Intelligent Web Caching Using Adaptive Regression Trees, Splines, Random Forests and Tree Net
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Reorganisation of Adaptive Websites Using Web Usage Mining Techniques Dr
International Journal of Computer & Organization Trends –Volume 4 Issue 3 May to June 2014 Reorganisation of Adaptive Websites using Web Usage Mining Techniques Dr. Ananthi Sheshasaayee#1, V.Vidyapriya#2 #1Head and Associate Professor, Department of Computer Science, Quaid-E-Millath Government College for Women (A), Chennai #2.Research Scholar, Department of Computer Science, Quaid-E-Millath Government College for Women (A), Chennai Abstract— Web Usage Mining is that area of Web Mining In practice, the three Web mining tasks above could which deals with the extraction of interesting knowledge be used in isolation or combined in an application, from logging information produced by Web servers. The especially in Web content and structure mining since motive of mining is to find users’ access models the Web documents might also contain links. Web automatically and quickly from the vast Web log data, such content mining is the process of extracting knowledge as frequent access paths, frequent access page groups and user clustering. Through web usage mining, the server log, from the content of documents or their descriptions. registration information and other relative information left Web document text mining, resource discovery based by user access can be mined with the user access mode on concepts indexing or agent; based technology may which will provide foundation for decision making of also fall in this category. Web structure mining is the organizations. Adaptive web sites are web sites that process of inferring knowledge from the World Wide automatically improve their organization and presentation Web organization and links between references and by learning from their user access patterns. -
A Study of Design Aspects of Web Personalization for Online Users in India
A Study of Design Aspects of Web Personalization for Online Users in India A Thesis submitted to Gujarat Technological University for the Award of Doctor of Philosophy in Management By Darshana Desai Enrollment No: 129990992004 Under supervision of Prof. Dr. Satendra Kumar GUJARAT TECHNOLOGICAL UNIVERSITY AHMEDABAD August - 2017 A Study of Design Aspects of Web Personalization for Online Users in India A Thesis submitted to Gujarat Technological University for the Award of Doctor of Philosophy in Management By Darshana Desai Enrollment No: 129990992004 under supervision of Prof. Dr. Satendra Kumar GUJARAT TECHNOLOGICAL UNIVERSITY AHMEDABAD August – 2017 © DARSHANA DESAI DECLARATION I declare that the thesis entitled A study of design aspects of web personalization for online users in India submitted by me for the degree of Doctor of Philosophy is the record of research work carried out by me during the period from October 2012 to December 2016 under the supervision of Prof. Dr. Satendra Kumar Professor and Research Head at C. K Shah Vijapurwala Institute of Management & Research(CKSVIM), Vadodara, Gujarat and this has not formed the basis for the award of any degree, diploma, associateship, fellowship, titles in this or any other University or other institution of higher learning. I further declare that the material obtained from other sources has been duly acknowledged in the thesis. I shall be solely responsible for any plagiarism or other irregularities, if noticed in the thesis. Signature of the Research Scholar: ………………………….. Date: …………………. Name of Research Scholar: Darshana Desai Place: Vadodara iv. CERTIFICATE I certify that the work incorporated in the thesis entitled A study of design aspects of web personalization for online users in India submitted by Shri / Smt. -
A Data Warehouse-Based System for Adaptive Website Recommendations
AWESOME – A Data Warehouse-based System for Adaptive Website Recommendations Andreas Thor Erhard Rahm University of Leipzig, Germany {thor, rahm}@informatik.uni-leipzig.de Abstract There are many ways to automatically generate rec- ommendations taking into account different types of in- Recommendations are crucial for the success of formation (e.g. product characteristics, user characteris- large websites. While there are many ways to de- tics, or buying history) and applying different statistical or termine recommendations, the relative quality of data mining approaches ([JKR02], [KDA02]). Sample these recommenders depends on many factors approaches include recommendations of top-selling prod- and is largely unknown. We propose a new clas- ucts (overall or per product category), new products, simi- sification of recommenders and comparatively lar products, products bought together by customers, evaluate their relative quality for a sample web- products viewed together in the same web session, or site. The evaluation is performed with products bought by similar customers. Obviously, the AWESOME (Adaptive website recommenda- relative utility of these recommendation approaches (rec- tions), a new data warehouse-based recommen- ommenders for short) depends on the website, its users dation system capturing and evaluating user and other factors so that there cannot be a single best ap- feedback on presented recommendations. More- proach. Website developers thus have to decide about over, we show how AWESOME performs an which approaches they should support and where and automatic and adaptive closed-loop website op- when they should be applied. Surprisingly, little informa- timization by dynamically selecting the most tion is available in the open literature on the relative qual- promising recommenders based on continuously ity of different recommenders. -
Applications of Machine Learning in Software Engineering
APPLICATIONS OF MACHINE LEARNING IN SOFTWARE ENGINEERING Manthan Patel1, Vivek Kumar Prasad2 1Student, Department of Computer Engineering, Nirma University, (India) 2Professor, Department of Computer Engineering, Nirma University, (India) ABSTRACT Machine Learning, the branch of Artificial Intelligence, is an ability for machine to learn, unlearn and relearn things from human activities as well as environment and gives more relevant result or drives towards it. It is more feasible as well as cost effective because it nullifies the manual efforts. It became a well-known topic due to its usages and therefore many people are trying to learn it. This paper mainly focuses on a brief about the different types of applications in software engineering and challenges for development. Keywords: Applications, Challenges, Machine learning I. INTRODUCTION The goal of learning for anything or anyone is to solve the problem from previous records or experiences. Learning for machines is nothing but to become more familiar with the thing in such a way that can help in many ways like weather prediction, recommendation based on the taste, deciding route in traffic, diagnosing samples with most accurate output, etc. Mainly a machine learning does the statistical analysis of data and extract and use most relevant data for solving the current situation or for prediction of future events. If we consider an example of data analysis from a warehouse which produce goods, the machine will take raw data as input and manipulate them such that the data will help the owner to understand as well as predict about profit making products among them so that the overall lose can be reduced. -
Maquetación 1
DIGIBÍS ® Newsletter. No. 19. January-June, 2018 Information about enriched digitization, software for Libraries, Archives and Museums and international standards . Archivo Carmen Martín Gaite Con el módulo de descripciones archivísticas de DIGIBIB Adapted to all devices The Municipal Archive of Castellón SUMMARY Editorial 3 INTERNATIONAL DIGICLIC DIGIBÍ S ® Newsletter Europeana CEO Tachi Hernando de Larramendi 3rd International EuropeanaTech Conference 4 Project Manager Europeana Business Plan 2018: Xavier Agenjo Bullón CFO democratizing culture 6 Nuria Ruano Penas IT Manager Jesús L. Domínguez Muriel Art Director EVENTS Antonio Otiñano Martínez Sales Manager Courses Javier Mas García Technology Coordinator Course-workshop of the Ignacio Francisca Hernández Carrascal Larramendi Foundation in the AEF 7 Administration Department María Luz Ruiz Rodríguez (coord.) José María Alcega Barroeta IT Department Feli Matarranz de Antonio (coord.) GLAM APPLICATIONS Alejandra Arri Pacheco Andrés Felipe Botero Zapata Julio Diago García Virtual Libraries Carlos Henche Hernández Luis Panadero Guardeño Linked data in the Fernando Román Ortega Innovation Department Virtual Library of Castilla y León 8 Paulo César Juanes Hernández (coord.) Noemí Barbero Urbano Virtual archive of the María Isabel Campillejo Suárez Susana Hernández Rubio municipality of Castellón 10 Montserrat Martínez Guerra Digitization Department The Polymath Virtual Library Francisco Viso Parra (coord.) María José Escuté Serrano present in Wikidata 12 Amando Martínez Catalán Javier Ramos