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JournalISSN: 0975-4024

International journal of engineering and technology 

Engg Journals Publications
About: International journal of engineering and technology is an academic journal. The journal publishes majorly in the area(s): Cloud computing & Fuzzy logic. It has an ISSN identifier of 0975-4024. It is also open access. Over the lifetime, 8965 publications have been published receiving 23351 citations.


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Journal ArticleDOI
TL;DR: A survey of various models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.
Abstract: Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

141 citations

Journal ArticleDOI
TL;DR: In this paper, the authors highlight the top technologies for tourism and Hospitality with regard to AR and VR, and highlight the most useful applications that are attracting greater attention from tourism researchers and professionals.
Abstract: Virtual Reality and Augmented Reality, these days, is offering many useful applications that is attracting greater attention from tourism researchers and professionals. As, AR and VR technologies are evolving, the number of scientific applications is also at increase. VR and AR are proving their worth especially when planning, marketing, education, tourist sport preservation coming to light. The aim of this research paper is to highlight top technologies for Tourism and Hospitality with regard to AR and VR.

103 citations

Journal Article
TL;DR: The paper is to find an efficient way of storing unstructured data and appropriate approach of fetching data and the public tweets of Twitter are targeted in this work to organize.
Abstract: Nowadays, most of information saved in companies are unstructured models. Retrieval and extraction of the information is essential works and importance in semantic web areas. Many of these requirements will be depend on the unstructured data analysis. More than 80% of all potentially useful business information is unstructured data, in kind of sensor readings, console logs and so on. The large number and complexity of unstructured data opens up many new possibilities for the analyst. Text mining and natural language processing are two techniques with their methods for knowledge discovery from textual context in documents. This is an approach to organize a complex unstructured data and to retrieve necessary information. The paper is to find an efficient way of storing unstructured data and appropriate approach of fetching data. Unstructured data targeted in this work to organize, is the public tweets of Twitter. Building an Big Data application that gets stream of public tweets from twitter which is latter stored in the HBase using Hadoop cluster and followed by data analysis for data retrieved from HBase by REST calls is the pragmatic approach of this project. Keyword: Unstructured Data, Hadoop, HBase, Data Mining

98 citations

Journal ArticleDOI
TL;DR: The paper proposes that learning analytics is dependent on personalised approach for both educators and students, and defines the characterising features that represents the relationship between learning analytics and personalised learning environment.
Abstract: This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in consider ing personalised learning within the context framework of learning analytics

86 citations

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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202122
2020187
2019251
20186,075
2017961
2016284