D. Sejal

Visvesvaraya Technological University

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Co-Authors

S. S. Iyengar

Florida International University

L.M. Patnaik

National Institute of Advanced Studies

K. R. Venugopal

Bangalore University

K R Venugopal

Bangalore University

D.K. Anvekar

Cognizant (India)

L.M. Patnaik

KIIT University

S. S. Iyengar

Florida International University

V. Tejaswi

Koneru Lakshmaiah Education Foundation

K. G. Shailesh

Visvesvaraya Technological University

T. Kamalakant

Bangalore University

Cooperative Institutions

Bangalore University

Visvesvaraya Technological University

Indian Institute of Science Bangalore

National Institute of Advanced Studies

Defence Institute of Advanced Technology

Florida International University

Meenakshi Academy of Higher Education and Research

Vijayanagara Institute of Medical Sciences

University of Miami

Louisiana State University

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Query Recommendation based on Query Relevance Graph.

Transactions on machine learning and data mining (2016)

D. Sejal K. G. Shailesh V. Tejaswi D.K. Anvekar K. R. Venugopal

Relevance

RDF query language

Source

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Citations (2)

ACSIR: ANOVA Cosine Similarity Image Recommendation in vertical search

International Journal of Multimedia Information Retrieval (2017)

D. Sejal T. Ganeshsingh K. R. Venugopal S. S. Iyengar L.M. Patnaik

Cosine similarity

Similarity (geometry)

Digitization

Result set

10.1007/s13735-017-0124-0

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Citations (6)

IRAbMC: Image Recommendation with Absorbing Markov Chain

D. Sejal V Rashmi D.K. Anvekar K. R. Venugopal S. S. Iyengar

Image Recommendation is an important feature for search engine as tremendous amount images are available online. It is necessary to retrieve relevant images to meet user's requirement. In this paper, we present an algorithm Image Recommendation with Absorbing Markov Chain (IRAbMC) to retrieve relevant images for user input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Absorbing Markov chain is used to calculate keyword relevance. Experiments results show that the IRAbMC algorithm outperforms Markovian Semantic Indexing (MSI) method with improved relevance score of retrieved ranked images.

Relevance

Feature (linguistics)

Relevance Feedback

10.1109/indicon.2015.7443286

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Citations (1)

Image recommendation based on keyword relevance using absorbing Markov chain and image features

International Journal of Multimedia Information Retrieval (2016)

D. Sejal V Rashmi K R Venugopal S. S. Iyengar L.M. Patnaik

Relevance

Feature (linguistics)

Relevance Feedback

10.1007/s13735-016-0104-9

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Citations (18)

WNPWR: Web navigation prediction framework for webpage recommendation

D. Sejal T. Kamalakant V. Tejaswi D.K. Anvekar K R Venugopal

Huge amount of user request data is generated in web-log. Predicting users' future requests based on previously visited pages is important for web page recommendation, reduction of latency, on-line advertising etc. These applications compromise with prediction accuracy and modelling complexity. we propose a Web Navigation Prediction Framework for webpage Recommendation(WNPWR) which creates and generates a classifier based on sessions as training examples. As sessions are used as training examples, they are created by calculating average time on visiting web pages rather than traditional method which uses 30 minutes as default timeout. This paper uses standard benchmark datasets to analyze and compare our framework with two-tier prediction framework. Simulation results shows that our generated classifier framework WNPWR outperforms two-tier prediction framework in prediction accuracy and time.

Benchmark (surveying)

10.1109/retis.2015.7232864

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Citations (2)

Image Recommendation Based on ANOVA Cosine Similarity

Procedia Computer Science (2016)

D. Sejal T. Ganeshsingh K. R. Venugopal S. S. Iyengar L.M. Patnaik

Online shopping is very popular and has grown exponentially due to revolution in digitization. It is a fundamental requirement of all the search engines to provide recommendation to identify user preferences. In this paper, we have proposed an algorithm to recommend images based on ANOVA Cosine Similarity where text and visual features are integrated to fill the semantic gap. Visual synonyms of each term are computed using ANOVA p-value by considering image visual features on text-based search. Expanded queries are generated for user input query and text based search is performed to get initial result set. Pair-wise image cosine similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain specific site. Experiments results shows that the ACSIR outperforms iLike method by providing more relevant products to the user input query.

Cosine similarity

Similarity (geometry)

Digitization

Result set

10.1016/j.procs.2016.06.091

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Citations (22)

Query Click and Text Similarity Graph for Query Suggestions

Lecture notes in computer science (2015)

D. Sejal K. G. Shailesh V. Tejaswi D.K. Anvekar K. R. Venugopal

Jaccard index

Spatial Query

Similarity (geometry)

10.1007/978-3-319-21024-7_22

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Citations (0)

DRDLC

R. Ramya Ganesh Singh T.D. Sejal K R Venugopal S. S. Iyengar

In recent years, the availability of digital documents over web is increased drastically and there is a need for effective methods to retrieve and organize the digital documents. Since data is dispersed globally and is unorganized, it is a challenging task to develop an effective methods that can generate high quality features in these documents. It is necessary to reduce the gap between users search intention and the retrieved results known as semantic gap. In this paper, Discovering Relevant Documents using Latent Dirichlet Allocation and Cosine Similarity (DRDLC) is proposed. Word similarity is computed using CS Cosine Similarity present in search results documents. LDA is applied on extracted patterns and documents. Hashing is used to extract high relevant documents efficiently. Further, term synonyms are identified using word net and the documents are re-ranked. Experiments using the model Relevance Feature Discovery (RFD) on Reuters Corpus Volume-1 (RCV-1) show that the proposed DRDLC framework results in improved performance by providing more relevant documents to the user input query.

Cosine similarity

Relevance

Similarity (geometry)

Feature (linguistics)

10.1145/3301326.3301342

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Citations (10)

Webpage recommendation with web navigation prediction framework

International Journal of Knowledge and Web Intelligence (2016)

D. Sejal T. Kamalakant D.K. Anvekar K. R. Venugopal S. S. Iyengar

Huge amount of user request data is generated in web-log. Predicting users' future requests based on previously visited pages is important for webpage recommendation, reduction of latency and online advertising. These applications compromise with prediction accuracy and modelling complexity. We propose a web navigation prediction framework for webpage recommendation (WNPWR) which creates and generates a classifier based on sessions as training examples. As sessions are used as training examples, they are created by calculating the average time on visiting webpages rather than traditional method which uses 30 minutes as default timeout. This paper uses standard benchmark datasets to analyse and compare our framework with two-tier prediction framework. Simulation results show that our generated classifier framework WNPWR outperforms two-tier prediction framework in prediction accuracy and time.

Benchmark (surveying)

10.1504/ijkwi.2016.078733

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Citations (0)

IR_URFS_VF: image recommendation with user relevance feedback session and visual features in vertical image search

International Journal of Multimedia Information Retrieval (2016)

D. Sejal Divakara Abhishek K. R. Venugopal S. S. Iyengar L.M. Patnaik

Relevance Feedback

Relevance

Cosine similarity

Similarity (geometry)

10.1007/s13735-016-0111-x

Cite

Citations (5)