How to link Google Scholar to the UJ Full-Text Online Articles

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Google Scholar is a favorite starting point for searching, so the linking to the subscribed full-text journals by the Library will benefit the University of Johannesburg researchers and students.

How to link Google Scholar to the UJ Full-Text Online Articles
Pavlinka Kovatcheva, Faculty Librarian: Science
University of Johannesburg
June 2017; E-mail: pkovatcheva@uj.ac.za

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How to link Google Scholar to the UJ full-text articles
Step 1: Access Google Scholar
https://scholar.google.co.za/

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How to link Google Scholar to the UJ full-text articles
Step 2: Select “Library Links” from the Menu
https://scholar.google.co.za/

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How to link Google Scholar to the UJ full-text articles
Step 3: Type “University of Johannesburg” and search
https://scholar.google.co.za/

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How to link Google Scholar to the UJ full-text articles
Step 4: Select the options with full-text @UJ and “Save”
https://scholar.google.co.za/

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How to link Google Scholar to the UJ full-text articles
Step 5: Search Google Scholar and Click on the Link - full-text @UJ
https://scholar.google.co.za/

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How to link Google Scholar to the UJ full-text articles
Step 6: Linking to the UJ Database with full-text access
https://scholar.google.co.za/

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When searching Google Scholar:
• The linking to the FULL-TEXT articles will work only for UJ affiliated users
• The linking works only for articles available in ONLINE format
• The linking to some content may not work. Please, follow up the reference through
the Library Resources (Journal title search via UJ: Library Catalogue or A-to-Z
Journals)
Recommended access to UJ Science Library Resources:
• Science Library LibGuides
• Science – Faculty LibGuide – as a starting point 
Science LibGuides: http://uj.ac.za.libguides.com/science UJ Library: https://www.uj.ac.za/library
In Conclusion

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Introduction to the Open Refine RDF tool

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- The document discusses market basket analysis and association rule mining, which are techniques used to analyze purchasing patterns in transactional data. - It provides an example of an association rule discovered from store transaction data: "If a basket contains beer, it is likely to also contain diapers." Knowing this, the store changed its layout to place diapers and beer next to each other, increasing sales of both products. - The key measures for evaluating association rules are support, confidence and lift, which indicate how often items are purchased together versus by chance alone. Market basket analysis can help businesses promote complementary products and increase overall revenue.

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This document discusses web usage mining and the associated processes. It introduces web usage mining as the automatic discovery of patterns in clickstream data and user interactions with websites. The goal is to analyze user behavioral patterns and profiles. Key aspects covered include preprocessing log data, identifying user sessions, integrating e-commerce events, and applying data mining techniques. Web usage mining is used to gain insights into user behaviors and personalize websites.

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Association rule mining

Utkarsh Sharma

The document discusses association rule mining and the Apriori and FP-Growth algorithms. It provides the following information: - Association rule mining discovers interesting relationships between variables in large databases. It expresses relationships between frequently co-occurring items as association rules. - The Apriori algorithm uses frequent itemsets to generate association rules. It employs an iterative approach and pruning to reduce candidate sets. - FP-Growth improves upon Apriori by compressing transaction data into a frequent-pattern tree structure and scanning the database twice instead of multiple times. This improves mining efficiency over Apriori.

Data mining to predict academic performance.

Ranjith Gowda

This document proposes using data warehousing and data mining techniques to predict student academic performance in schools. It describes collecting student data like scores, attendance, discipline, and assignments into a data warehouse. Data mining methods are then used to analyze the student data and identify relationships between variables to predict performance, such as whether students are progressing, being retained, or conditionally progressing. The results could help schools identify students at risk of failing and take actions to help them succeed.

NGSI-LD IoT Agents

FIWARE

This training camp teaches you how FIWARE technologies and iSHARE, brought together under the umbrella of the i4Trust initiative, can be combined to provide the means for creation of data spaces in which multiple organizations can exchange digital twin data in a trusted and efficient manner, collaborating in the development of innovative services based on data sharing and creating value out of the data they share. SMEs and Digital Innovation Hubs (DIHs) will be equipped with the necessary know-how to use the i4Trust framework for creating data spaces!

Ontology

Sudarsun Santhiappan

The document introduces ontology and describes what it is from both philosophical and computer science perspectives. An ontology in computers consists of a vocabulary to describe a domain, specifications of the meaning of terms, and constraints capturing additional knowledge about the domain. It then provides an example ontology and discusses applications of ontologies such as for the semantic web. It also discusses important considerations for building ontologies such as collaboration, versioning, and ease of use.

Naive Bayes Classifier in Python | Naive Bayes Algorithm | Machine Learning A...

Edureka!

** Machine Learning Training with Python: https://www.edureka.co/python ** This Edureka tutorial will provide you with a detailed and comprehensive knowledge of the Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial: 1. What is Naive Bayes? 2. Bayes Theorem and its use 3. Mathematical Working of Naive Bayes 4. Step by step Programming in Naive Bayes 5. Prediction Using Naive Bayes Check out our playlist for more videos: http://bit.ly/2taym8X

Introduction To Tensorflow

Rayyan Khalid

Web mining

Daminda Herath

Cassandra Data Modelling

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Information retrieval system

Leslie Vargas

1. The document defines key terms related to information retrieval systems such as information, retrieval, system, and discusses the basic components and functions of IRS. 2. It explains that the role of users is to formulate queries, and the role of librarians is to assist users in meeting their information needs. 3. The document contrasts older IRS that retrieved entire documents with modern IRS that allow storage, organization, and access to text and multimedia information through techniques like keyword searching and hyperlinks.

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Lecture13 - Association Rules

Albert Orriols-Puig

This document provides an introduction to association rule mining. It begins with an overview of association rule mining and its application to market basket analysis. It then discusses key concepts like support, confidence and interestingness of rules. The document introduces the Apriori algorithm for mining association rules, which works in two steps: 1) generating frequent itemsets and 2) generating rules from frequent itemsets. It provides examples of how Apriori works and discusses challenges in association rule mining like multiple database scans and candidate generation.

Advances in Learning Analytics and Educational Data Mining

MehrnooshV

Session 8 - Creating Data Processing Services | Train the Trainers Program

FIWARE

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Nees Jan van Eck

Building Linked Data Applications

EUCLID project

This presentation gives details on technologies and approaches towards exploiting Linked Data by building LD applications. In particular, it gives an overview of popular existing applications and introduces the main technologies that support implementation and development. Furthermore, it illustrates how data exposed through common Web APIs can be integrated with Linked Data in order to create mashups.

Linked Open Data Principles, Technologies and Examples

Open Data Support

Iot enabled technologies

ShilpaKrishna6

This document discusses key enabling technologies for the Internet of Things (IoT), including wireless sensor networks, embedded systems, cloud computing, communication protocols, and big data analytics. It provides examples of how these technologies are used in various IoT applications and systems. It also outlines some characteristics of big data generated by IoT systems and describes the basic components of embedded systems and communication protocols used in IoT.

Lect7 Association analysis to correlation analysis

hktripathy

Association rule mining aims to discover interesting relationships between items in large datasets. The document discusses key concepts in association rule mining including support, confidence, and correlation. Support measures how frequently an itemset occurs, while confidence measures the conditional probability of an itemset given another itemset. Correlation evaluates statistical dependence between itemsets and can be used to measure lift. Various measures are proposed to evaluate interestingness and redundancy of discovered rules.

The Pursuit of International Recognition (Indonesian Scholar Forum, ISIC PPI ...

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How to link Google Scholar to the UJ Full-Text Online Articles

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