Relevant Web Pages Research Articles

An Enhanced PageRank Algorithm based on Optimized Normalized Technique and Content-based Approach

PageRank is an algorithm concerning search queries over the Internet. The algorithm returns the best search results to the user based on the webpage relevancy by calculating the outgoing links from each webpage. Although useful, the algorithm consumes a considerable amount of time as it needs to calculate the available webpages, which are also increasing in number over time. Moreover, the returned results by the algorithm are biased towards old webpages because they have the volume due to their lifetime, thus resulting in newly created webpages to have lower page ranks even though they have comparatively more relevant and useful information. To overcome these issues, this paper proposes an alternative hybrid PageRank algorithm based on optimized normalization technique and content-based approach. The proposed algorithm reduces the number of iterations required to calculate the page rank, hence improves the efficiency, by calculating the mean of all page rank values and normalizes them through the use of the mean. Through this approach, the algorithm is also able to determine the relevancy of webpages based on validity of links rather than popularity. These claims are demonstrated by an experiment conducted on the proposed algorithm using a dummy web structure consisting of 12 webpages. The results showed that the traditional PageRank algorithm has 74% more iterations than the proposed algorithm. The proposed algorithm returned a mean value of 1.00 compared to 1.32 for the traditional algorithm. These results confirm that the proposed algorithm saves a substantial amount of computing power while being more precise and not biased.

Public Choice Theory: Its Application and Challenges in Public-Sector

This paper deals with the application and challenges of Public Choice Theory (PCT) in public-sector. The public-sector plays foundational roles in social change. Citizens and customers perception and expectation of the government is increasing. They are pressurizing the public-sector more than ever before. Hence, I analyzed, critiqued, and contrasted PCT in terms of contemporary authors for performance efficiency and quality in the public-sector. The application of PCT in public-sector seemed more challenging than the application of the theory in private-sector. In transferring concepts and applications to the public-sector, it requires developing principles and tools for the less competitive and a different context of this sector. The article gave a critical overview of the different definitions, ideas, principles of the PCT, and improvement as applied in the public-sector to bring about quality public service and good governance. This paper based on the overall review of literature, such as relevant books, journals, articles, research results, newspapers, and web pages attempted to identify the challenges and remedies of enhancing service effectiveness and quality in public-sector and the study found out that the PCT is not universally applicable in the public-sector. Keywords: Public choice theory, Public-Sector, new public management, quality service, customer, citizen. DOI: 10.7176/JAAS/66-02 Publication date: July 31 st 2020

The Unprecedented Pandemic 'COVID-19' Effect on the Bangladesh Apparel Workers by Shivering the Apparel Supply Chain

COVID-19, known as a pandemic, affects the global economy. ILO declared this virus as a labor market and economic crisis. This study has been conducted for understanding the effect of COVID-19 on the apparel industry and the apparel manufacturing workers. This is a descriptive study, following the inductive procedure. The relevant information has been collected from the current academic literature, newspapers, reports publications, and relevant web pages. An online interview with the manufacturers, policymakers, trade unions, researchers, and academicians has been conducted for primary information collection. This study has found that the apparel industry is one of the most affected industries among the other industries by COVID-19. The retailers’ shops are being closed with having zero turnovers which leads to ordering cancelation to the manufacturing factories. Bangladesh's apparel manufacturing industry is also drastically affected due to COVID-19. The factories can’t pay the workers’ salaries in this critical situation. Therefore, millions of workers have been sent home without their wages. Sometimes, it has predicted that the workers would lose jobs due to factory closure. The Government took lots of initiatives i.e. tax rebates, reduce VAT, financial support to the owners to pay the workers’ wages, loan installment rescheduling, etc. Albeit, these initiatives were taken for the welfare of the factory owners and the workers remain oppressed. So, a proper policy strategy is indeed an emergency to support the destitute workforce during the COVID-19 as well as in future financial crises that can happen due to this kind of epidemic or any reason. This study will be supportive to the stakeholders of this sector to learn the impact of COVID-19 on the workers and make the necessary adjustment for the future betterment.

A systematic review on page ranking algorithms

Search engines are very useful tool now a days to fulfill the information need of a user. The performance of search engine mainly depends on page ranking algorithm which provides highly relevant web pages at the top of the search result. Page ranking algorithms based on content of the web page are called content based page ranking. The ranking algorithms which use link structure of web pages are called link structure based page ranking algorithms. This paper critically surveys various content based page ranking algorithms, link structure based page ranking algorithm and hybrid page ranking algorithms which are proposed by many researchers in recent years. Some critical issues in different page ranking algorithms are identified which open new paths for researchers working in this area.

Web Information Extraction for Finding Remedy Based on a Patient-Authored Text: A Study on Homeopathy

Automatic medical diagnosis and remedy finding is an active research area for decades. The increasing trend of finding health remedies through the internet emerged the necessity of research on the analysis of the patient-authored text. Focussed analysis of the patient-authored text can also help in automatic remedy finding. As the web contains a huge amount of medicine and diagnosis-related information, an intelligent system can extract the relevant information to provide a health remedy given a patient-authored text query. In this paper, we attempted to develop such a system. As the patients’ description of suffering plays a key role in homeopathy remedy finding, here we focussed on the homeopathy domain. As per the best of our knowledge, this is the first attempt in this domain. For the development, first, the patient-authored text is processed to identify the disease name and characteristic symptoms. Then a query is formed and a set of relevant web pages is retrieved. The retrieved pages are then processed in multiple levels to extract the medicine names. The appropriateness of the medicines is computed using a hybrid similarity scoring technique. The medicine having the highest similarity is suggested to the user. The system is tested using a set of real questions collected from various relevant websites. The evaluation results demonstrate that the system recommends a relevant remedy in 96.33% of cases.

Web Pages Credibility Scores for Improving Accuracy of Answers in Web-Based Question Answering Systems

Web-based question answering (QA) systems are effective in corroborating answers from multiple Web sources. However, Web also contains false, fabricated, and biased information that can have adverse effects on the accuracy of answers in Web-based QA systems. Existing, solutions focus primarily on finding relevant Web pages but either do not evaluate Web pages’ credibility or evaluate two to three out of seven credibility categories. This research proposed a credibility assessment algorithm that uses seven categories, including correctness, authority, currency, professionalism, popularity, impartiality, quality, for scoring credibility, where each credibility category consists of multiple factors. The credibility assessment module is added on top of an existing QA system to score answers based on the credibility of Web pages. The system ranks answers based on the Web pages’ credibility from where answers have been taken. The research conducted extensive quantitative tests on 211 factoid questions, taken from TREC QA data from 1999-2001. Our research findings show that credibility categories including correctness, professionalism, impartiality, and quality significantly improved the accuracy of answers. On the other hand, categories such as authority, currency, popularity played a minor role instead. This research hopes to allow researchers and experts in using the Web credibility assessment model to improve the accuracy of information systems. Credibility scores should assist Web users in selecting credible information, while also forcing content creators to focus more on publishing credible content.

The Role of the Food and Drug Administration in Drug Development: On the Subject of Proarrhythmia Risk.

The Food and Drug Administration (FDA) is responsible for the regulation of the pharmaceutical industry in the interest of protecting public health. The aim of this review was to outline the evolution and current role of the FDA in the development and approval of new drugs. Additionally, we describe current assessments of proarrhythmia risk to illustrate recent FDA initiatives intended to harness information technology to modernize the regulatory process. In order to identify the literature required to produce this review, search tools such as PubMed and Google Scholar were used to locate relevant web pages and articles. The job of the FDA is not only to ensure that high standards for drug efficacy and safety are applied to products available to American consumers and patients but also to balance the lengthy, costly process of maintaining these standards against the pressure to provide access to effective treatments earlier and without surplus expenditures. In order to provide expedited access to the newest effective therapies for critically ill patients in the safest way possible, the FDA has developed several accelerated pathways to fast-track drug approval. Through partnerships with industry and academic institutions, research is being conducted into how information technology can be integrated into the drug development process to improve its cost-effectiveness.

WS-BD-Based Two-Level Match: Interesting Sequential Patterns and Bayesian Fuzzy Clustering for Predicting the Web Pages from Weblogs

Abstract The rapid increase in information and technology has led to the increased amount of web pages, which raises the complexity in sticking to relevant web pages, and the visitor suffers due to wastage of time resulting in lack of satisfaction. This paper proposes a web page prediction method using a weighed support and Bhattacharya distance-based (WS-BD) two-level match. The major aim of the proposed method is to attain customer satisfaction. Initially, interesting sequential patterns are obtained using the weighed support that filters the sequential patterns obtained using a PrefixSpan algorithm based on the frequency, duration and recurrence of the web pages. Interesting sequential patterns are clustered using the proposed dice similarity-based Bayesian fuzzy clustering, and the web page is predicted using the two-level match based on Bhattacharya distance. The experimentation is performed using the CTI and MSNBC data which proves the effectiveness of the proposed method. The proposed method shows 9.59, 21.22 and 10.17% improvement than the existing FCM-KNN in terms of precision, recall and F measure for the CTI dataset. Also, the proposed method shows 2.58, 22.17 and 7.83% improvement than the existing FCM-KNN in terms of precision, recall and F measure for the MSNBC dataset.

Electronic Patient-Generated Health Data to Facilitate Disease Prevention and Health Promotion: Scoping Review.

BackgroundDigital innovations continue to shape health and health care. As technology socially integrates into daily living, the lives of health care consumers are transformed into a key source of health information, commonly referred to as patient-generated health data (PGHD). With chronic disease prevalence signaling the need for a refocus on primary prevention, electronic PGHD might be essential in strengthening proactive and person-centered health care.ObjectiveThis study aimed to review and synthesize the existing literature on the utilization and implications of electronic PGHD for primary disease prevention and health promotion purposes.MethodsGuided by a well-accepted methodological framework for scoping studies, we screened MEDLINE, CINAHL, PsycINFO, Scopus, Web of Science, EMBASE, and IEEE Digital Library. We hand-searched 5 electronic journals and 4 gray literature sources, additionally conducted Web searches, reviewed relevant Web pages, manually screened reference lists, and consulted authors. Screening was based on predefined eligibility criteria. Data extraction and synthesis were guided by an adapted PGHD-flow framework. Beyond initial quantitative synthesis, we reported narratively, following an iterative thematic approach. Raw data were coded, thematically clustered, and mapped, allowing for the identification of patterns.ResultsOf 183 eligible studies, targeting knowledge and self-awareness, behavior change, healthy environments, and remote monitoring, most literature (125/183, 68.3%) addressed weight reduction, either through physical activity or nutrition, applying a range of electronic tools from socially integrated to full medical devices. Participants generated their data actively (100/183, 54.6%), in combination with passive sensor-based trackers (63/183, 34.4%) or entirely passively (20/183, 10.9%). The proportions of active and passive data generation varied strongly across prevention areas. Most studies (172/183, 93.9%) combined electronic PGHD with reflective, process guiding, motivational and educational elements, highlighting the role of PGHD in multicomponent digital prevention approaches. Most of these interventions (110/183, 60.1%) were fully automatized, underlining broader trends toward low-resource and efficiency-driven care. Only a fraction (47/183, 25.6%) of studies provided indications on the impact of PGHD on prevention-relevant outcomes, suggesting overall positive trends, especially on vitals (eg, blood pressure) and body composition measures (eg, body mass index). In contrast, the impact of PGHD on health equity remained largely unexplored. Finally, our analysis identified a list of barriers and facilitators clustered around data collection and use, technical and design considerations, ethics, user characteristics, and intervention context and content, aiming to guide future PGHD research.ConclusionsThe large, heterogeneous volume of the PGHD literature underlines the topic’s emerging nature. Utilizing electronic PGHD to prevent diseases and promote health is a complex matter owing to mostly being integrated within automatized and multicomponent interventions. This underlines trends toward stronger digitalization and weaker provider involvement. A PGHD use that is sensitive to identified barriers, facilitators, consumer roles, and equity considerations is needed to ensure effectiveness.

Study on Unknown Term Translation Mining from Google Snippets

Bilingual web pages are widely used to mine translations of unknown terms. This study focused on an effective solution for obtaining relevant web pages, extracting translations with correct lexical boundaries, and ranking the translation candidates. This research adopted co-occurrence information to obtain the subject terms and then expanded the source query with the translation of the subject terms to collect effective bilingual search engine snippets. Afterwards, valid candidates were extracted from small-sized, noisy bilingual corpora using an improved frequency change measurement that combines adjacent information. This research developed a method that considers surface patterns, frequency–distance, and phonetic features to elect an appropriate translation. The experimental results revealed that the proposed method performed remarkably well for mining translations of unknown terms.

Day of the week to tweet: a randomised controlled trial

ObjectiveTo assess the effects of using health social media on different days of the working week on web activity.DesignIndividually randomised controlled parallel group superiority trial.SettingTwitter and Weibo.Participants194 Cochrane Schizophrenia Group...

IMPLEMENTATION OF DIGITAL INTERACTIVE TECHNOLOGIES AND E-LEARNING TOOLS IN ENGLISH LANGUAGE TEACHING FOR THE DEVELOPMENT OF TRANSLATORS’ PROFESSIONAL COMPETENCES (On The Basis of Higher Educational Institutions of Kazakhstan)

The alternations occurring due to globalization processes have determined the success of intercultural communication and professional development in our times. Since there is a necessity to interact with people as from other cultures so of various professions. Therefore, educators are responsible for an adequate instruction and as EL teachers we feel our class is the right place to develop specific abilities and professional skills such as intercultural communicative competence and professional competence development. Moreover, we should take into account that the growth of digital, interactive and Internet technologies, and spread of e-learning and distance education have impacted and reshaped the EL teaching and learning processes into a new wave. This paper focuses on the application of digital, interactive, Internet technologies and ICT as effective tools in intercultural communication and professional training of translators at higher education institutions of Kazakhstan. Based on some theoretical considerations, the author shares the most relevant webpages and sites, e-learning online tasks proposing different activities to develop our learners` intercultural abilities and professional skills. This paper discusses the ways in which technology can be used to create effective, engaging and relevant activities for our learners as in the classroom so distantly and virtually. The content will be of interest to anyone interested in using digital resources and interactive tools to support any form of education, namely e-learning, distant education or blended learning. The main aim of the paper is to introduce EL teachers to Internet technologies and ICT tools that they can experiment with and provide practical ideas for how they might and can apply these in the classroom and outside the classroom in order to develop learners` intercultural and professional competences.

Intelligent web search system for personalised web search based on recommendation of web page communities

In this paper an intelligent web search system is proposed based on recommendation of web page communities for personalised web search (PWS). Web page communities are set of web pages that provide the good quality resource on a given topic. The intelligent search system adapts the web search to the user's information need based on recommendation of web page communities. The groups of similar content clicked web pages in clusters are selected for generation of web page communities using maximum flow algorithm with hyperlink-induced topic search (HITS). The cluster of web page communities is selected for recommendations of relevant web pages to user during web search for effective web information retrieval. Experiment was conducted on collection of web query sessions in academics, entertainment and sports domain. The experimental results were compared with classic IR and PWS (HITS) based on same dataset and hence the results show the improvement in precision of search results using intelligent web search based on web page communities.

An intelligent inventive system for personalised webpage recommendation based on ontology semantics

Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users' web usage data. An overall accuracy of 87.73% is achieved by the proposed approach.

An intelligent inventive system for personalised webpage recommendation based on ontology semantics

Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users' web usage data. An overall accuracy of 87.73% is achieved by the proposed approach.

Towards common terminology on energy landscapes - preface

The COST RELY Glossary on Renewable Energy and Landscape Quality is the result of the European COST RELY project that focused on investigating the influence of renewable energy production on landscape quality. 31 people participated in developing and revising the definitions and descriptions for the 46 terms included in the glossary. Work was done in the period from 2015 to 2017 in multiple rounds of revision done by the RELY experts. Terms in the glossary are clustered into three groups: first group terms are directly connected to the landscape and its characteristics, the second one touches the planning process and methods, and the last one covers different renewable energy sources and production types. Each entry to the glossary consists of six elements: the term, definition, related terms, keywords, illustration(s) and sources. The terms are based on the expert knowledge of the contributors, scientific literature (monographs and articles), EU regulation, relevant web pages and other useful sources, stated in the Reference section. At the end of the glossary, terms are translated into 28 European languages including Esperanto. The glossary targets the researchers from the field, policy makers, local communities, investors in the sector of renewable energy and NGOs concerned with the matter in order to assure that people from different educational background and profession understand and use the term in the same manner. Beside from internal Action use, the terms shall contribute to existing glossaries on the relevant topics.

LionRank: lion algorithm-based metasearch engines for re-ranking of webpages

Due to the rapid growth of the web, the process ofcollecting the relevant web pages based on the user query is one of themajor challenging tasks in recent days. Hence, it is very complicated forthe users to know the most relevant information even though various searchengines are widely employed. To deal with users trouble in identifying therelevant information from the web, we have proposed a meta-lion searchengine to capture and analyze the ranking scores of various search enginesand thereby, generate the re-ranked score results. Accordingly, LionRank, alion algorithm-based meta-search engine is proposed for the re-ranking ofthe web pages. Here, different features like text based, factor based, rankbased and classifier based features are used by the underlying searchengines. In classifier based feature extraction, we have used the fuzzy integrated extended nearest neighbor (FENN) classifier to include thesemantics in feature extraction. Moreover, an intelligent re-ranking processis proposed based on the lion algorithm to fuse the features scoresoptimally. Finally, the results of the proposed LionRank is analyzed withthe web page database collected through four benchmark queries, and thequantitative performance are analyzed using precision, recall, and F-score.From the results, we proved that the proposed LionRank obtained the maximumF-score of 81%as compared with that of existing search engines likeQuadRank, Outrank, Google, Yahoo, and Bing.

Integrating Wikipedia Articles and Images into an Information Resource for Radiology Patients.

Wikipedia-an open-access online encyclopedia-contains a large number of medically relevant articles and images that may help supplement glossaries of radiology terms. We sought to determine the extent to which concepts from a large online radiology glossary developed as part of the Patient-Oriented Radiology Reporter (PORTER) initiative could be mapped to relevant Wikipedia web pages and images using automated or semi-automated approaches. The glossary included 4090 concepts with their definitions; the concept's preferred name and lexical variants, such as plurals, adjectival forms, synonyms, and abbreviations, yielded a total of 13,030 terms. Of the 4090 concepts, 3063 (74.9%) had a corresponding English-language Wikipedia page identified by automated search with subsequent manual review. We applied the MediaWiki application programming interface (API) to generate web-service calls to identify the images from each concept's corresponding Wikipedia page; three reviewers selected relevant images to associate with the glossary's concepts. Licensing terms for the images were reviewed. For 800 randomly sampled concepts that had associated Wikipedia pages, 362 distinct images were identified from the MediaWiki library and matched to 404 concepts (51%). Three images (1%) had unspecified licensing terms; the rest were in the public domain or available via a Creative Commons license. Wikipedia and the MediaWiki library offer a large collection of medical articles and images that can be incorporated into an online lay-language glossary of radiology terms though a semi-automated approach.

A Comparative Analysis of English Language Teacher Education Programs in Turkey and Malaysia

This paper reports on findings from a comparative study that attempted to identify the differences and similarities between the English Language Teacher Education Programs employed in Turkey and Malaysia. Employing a qualitative case study research design, a document analysis method was chosen to compare the programs in terms of several points. To this end, information related to each program was gathered from the relevant webpages of the programs and then subjected to content analysis. The analysis of the data revealed that the ELTEPs differ from each other in two main aspects, i.e. course-related and non-course related. In terms of course-related aspects, the programs vary across course contents, total credits, types of courses, with the Malaysian ELTEP having non-secular courses in the curriculum. Concerning non-course-related issues, it has emerged that differences lie in the length of the programs, the school types student-teachers can be appointed to after graduation, overseas experiences, length of field experience and career prospects. Finally, drawing on the differences and similarities between the two programs, suggestions are made to improve the programs by bearing in mind the strong sides of each program.

Multi term based co-term frequency method for term weighting in information retrieval

Nowadays, World Wide Web (WWW) has become the only source of all kind of information. Retrieving the relevant web pages based on user queries from WWW is an exigent task. Term frequency inverse document frequency (TF-IDF) is the most frequently used method for term weighting based on the occurrences and presence of a term inside the document. Retrieved document based on a single query term may not relate to the user search. This may lead the user to process the unwanted information. So, this paper proposes a new term weighting method named co-term frequency, in which the weight is assigned according to the multi terms which commonly occur in all documents. From the measures of precision, recall and F-score of the proposed method, it is clearly evident that the proposed framework retrieves the most relevant web pages when compared to other term weighting methods.