scispace - formally typeset
Search or ask a question
Author

Senthilkumar N C

Bio: Senthilkumar N C is an academic researcher from VIT University. The author has contributed to research in topics: Domain (software engineering) & Ranking (information retrieval). The author has an hindex of 1, co-authored 4 publications receiving 3 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: The main aim of this paper is to design and implement a personalized search engine which works based on the domain of the user with the specific constraints suggested by the user and caters to customized needs with collaborative feedback using fuzzy decision tree based on fuzzy rules.
Abstract: In the fast moving world, users cross over large amount of data for their daily life. Due to the misinterpretation of the context, user cannot retrieve the proper context or failure to retrieve the information. The main aim of this paper is to design and implement a personalized search engine which works based on the domain of the user with the specific constraints suggested by the user. In this paper, the proposed system, build a search engine with web content which get information from the document corpus for the domain through the cloud databases. Web search engine re-ranks the generic results based on a ranking of a context linked with the domain. In this system, collaborative search service helps to improve the relevancy of the search results and to reduce the overtime on bad links and hence caters to customized needs with collaborative feedback using fuzzy decision tree based on fuzzy rules.

4 citations

Journal ArticleDOI
Senthilkumar N C1
30 Oct 2019

3 citations

Journal ArticleDOI
Senthilkumar N C1
01 Jan 2020

2 citations

Journal ArticleDOI
12 Jun 2020
TL;DR: F fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios and the future needs of users are categorized using this proposed system.
Abstract: The user interest in content searching in the web will be changed over by time.,The system is in need to find the content of user over the temporal aspects.,So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.,So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.,In this work, fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios.,In this proposed work, both the long-term and short-term interest are evaluated using the specialized user interface designed to retrieve the user interest based on the user searching activities.,This work also categorizes the future needs of users using this proposed system.

1 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: This work proposes a Profile Aware ObScure Logging (PaOSLo) Web search privacy-preserving protocol that mitigates the digital traces a user leaves in Web searching and compares the performance of PaOSLo with modern distributed protocols like OSLo and UUP(e).
Abstract: Web search querying is an inevitable activity of any Internet user. The web search engine (WSE) is the easiest way to search and retrieve data from the Internet. The WSE stores the user’s search queries to retrieve the personalized search result in a form of query log. A user often leaves digital traces and sensitive information in the query log. WSE is known to sell the query log to a third party to generate revenue. However, the release of the query log can compromise the security and privacy of a user. In this work, we propose a Profile Aware ObScure Logging (PaOSLo) Web search privacy-preserving protocol that mitigates the digital traces a user leaves in Web searching. PaOSLo systematically groups users based on profile similarity. The primary objective of this work is to evaluate the impact of the systematic group compared to random grouping. We first computed the similarity between the users’ profiles and then clustered them using the K-mean algorithm to group the users systematically. Unlikability and indistinguishability are the two dimensions in which we have measured the privacy of a user. To compute the impact of systematic grouping on a user’s privacy, we have experimented with and compared the performance of PaOSLo with modern distributed protocols like OSLo and UUP(e). Results show that, at the top degree of the ODP hierarchy, PaOSLo preserved 10% and 3% better profile privacy than the modern distributed protocols mentioned above. In addition, the PaOSLo has less profile exposure for any group size and at each degree of the ODP hierarchy.

5 citations

Journal ArticleDOI
11 May 2020
TL;DR: A forecasting method is proposed in this paper to predict possible campus placement of any institution using an ensemble approach based voting classifier for choosing best classifier models to achieve better result over other classifiers.
Abstract: Campus placement is a measure of students’ performance in a course. A forecasting method is proposed in this paper to predict possible campus placement of any institution. Data mining and knowledge discovery processes on academic career of students are applied. Supervised machine learning technique based classifiers are used for achieving this process. It uses an ensemble approach based voting classifier for choosing best classifier models to achieve better result over other classifiers. Experimental results have indicated 86.05% accuracy of ensemble based approach which is significantly better over other classifiers.

5 citations

Journal ArticleDOI
TL;DR: The experimental results show that the design system has high recall rate, high throughput, and the construction time of each data item index in different files is short, which improves the search efficiency and search accuracy.
Abstract: In order to improve the search performance of rich text content, a cloud search engine system based on rich text content is designed. On the basis of traditional search engine hardware system, several hardware devices such as Solr index server, collector, Chinese word segmentation device and searcher are installed, and the data interface is adjusted. On the basis of hardware equipment and database support, this paper uses the open source Apache Tika framework to obtain the metadata of rich text documents, implements word segmentation according to the rich text content and semantics, and calculates the weight of each keyword. Input search keywords, establish a text index, use BM25 algorithm to calculate the similarity between keywords and text, and output the search results of rich text according to the similarity calculation results. The experimental results show that the design system has high recall rate, high throughput, and the construction time of each data item index in different files is short, which improves the search efficiency and search accuracy.

3 citations

Journal ArticleDOI
TL;DR: In this paper , the authors used data from 292 children with an autism spectrum disorder to assess the development of children and the detection of ASD and suggest the need to strengthen the training of health professionals in aspects such as psychology and developmental disorders.
Abstract: Autism is a disorder of neurobiological origin that originates a different course in the development of verbal and nonverbal communication, social interactions, the flexibility of behavior, and interests. The results obtained offer relevant information to reflect on the practices currently used in assessing the development of children and the detection of ASD and suggest the need to strengthen the training of health professionals in aspects such as psychology and developmental disorders. This study, based on genuine and current facts, used data from 292 children with an autism spectrum disorder. The input dataset has 20 characteristics, and the output dataset has one attribute. The output property indicates whether or not a certain person has autism. The research study first and foremost performed data pretreatment activities such as filling in missing data gaps in the data collection, digitizing categorical data, and normalizing. The features were then clustered using k-means and x-means clustering methods, then artificial neural networks and a linguistic strong neurofuzzy classifier were used to classify them. The outcomes of each strategy were examined, and their respective performances were compared.

2 citations

Proceedings ArticleDOI
Eleni Ilkou1
25 Apr 2022
TL;DR: In this paper , the authors adopt a similar technique in the educational domain in e-learning platforms by deploying Personal Knowledge Graphs (PKGs) to represent users and learners, and propose a novel PKG development that relies on ontology and interlinks to Linked Open Data.
Abstract: Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia [21]. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users’ featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.

2 citations