A personalised information support system for searching portals and e‐resources

Sirisha, B.S.; Jeevan, V.K.J.; Kumar, Ram; Goswami, Arkopal K.

doi:10.1108/00330330910934129

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…The inclusion of different methods described in this study job poses a number of difficulties for further studies, such as validating the practicality of the suggested model in actual surroundings: using a realistic medical environment by considering various priorities for digital medical images. Incorporate more advanced methods for the recovery of medical image [44]. Computing and Computer-Assisted Intervention (pp.…”

Section: Future Scopementioning

confidence: 99%

Modified Location Model Estimation using Content Based Medical Image Retrieval

Kumar

2019

IJMTS

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Image has become more and more difficult to process for human beings. Perfect results cannot be obtained through Content Based Medical Image Retrieval (CBMIR). The CBMIR was implemented to find order effectively retrieve the picture from an enormous database. Deep learning has taken Artificial Intelligence (AI) at an unprecedented rate through revolution and infiltration in the medical field. It has access to vast quantities of information computing energy of effective algorithms of Machine Learning (ML). It enables Artificial Neural Network (ANN) to attain outcomes nearly every Deep Learning (DL) problems. It helps ANN to achieve results everywhere. It is a difficult task to obtain medical images from an anatomically diff dataset. The goal of the research is to automate the medical image recovery scheme that incorporates subject and place probabilities to improve efficiency. It is suggested to integrate the different data or phrases into a DL location model. It is also measuring a fresh metric stance called weighted accuracy (wPrecision). The experiment will be conducted on two big medical image datasets revealing that the suggested technique outperforms current medical imaging technologies in terms of accuracy and mean accuracy. The CBMIR have about 8,000 pictures, the proposed technique will attain excellent precision (nearly 90 percent). The proposed scheme will attain greater precision for the top ten pictures (97.5 percent) as compared to the last CBMIR recovery technologies with 15,000 picture dataset. It will assist doctors with better accuracy in obtaining medical images.

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Section: Future Scopementioning

confidence: 99%

Modified Location Model Estimation using Content Based Medical Image Retrieval

Kumar

2019

IJMTS

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“…"There is considerable difficulty in getting real and correct user interests and mapping them effectively into the products and services offered by the library. Also, the interests of users keep on changing continuously" (Sirisha et al, 2009). Unless a user logs-in with the system or has previous browsing history on a site, the system will not have a basis from which to generate content relevance.…”

mentioning

confidence: 99%

Leveraging student course enrollment data to infuse personalization in a library website

Chan

2014

Library Hi Tech

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Purpose -The purpose of this paper is to describe the benefits of integrating personalization within a library web site and presents methodology for achieving this goal within an academic setting. Design/methodology/approach -The project documented in this study explores the use of student course enrollment data as the basis for creating a personalized library web site. Off-the-shelf, open source applications are used in conjunction with existing university data to deliver a final product that offers an enhanced user experience for the university community. Findings -Adaptive personalization is increasingly commonplace on the web. Academic libraries have a unique source of existing data that offers the potential of adding personalization to the library web site. At present, the personalization of library online services remains largely unexplored. This project illustrates one relatively low-cost method to help libraries interested in creating personalized web sites. Practical implications -This paper provides a guide for libraries interested in the implementation of personalization within their web sites. Originality/value -The project described in this case study is highly unique within libraries. The paper outlines the feasibility and technical requirements associated with using course enrollment data to add personalized content to a library web site.

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Calculating altmetrics based on the data obtained at users’ accessing library e-catalogs

Гончаров

Колосов

2021

Naučnye i tehničeskie biblioteki

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The authors examine the libraries’ independent calculations and analysis of alternative metrics to determine user-defined relevancy of publications. Logfiles collected by web-servers at users’ accessing e-catalogs may be used for such calculations and analysis. The almetrics’ key advantages as compared to traditional bibliometrics and webometrics are: real-time representation; openness and transparency; coverage of wider non-academic audience; coverage of various sources and research findings. The web-server log-files enable to analyze number of user views of bibliographic records in e-catalog, to rate authors, individual publications and topical queries for a time period. In case of links to full texts, the number of pdf. documents loaded by users and n umber of p ages viewed ( when library computer-based system supports full-text document scrolling mode) may be defined. The authors also discuss specificity of handling log-files for various analysis types: statistical, content addressable, defining sequential sets, clustering. The functionality and table structure of RNPLST’s software module for advanced bibliometric analysis is described. The further task of the study will be to define trends of user behavior and forecasts for user group behavior when using e-catalog.The article is prepared within the framework of the State Order to Russian National Public Library for Science and Technology “Development and improvement of the system of Open Archive of integrated information and library resources of Russian National Public Library for Science and Technology as modern knowledge management system in digital environment: on the way to Open Science” № 075-01300-20-00 for 2020–2022.

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A personalised information support system for searching portals and e‐resources

Cited by 5 publications

References 17 publications

Modified Location Model Estimation using Content Based Medical Image Retrieval

Modified Location Model Estimation using Content Based Medical Image Retrieval

Leveraging student course enrollment data to infuse personalization in a library website

Calculating altmetrics based on the data obtained at users’ accessing library e-catalogs

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