (NLP) in complex content management, there is potential to achieve successful SEO results. Therefore, the purpose of this paper is to contribute towards performing an exhaustive study on the respective NLP and ML methodologies to explore their strengths and weaknesses. Additionally, the paper highlights distinct learning outcomes and a specific research gap intended to assist future research work with a guideline necessary for optimizing search engine performance.

Proposing a New Combined Indicator for Measuring Search Engine Performance and Evaluating Google, Yahoo, DuckDuckGo, and Bing Search Engines based on Combined Indicator

This study has developed a combined indicator to evaluate the performance of different search engines. Documentary analysis, survey, and evaluative methods are employed in the present study. The research was conducted in two stages. First, a combined indicator was designed to measure search engines. To this end, 72 criteria for measuring the performance of search engines were identified, out of which 22 criteria were selected. Accordingly, 10 criteria were selected in six general classes through a survey of subject matter experts. Validation of our proposed combined indicator was obtained by Delphi method and using the opinions of experts in the fields of information science and information system. Second, web search engines were evaluated based on the proposed combined indicator. The statistical population of this part of the research consisted of two categories: (1) general web search engines, and (2) general subjects. The sample size of the first category contained four search engines Yahoo, Google, DuckDuckGo, and Bing, and the second category involved 40 search terms under 10 general categories. The results showed that the combined indicator had six general criteria: (1) relevance, (2) ranking, (3) novelty ratio, (4) coverage ratio, (5) ratio of unrelated documents, and (6) proportion of duplication hits. According to this indicator, Google is at the top, followed by Bing. This study proposes a new indicator for evaluating search engine performance, which can measure the efficiency of search engines. Therefore, its use to measure the performance of search engines is recommended to researchers and search engine developers.

Future of Artificial Intelligence in Libraries

ABSTRACT Commercial and research applications of artificial Intelligence (AI) are becoming increasingly important. AI is used to improve products, predict customer behavior, keep track of inventory, and analyze Big Data. AI agents are also employed to improve the performance of search engines and smartphones. There are a number of ways that AI is being considered for applications in the library, especially data analysis, supporting remote access to library services, and making the library a center for research using Big Data. AI has the potential to perform routine tasks that now require a human being, which will free up librarians to offer the in-depth expertise that is essential for advanced research.

Impact factions: assessing the citation impact of different types of open access repositories

Institutional repositories (IR) maintained by research libraries play a central role in providing open access to taxpayer-funded research products. It is difficult to measure the extent to which IR contribute to new scholarship because publisher self-archiving policies typically require researchers to cite the “version of record” of a manuscript even when an IR copy is accessed to conduct the research. While some studies report an open access (OA) citation advantage resulting from the availability of self-archived or “green” OA manuscripts, few have sought to measure an OA citation effect of IR separately from disciplinary repositories, including arXiv and PubMed Central. In this study, the authors present a bibliometric analysis examining correlations between search engine performance of items in IR, OA availability from different types of repositories, and citations. The analysis uses a novel, open dataset of IR access and usage derived from five months of Google search engine results pages (SERP) data, which were aggregated by the Repository Analytics and Metrics Portal (RAMP) web service. Findings indicate that making OA copies of manuscripts available in self-archiving or “green” repositories results in a positive citation effect, although the disciplinary repositories within the sample significantly outperform the other types of OA services analyzed. Also evident is an increase in citations when a single manuscript is available in multiple OA sources.

Performance of search engines in harvesting grey literature

PurposeThe purpose of the study is to examine how far search engines have succeeded in retrieving electronic theses and dissertations (ETDs) using different search techniques and approaches. This study also compared the search results of search engines to identify the most suitable search engine for searching and retrieving the ETDs.Design/methodology/approachFifty ETDs have been selected using systematic random sampling techniques from Shodhganga (a reservoir of Indian theses). These ETDs have been searched through prominent search engines such as Google, Google Scholar, Yahoo and Open Access Theses and Dissertation (OATD) using different search approaches and techniques.FindingsThe overall performance of the search engines revealed that Google retrieved most of the ETDs (89%) followed by OATD (88.5%), Yahoo (78%) and Google Scholar (76%), respectively.Originality/valueThe results have brought into light interesting findings regarding search engines in retrieving grey literature.

Performance of Search Engines and Specialized Medical Databases in the Field of Cancer

Performance of Search Engines and Specialized Medical Databases in the Field of Cancer

Web Pages Ranking Algorithms: A Survey

Due to the daily expansion of the web, the amount of information has increased significantly. Thus, the need for retrieving relevant information has also increased. In order to explore the internet, users depend on various search engines. Search engines face a significant challenge in returning the most relevant results for a user's query. The search engine's performance is determined by the algorithm used to rank web pages, which prioritizes the pages with the most relevancy to appear at the top of the result page. In this paper, various web page ranking algorithms such as Page Rank, Time Rank, EigenRumor, Distance Rank, SimRank, etc. are analyzed and compared based on some parameters, including the mining technique to which the algorithm belongs (for instance, Web Content Mining, Web Structure Mining, and Web Usage Mining), the methodology used for ranking web pages, time complexity (amount of time to run an algorithm), input parameters (parameters utilized in the ranking process such as InLink, OutLink, Tag name, Keyword, etc.), and the result relevancy to the user query.