neepa shah

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Collapp- Real Time Online Code Collaborator

— Online Code Collaborator (Collapp) is web based

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Drowsiness Detection based on Eye Movement, Yawn Detection and Head Rotation

Research shows that driver fatigue is one of the major reasons of road accidents. In this paper, ... more Research shows that driver fatigue is one of the major reasons of road accidents. In this paper, we discuss our system which can be used to measure the level of alertness of the driver based on some critical physiological parameters. The system will analyze these parameters and accordingly issue an audio warning to the driver in case significant drowsiness is detected. The system is a software prototype of this system in vehicles, where images that are captured will be processed using image processing techniques and accordingly issue warning. The overall goal of the system is to reduce the number of accidents, and therefore improve the worsening road conditions.

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TweerAnalyzer: Twitter Trend Detection and Visualization

2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA)

Twitter can be identified as one of the largest social networking sites. A large number of users ... more Twitter can be identified as one of the largest social networking sites. A large number of users have accepted Twitter as a universal platform for spreading news, sharing articles and socializing with other people globally. Subsequently, such a high-volume, high-velocity surge of Twitter data generated at each second have the potential of being utilized for significant analytical and interpretation purposes. The objective of this paper is to demonstrate an easy and simple solution, called TweetAnalyzer. We propose a system to extract real-time Twitter data and to represent the trending Twitter hashtags and active users on a bar graph. TweetAnalyzer also makes use of the user's current location coordinates to represent the tweets on a world map. The proposed system can be easily deployed and used for various real-world applications such as job search, news updates, and business intelligence.

Scalability analysis of semantics based distributed document clustering algorithms

2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

Document clustering is an unsupervised learning paradigm. It provides efficient representation an... more Document clustering is an unsupervised learning paradigm. It provides efficient representation and visualization of the documents; thus helps in easy navigation. The importance of document clustering emerges from the massive volume of textual documents created. Here, semantics based distributed document clustering using Hadoop and MapReduce is proposed. Distributed version of two well-known algorithms, K-Means and Bisecting K-Means, is implemented; testing and comparison of both these algorithms is done for scalability and stability on single-node and multi-node Hadoop cluster. Also, scalability of the cluster setup and both the distributed algorithms is verified. The scalability of the algorithms is based on the Speedup, Scaleup, and Sizeup parameters.

Distributed Document Clustering Using K-Means

Document clustering, one of the traditional data mining techniques, is an unsupervised learning p... more Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent grouping of the text documents.The importance of document clustering emerges from the massive volumes of textual documents created. Also, with more and more development of information technology, data set in many domains is reaching beyond peta-scale; making it difficult to work with the document clustering algorithms in central site and leading to the need of increasing the computational requirements. The concept of distributed computing thus; is explored for document clustering giving rise to distributed document clustering. Here, we propose distributed document clustering usingHadoop and MapReduce. We implemented Kmeans and tested on single node and then modified the map, reduce functions to run over cluster of three machines. We tested on two datasets consisting of 20000 documents (20-NewsGroups) and 21578 documents (Reuters-2...

TweerAnalyzer: Twitter Trend Detection and Visualization

2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017

Document Clustering with Cluto

Scalability analysis of semantics based distributed document clustering algorithms

Mining Tool for Educational Purposes

In Current Educational System, teachers follow manual checking process for assessing the students... more In Current Educational System, teachers follow manual checking process for assessing the students' performance. They search for the key points in the answers and based on their relevance in the text, grade the students. Checking each and every student's assignment and paper manually is a very tiresome, lengthy and prolonged process. Also there are chances that they may overlook some of the points which the student might have mentioned in their answers and it may reduce the student's grade. This paper presents a mining tool to highlight the sentences that contains the relevant key terms of extracted questions and proposes its use in educational applications. The concepts of pattern matching have been employed to analyze texts and build graphs to represent the overall performance. The mining tool will help evaluate texts from qualitative perspective and ease out teachers' tasks to a great extent.

Efficient pre-processing for enhanced semantics based distributed document clustering

Document clustering, one of the traditional data mining techniques, is an unsupervised learning p... more Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent groupings of the text documents, so that a set of clusters is produced in which clusters exhibit high intra-cluster similarity and low inter-cluster similarity. The importance of document clustering emerges from the massive volumes of textual documents created. For distributed document clustering, we experimented on Hadoop and MapReduce open source platforms. We tested on two datasets consisting of 20000 (20-NewsGroup) and 21578 (Reuters) documents. We focused on efficient pre-processing wherein we applied syntactic and semantics tagging to the dataset. Here, we have suggested syntactic and semantic parsing i.e. POS tagging, and word sense disambiguation for better understanding of synonymous words and hyponymy words using WordNet. Porter's Stemming algorithm is revised and compared with traditional algorithm. We also suggest ...

Vile Parle(West), Mumbai-56

Document clustering is automatic organization of documents into clusters so that documents within... more Document clustering is automatic organization of documents into clusters so that documents within a cluster have high similarity in comparison to documents in other clusters. It has been studied intensively because of its wide applicability in various areas such as web mining, search engines, and information retrieval. It is measuring similarity between documents and grouping similar documents together. It provides efficient representation and visualization of the documents; thus helps in easy navigation also. In this paper, we have given overview of various document clustering methods studied and researched since last few years, starting from basic traditional methods to fuzzy based, genetic, coclustering, heuristic oriented etc. Also, the document clustering procedure with feature selection process, applications, challenges in document clustering, similarity measures and evaluation of document clustering algorithm is explained.

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Drowsiness Detection based on Eye Movement , Yawn Detection and Head Rotation

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AgroConsultant: Intelligent Crop Recommendation System Using Machine Learning Algorithms

2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)

Intelligent Travel Bot using Wide and Deep Learning

International Journal of Computer Sciences and Engineering

Heuristic generation of dynamic quiz game using linked data and gamfication

2016 International Conference on Inventive Computation Technologies (ICICT), 2016

3D Medical Application

Procedia Computer Science, 2015

E-Learning and Semantic Web

International Journal of e-Education, e-Business, e-Management and e-Learning, 2012

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Document Clustering: A Detailed Review

International Journal of Applied Information Systems, 2012

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Semantic based Document Clustering: A Detailed Review

International Journal of Computer Applications, 2012

Document clustering, one of the traditional data mining techniques, is an unsupervised learning p... more Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent groupings of the text documents, so that a set of clusters is produced in which clusters exhibit high intra-cluster similarity and low intercluster similarity. The importance of document clustering emerges from the massive volumes of textual documents created. Although numerous document clustering methods have been extensively studied in these years, there still exist several challenges for increasing the clustering quality. Particularly, most of the current document clustering algorithms does not consider the semantic relationships which produce unsatisfactory clustering results. Since last three-four years efforts have been seen in applying semantics to document clustering. Here, an exhaustive and detailed review of more than thirty semantic driven document clustering methods is presented. After an introduction to the document clustering and its basic requirements for improvement, traditional algorithms are overviewed. Also, semantic similarity measures are explained. The article then discusses algorithms that make semantic interpretation of documents for clustering. The semantic approach applied, datasets used, evaluation parameters applied, limitations and future work of all these approaches is presented in tabular format for easy and quick interpretation.

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Whiteboard Capture and Processing for E-Learning

International Journal of Computer Applications, 2012

This paper describes the Whiteboard capture and processing for E-Learning (WCPEL) system which ca... more This paper describes the Whiteboard capture and processing for E-Learning (WCPEL) system which captures video images, classifies the pixels into whiteboard background, pen strokes and foreground objects (e.g., people in front of the whiteboard), extracts newly written pen strokes, and corrects the color to make the whiteboard completely white. It improves captured content by correcting perspective distortion, by improving image contrast, and by compensating for changes due to light levels and for people moving through the field of view of the camera. This allows us to transmit whiteboard contents using very low bandwidth to remote meeting participants. Combined with other teleconferencing tools such as voice conference and application sharing, our system becomes a powerful tool to share ideas during online meetings. In this paper, we present a new system that combines the affordances of existing whiteboards with complementary digital tools that facilitate the retrieval, reflection removal, and use of whiteboard content long after its initial creation, whether or not it is still on the board. This system captures whiteboard content without explicit intervention from the user and stores content along with the modified video.

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Collapp- Real Time Online Code Collaborator

— Online Code Collaborator (Collapp) is web based

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Drowsiness Detection based on Eye Movement, Yawn Detection and Head Rotation

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TweerAnalyzer: Twitter Trend Detection and Visualization

2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA)

Scalability analysis of semantics based distributed document clustering algorithms

2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

Distributed Document Clustering Using K-Means

TweerAnalyzer: Twitter Trend Detection and Visualization

2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017

Document Clustering with Cluto

Scalability analysis of semantics based distributed document clustering algorithms

Mining Tool for Educational Purposes

Efficient pre-processing for enhanced semantics based distributed document clustering

Document clustering, one of the traditional data mining techniques, is an unsupervised learning p... more Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent groupings of the text documents, so that a set of clusters is produced in which clusters exhibit high intra-cluster similarity and low inter-cluster similarity. The importance of document clustering emerges from the massive volumes of textual documents created. For distributed document clustering, we experimented on Hadoop and MapReduce open source platforms. We tested on two datasets consisting of 20000 (20-NewsGroup) and 21578 (Reuters) documents. We focused on efficient pre-processing wherein we applied syntactic and semantics tagging to the dataset. Here, we have suggested syntactic and semantic parsing i.e. POS tagging, and word sense disambiguation for better understanding of synonymous words and hyponymy words using WordNet. Porter's Stemming algorithm is revised and compared with traditional algorithm. We also suggest ...

Vile Parle(West), Mumbai-56

Download

Drowsiness Detection based on Eye Movement , Yawn Detection and Head Rotation

Download

AgroConsultant: Intelligent Crop Recommendation System Using Machine Learning Algorithms

2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)

Intelligent Travel Bot using Wide and Deep Learning

International Journal of Computer Sciences and Engineering

Heuristic generation of dynamic quiz game using linked data and gamfication

2016 International Conference on Inventive Computation Technologies (ICICT), 2016

3D Medical Application

Procedia Computer Science, 2015

E-Learning and Semantic Web

International Journal of e-Education, e-Business, e-Management and e-Learning, 2012

Download

Document Clustering: A Detailed Review

International Journal of Applied Information Systems, 2012

Download

Semantic based Document Clustering: A Detailed Review

International Journal of Computer Applications, 2012

Document clustering, one of the traditional data mining techniques, is an unsupervised learning p... more Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent groupings of the text documents, so that a set of clusters is produced in which clusters exhibit high intra-cluster similarity and low intercluster similarity. The importance of document clustering emerges from the massive volumes of textual documents created. Although numerous document clustering methods have been extensively studied in these years, there still exist several challenges for increasing the clustering quality. Particularly, most of the current document clustering algorithms does not consider the semantic relationships which produce unsatisfactory clustering results. Since last three-four years efforts have been seen in applying semantics to document clustering. Here, an exhaustive and detailed review of more than thirty semantic driven document clustering methods is presented. After an introduction to the document clustering and its basic requirements for improvement, traditional algorithms are overviewed. Also, semantic similarity measures are explained. The article then discusses algorithms that make semantic interpretation of documents for clustering. The semantic approach applied, datasets used, evaluation parameters applied, limitations and future work of all these approaches is presented in tabular format for easy and quick interpretation.

Download

Whiteboard Capture and Processing for E-Learning

International Journal of Computer Applications, 2012

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