Improve Search Engine Research Articles

#Headlesspreggos: challenging visual imaginaries of pregnancy and reproduction

Amid new abortion restrictions in the USA, scientific advances in genetic technologies and investigations of COVID-19 vaccinations in pregnancy, news stories about reproduction abound, often accompanied by images of what...

Enhancing Website Visibility: Implementing SEO Techniques for Improved Search Engine Ranking

Abstract: The Internet is a very important tool in today's technological age for a variety of purposes, especially when it comes to business promotion. Building a website that offers its users useful content is a crucial step in any online business promotion campaign. But simply having a website is not enough to increase traffic; you also need to ensure that it appears high on search engine results pages (SERPs). A very successful way to accomplish this is by using search engine optimization (SEO) strategies. These approaches use strategies including content refinement, link building, and keyword research to increase a website's exposure on search engine results pages. However, it's critical to recognize that SEO is a continuous process rather than a onetime event. Maintaining good search engine rankings and sustaining the profitability of your online business requires consistent attention and work. By optimizing your website carefully, you may increase its visibility in search results, draw in more visitors, and eventually promote business growth. Thus, it's critical to stick to your SEO tactics to maintain good search engine results and guarantee ongoing success for your online business.

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.

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>

SEO-OPTIMIZED WRITING: REMOVING THE MYSTERY

In the digital age, SEO optimization plays a crucial role in enhancing the visibility and reach of online content. This paper, authored by Jerry Doby, Ph.D., Editor-in-Chief of The Hype Magazine, aims to demystify SEO-optimized writing for professionals such as journalists, bloggers, copywriters, and other enthusiasts. The study delves into the step-by-step process of optimizing content to improve search engine placement and attract a larger audience. The paper begins by discussing the significance of an SEO-optimized headline and its impact on search engine placement. It emphasizes the importance of researching relevant keywords and phrases and incorporating them into the headline while maintaining a clear and concise message. Additionally, the paper highlights the use of numbers, questions, and emotional triggers to increase click-through rates and engagement, further enhancing search engine placement.

Improving zero-shot retrieval using dense external expansion

Pseudo-relevance feedback (PRF) is a classical technique to improve search engine retrieval effectiveness, by closing the vocabulary gap between users’ query formulations and the relevant documents. While PRF is typically applied on the same target corpus as the final retrieval, in the past, external expansion techniques have sometimes been applied to obtain a high-quality pseudo-relevant feedback set using the external corpus. However, such external expansion approaches have only been studied for sparse (BoW) retrieval methods, and its effectiveness for recent dense retrieval methods remains under-investigated. Indeed, dense retrieval approaches such as ANCE and ColBERT, which conduct similarity search based on encoded contextualised query and document embeddings, are of increasing importance. Moreover, pseudo-relevance feedback mechanisms have been proposed to further enhance dense retrieval effectiveness. In particular, in this work, we examine the application of dense external expansion to improve zero-shot retrieval effectiveness, i.e. evaluation on corpora without further training. Zero-shot retrieval experiments with six datasets, including two TREC datasets and four BEIR datasets, when applying the MSMARCO passage collection as external corpus, indicate that obtaining external feedback documents using ColBERT can significantly improve NDCG@10 for the sparse retrieval (by upto 28%) and the dense retrieval (by upto 12%). In addition, using ANCE on the external corpus brings upto 30% NDCG@10 improvements for the sparse retrieval and upto 29% for the dense retrieval.

Introduction to Hybrid algorithms

Algorithms are the building blocks of the systems industry created by human civilization. From automation to a wide range of simple tasks to predicting values from existing databases visually patterns, algorithms are found almost everywhere. We have successfully created solutions to some of the most complex problems with using algorithms. However, there is still a category of problems that can be solved by the user information. Hybrid algorithms deal with certain types of problems. Hybrid algorithms play an important role in improving search engine algorithms. Consolidation aims to combine the benefits of each algorithm to build a hybrid algorithm, while at the same time trying minimize any major damage. In general, the effect of hybridization can often do something improvement depending on the speed of calculation or accuracy. This chapter considers the concepts, types, and motives behind the existence of a mixture algorithms. Types of integrated algorithms, description of each type, different categories as well segmentation algorithms are the main topics of discussion in this chapter.

SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable city

In today’s era, Sustainable city is evaluated using the services provided to the society. The designing of the integral part of the society should be focused towards the benefits of people. Internet is extensively utilized by the society using Search Engines. The accuracy and time it takes for different search engines to retrieve information from a cloud computing repository around the world, varies. However, it has been discovered in the literature that webpage ranking reduces the amount of time a user spends surfing, which saves a significant amount of energy during computation and transmission across the network. The hyperlink structure of the web graph is used in most of the earlier solutions documented in the literature, which consumes a lot of energy during calculation. It may exacerbate the link leakage problem by increasing the frequency of spam pages. In light of the energy consumption of various smart gadgets, hyperlink structure alone is no longer sufficient for predicting webpage relevance. Its true importance is revealed by user surfing activity. To improve search engine accuracy and speed, it is critical to demote spam pages, lowering energy consumption. Among all the existing ranking algorithms in the literature, one of the important components is the PageRank algorithm used by Google’s ranking module. Keeping focus on these points, in this paper, various page ranking algorithms based upon supervised learning are surveyed and summarized with respect to different selected parameters and experiments performed. Using this information, a detailed taxonomy of search engine results is presented in the text. Moreover, PageRank algorithms are explored by using different supervised learning techniques applied in the existing proposals for getting and processing the results. In the nutshell, the PageRank methodology is surveyed with respect to web spam detection which is the demand of cognitive systems in smart cities.

User Search Goals with Feedback Sessions Using Map-Reduce Algorithm

In the existing search results the search engine users search there results for different goals. It will be very useful for improving search engine relevance and user experience. In existing system there are so many Data mining algorithm used for searching relevant information. Rank algorithm, correlation algorithm has been used but that was not effective. In this paper introduces the map reduce algorithm for improving the search results proposed system, the data mining domain introduce the map reduce algorithm. It is effective for cross domain environment and also can use Big data for future work. The search results achieved through using feedback sessions and map reduce technique. This results of the cross domain have been improved using this technique. The experimented results show the efficiency.

Blog Link Classification

Blog links raise three key questions: Why did the author make the link, what exactly is he pointing at, and what does he feel about it? In response to these questions we introduce a link model with three fundamental descriptive dimensions where each dimension is designed to answer one question. We believe the answers to these questions can be utilized to improve search engine results for blogs. While proving this is outside the scope of this paper, we do prove that knowing the rhetorical role of a link helps determine what the author was pointing at and how he feels about it.

Improving search engine efficiency through contextual factor selection

Learning to rank (LTR) is an important artificial intelligence (AI) approach supporting the operation of many search engines. In large-scale search systems, the ranking results are continually improved with the introduction of more factors to be considered by LTR. However, the more factors being considered, the more computation resources required, which in turn, results in increased system response latency. Therefore, removing redundant factors can significantly improve search engine efficiency. In this paper, we report on our experience incorporating our Contextual Factor Selection (CFS) deep reinforcement learning approach into the Taobao e-commerce platform to optimize the selection of factors based on the context of each search query to simultaneously maintaining search result quality while significantly reducing latency. Online deployment on Taobao.com demonstrated that CFS is able to reduce average search latency under everyday use scenarios by more than 40% compared to the previous approach with comparable search result quality. Under peak usage during the Single’s Day Shopping Festival (November 11th) in 2017, CFS reduced the average search latency by 20% compared to the previous approach.

Search interaction based on verbalization of thinking in information retrieval

A team based in the Faculty of Library, Information and Media Science at the University of Tsukuba is investigating a means of improving search engines by teaching them to listen to people's natural language. Their work will enable the development of fluid interactions with search engines of the future.

Research on the Vocabulary Relevancy Algorithm of the Improved Search Engine

Aiming at the limitation of the existing search engine search algorithm and the low reliability of the derived vocabulary relevancy algorithm, a vocabulary relevancy algorithm based on search engine and vocabulary relevancy algorithm is proposed under the background of making full use of the knowledge base of Hownet. Firstly, the main defects of search engine algorithm are considered in two aspects, meaningless text and redundancy. Then, the two problems are solved by using “Hownet” and DSC algorithm. Finally, the search engine-based vocabulary relevancy algorithm is improved by integrating multiple factors such as weight reduction and noise reduction. Experimental results show that compared with the pure improvement method based on the search engine, the method of spearman coefficient and Pearson coefficient were improved, as well as reduce meaningless language segment on the calculation of correlation, verified the “Hownet”, and DSC algorithm into the search engine algorithm can effectively improve the computing performance of relevant vocabulary.

Do better search engines really equate to better clinical decisions? If not, why not?

AbstractPrevious research has found that improved search engine effectiveness—evaluated using a batch‐style approach—does not always translate to significant improvements in user task performance; however, these prior studies focused on simple recall and precision‐based search tasks. We investigated the same relationship, but for realistic, complex search tasks required in clinical decision making. One hundred and nine clinicians and final year medical students answered 16 clinical questions. Although the search engine did improve answer accuracy by 20 percentage points, there was no significant difference when participants used a more effective, state‐of‐the‐art search engine. We also found that the search engine effectiveness difference, identified in the lab, was diminished by around 70% when the search engines were used with real users. Despite the aid of the search engine, half of the clinical questions were answered incorrectly. We further identified the relative contribution of search engine effectiveness to the overall end task success. We found that the ability to interpret documents correctly was a much more important factor impacting task success. If these findings are representative, information retrieval research may need to reorient its emphasis towards helping users to better understand information, rather than just finding it for them.

Writing an Effective Title

Writing an Effective Title

Selective personalization and group profiles for improved web search personalization

Personalization is a common technique used in Web search engines to improve the effectiveness of retrieval. While personalizing some queries yields significant improvements in user experience by providing a ranking in line with the user preferences, it fails to improve or even degrades the effectiveness for less ambiguous queries. A potential personalization metric could improve search engines by selectively applying personalization. One such measure, click entropy uses the query history and the clicked documents for the query, which might be sparse for some queries. In this article, the topic entropy measure is improved by integrating the user distribution into the metric, robust to the sparsity problem. Furthermore, a topic model-based ranking for the personalization method is proposed using grouped user profiles. Experiments reveal that the proposed potential prediction method correlates with human query ambiguity judgments and the group profile-based ranking method improves the mean reciprocal rank by 8%.

Thermal Cracking Furnace Optimal Modeling Based on Enriched Kumar Model by Free-Radical Reactions

The Kumar model as a molecular model has achieved successful application. However, only 22 reactions limit its veracity and adaptability for feedstocks. A series of models with different degrees of integration of the free radical model and the molecular model has been proposed to enhance feedstock adaptability and simulation accuracy. An improved search engine algorithm, namely Improved PageRank (IPR), is provided and applied to calculate the importance of substances in Kumar model to screen the free-radical reaction network for efficient model selection. A methodology of optimal structure and model parameters chosen is applied to the target to improve the adaptability of the material and the accuracy of the model. Then, two cases with different feedstocks are demonstrated with industrial data to verify the correctness of the proposed approach and its wide feedstock adaptability. The proposed model demonstrates good performance: (1) The mean relative errors (MRE) of the K-R (Kumar and free-radical) model have reached an order of magnitude less than 0.1% compared with 5% in the Kumar model. Further, (2) the K-R model can be implemented to model some feedstocks which Kumar model can’t simulate successfully. The K-R model can be applied in simulation of extensive feedstocks with high accuracy.

An Approach to Find Synonym and Bigram to Improve Query Recommendation Efficiency of Exploratory Search Using Search Goal Shift Graphs

Exploratory search aids in the production of desired results which improve browsing abilities of user in current era. To improve search engine performance, this method inspects search goal shift to obtain pertinent knowledge about query entered by the user. The existing system fails to provide bigram relationship and ignores synonyms of the submitted query. Also there is un-certainty of spam links which may be included in the final result. To overcome these lacunas, the proposed framework will conduct an exploratory search to recommend query by making use of bi-gram approach which will also remove the spam links with the help of trust rank algorithm and finds all possible synonyms of the submitted query in order to obtain a proper result. This phenomenon helps to produce better user recommendations.

Improving Search Engines by Demoting Non-Relevant Documents

A good search engine aims to have more relevant documents on the top of the list. This paper describes a new technique called ???Improving search engines by demoting non-relevant documents??? (DNR) that improves the precision by detecting and demoting non-relevant documents. DNR generates a new set of queries that are composed of the terms of the original query combined in different ways. The documents retrieved from those new queries are evaluated using a heuristic algorithm to detect the non-relevant ones. These non-relevant documents are moved down the list which will consequently improve the precision. The new technique is tested on WT2g test collection. The testing of the new technique is done using variant retrieval models, which are the vector model based on the TFIDF weighing measure, the probabilistic models based on the BM25, and DFR-BM25 weighing measures. The recall and precision ratios are used to compare the performance of the new technique against the performance of the original query.

Community detection and influential node identification in complex networks using mathematical programming

Integer programming models for community detection in relational networks have diverse applications in different fields. From making our lives easier by improving search engine optimization to saving our lives by aiding in threat detection and disaster management, researches in this niche have added value to human experience and knowledge. Besides the community structure, the influential nodes or members in a complex network are highly effective at diffusing information quickly to others in the community. Prior research dealing with the use of optimization models for clustering networks has independently focused on detecting communities. In this research, we propose a new integer linear programming model to detect community structure in real-life networks and also identify the most influential node within each community. We validate the proposed model by testing it on a well-established community network. Further, the performance of the proposed model is evaluated by comparing it with the existing best performing optimization model as well as three heuristic approaches for community detection. The experimental results indicate that in most cases the proposed integer programming model performs better than the existing optimization model with respect to modularity, Silhouette coefficient and computational time. Besides, our model yields superior Silhouette and competitive modularity values compared to the heuristic approaches in many cases.