2. Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/7
Browse
57 results
Search Results
Item Resource aware scheduling in Hadoop for heterogeneous workloads based on load estimation(2013) Kapil, B.S.; Sowmya, Kamath S.Currently, most cloud based applications require large scale data processing capability. Data to be processed is growing at a rate much faster than available computing power. Hadoop is used to enable distributed processing on large clusters of commodity hardware. In large clusters, the workloads may be heterogeneous in nature, that is, I/O bound, CPU bound or network intensive jobs that demand different types of resources requirement so as to run simultaneously on large cluster. Hadoops job scheduling is based on FIFO where, parallelization based on types of job has not been taken into account for scheduling. In this paper, we propose a new scheduling algorithm for Hadoop based distributed system, based on the classification of workloads to assign a specific category to a particular cluster according to current load of the cluster. The proposed scheduler increases the performance of both CPU and I/O resources in a cluster under heterogeneous workloads, by approximately 12% when compared to Hadoops FIFO scheduler. � 2013 IEEE.Item Query-oriented unsupervised multi-document summarization on big data(2016) Sunaina; Sowmya, Kamath S.Real time document summarization is a critical need nowadays, owing to the large volume of information available for our reading, and our inability to deal with this entirely due to limitations of time and resources. Oftentimes, information is available in multiple sources, offering multiple contexts and viewpoints on a single topic of interest. Automated multi-document summarization (MDS) techniques aim to address this problem. However, current techniques for automated MDS suffer from low precision and accuracy with reference to a given subject matter, when compared to those summaries prepared by humans and takes large time to create the summary when the input given is too huge. In this paper, we propose a hybrid MDS technique combining feature based algorithms and dynamic programming for generating a summary from multiple documents based on user provided query. Further, in real-world scenarios, Web search serves up a large number of URLs to users, and the work of making sense of these with reference to a particular query is left to the user. In this context, an efficient parallelized MDS technique based on Hadoop is also presented, for serving a concise summary of multiple Webpage contents for a given user query in reduced time duration. � 2016 ACM.Item Performance evaluation of web browsers in Android(2013) Harsha, Prabha, E.; Piraviperumal, D.; Naik, D.; Sowmya, Kamath S.; Prasad, G.In this day and age, smart phones are fast becoming ubiquitous. They have evolved from their traditional use of solely being a device for communication between people, to a multipurpose device. With the advent of Android smart phones, the number of people accessing the Internet through their mobile phones is on a steep rise. Hence, web browsers play a major role in providing a highly enjoyable browsing experience for its users. As such, the objective of this paper is to analyze the performance of five major mobile web browsers available in the Android platform. In this paper, we present the results of a study conducted based on several parameters that assess these mobile browsers' functionalities. Based on this evaluation, we also propose the best among these browsers to further enrich user experience of mobile web browsing along with utmost performance. � 2013 Springer Science+Business Media New York.Item Ontology based approach for event detection in twitter datastreams(2015) Kaushik, R.; Apoorva, Chandra, S.; Mallya, D.; Chaitanya, J.N.V.K.; Sowmya, Kamath S.In this paper, we present a system that attempts to interpret relations in social media data based on automatically constructed dataset-specific ontology. Twitter data pertaining to the real world events such as the launch of products and the buzz generated by it, among the users of Twitter for developing a prototype of the system. Twitter data is filtered using certain tag-words which are used to build an ontology, based on extracted entities. Wikipedia data on the entities are collected and processed semantically to retrieve inherent relations and properties. The system uses these results to discover related entities and the relationships between them. We present the results of experiments to show how the system was able to effectively construct the ontology and discover inherent relationships between the entities belonging to two different datasets. � 2015 IEEE.Item Ontology based algorithms for indexing and search of semantically close natural language phrases(2007) Sowmya, Kamath S.Free text constitutes a overwhelming fraction of information available on the World Wide Web. Specifically, consider small chunks of natural language phrases frequently used by Web users to describe stuff relevant to them. For example, consider the following two posts on a classifieds site (which serves a small locality, say, a university campus) - "2 Tickets for the prom tonight" and "Trade 2 extra passes for tonight's Ball for $25". For a human looking at these two posts, its trivial to conclude that he has found what he wanted. But when there are thousands of such posts and in the absence of any common keywords or any additional information from the user it is unlikely that naive keyword based matching will be of any help in reflecting the glaring similarity between these descriptions. This problem is very relevant and challenging because users tend to describe the same item in several dif ferent ways. Humans frequently use their commonsense and background knowledge to infer that these relate to the same item. However the enormous sizes of most datasets prohibit manual classification. To automate this, we present intuitive and scalable algorithms which use existing Ontologies like WordNet to correctly relate semantically close descriptions.Item Novel hybrid feature selection models for unsupervised document categorization(2017) Bhopale, A.P.; Sowmya, Kamath S.Dealing with high dimensional data is a challenging and computationally complex task in the data pre-processing phase of text clustering. Conventionally, union and intersection approaches have been used to combine results of different feature selection methods to optimize relevant feature space for document collection. Union method selects all features from considered sub-models, whereas, intersection method selects only common features identified by sub-models. However, in reality, any type of feature selection can cause a loss of some potentially important features. In this paper, a hybrid feature selection model called Modified Hybrid Union (MHU) is proposed, which selects features by considering the individual strengths and weaknesses of each constituent component of the model. A comparative evaluation of its performance for K-means clustering and Bio-inspired Flockbased clustering is also presented on standard data sets such as OWL-S TC and Reuters-21578. � 2017 IEEE.Item Medical Image Retrieval Using Manifold Ranking with Relevance Feedback(2018) Soundalgekar, P.; Kulkarni, M.; Nagaraju, D.; Sowmya, Kamath S.Medical image retrieval (MedIR) is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content. Traditional models do not take the intrinsic characteristics of data into consideration and have achieved limited accuracy in application to medical images. Manifold Ranking (MR) is a technique that can be used in further optimizing precision and recall in MedIR applications as it ranks items by traversing a dynamically constructed content-specific information graph. In this paper, a MedIR approach based on Manifold Ranking is proposed. Medical images being multi-dimensional, exhibit underlying cluster and manifold information which enhances semantic relevance and allows for label uniformity. Hence, when adapted for MedIR, MR can help in achieving large-scale ranking across datasets as is the case in most medical imaging applications. In addition, a relevance feedback mechanism was also incorporated to support a learning based system. We show that MR achieved significant improvement in retrieval results with relevance feedback as compared to the Euclidean Distance (ED) rankings. This showcases the importance of analyzing the inherent latent structure in medical image data for better performance over traditional methods. � 2018 IEEE.Item NLP based intelligent news search engine using information extraction from e-newspapers(2015) Kanakaraj, M.; Sowmya, Kamath S.Extracting text information from a web news page is a challenging task as most of the E-News content is provided with support from backend Content Management Systems (CMSs). In this paper, we present a personalized news search engine that focuses on building a repository of news articles by applying efficient extraction of text information from a web news page from varied e-news portals. The system is based on the concept of Document Object Model(DOM) tree manipulation for extracting text and modifying the web page structure to exclude irrelevant content like ads and user comments. We also use WordNet, a thesaurus of English language based on psycholinguist studies for matching the extracted content semantically to the title of the web page. TF-IDF (Term Frequency Inverse Document Frequency) is used for identifying the web page blocks carrying information relevant to the pages title. In addition to the extraction of information, functionalities to gather related information from different web news papers and to summarize the gathered information based on user preferences have also been included. We observed that the system was able to achieve good recall and high precision for both generalized and specific queries. � 2014 IEEE.Item Machine learning for mobile wound assessment(2018) Sowmya, Kamath S.; Sirazitdinova, E.; Deserno, T.M.Chronic wounds affect millions of people around the world. In particular, elderly persons in home care may develop decubitus. Here, mobile image acquisition and analysis can provide a good assistance. We develop a system for mobile wound capture using mobile devices such as smartphones. The photographs are acquired with the integrated camera of the device and then calibrated and processed to determine the size of various tissues that are present in a wound, i.e., necrotic, sloughy, and granular tissue. The random forest classifier based on various color and texture features is used for that. These features are Sobel, Hessian, membrane projections, variance, mean, median, anisotropic diffusion, and bilateral as well as Kuwahara filters. The resultant probability output is thresholded using the Otsu technique. The similarity between manual ground truth labeling and the classification is measured. The acquired results are compared to those achieved with a basic technique of color thresholding, as well as those produced by the SVM classifier. The fast random forest was found to produce better results. It is also seen to have a superior performance when the method is applied only to the wound regions having the background subtracted. Mean similarity is 0.89, 0.39, and 0.44 for necrotic, sloughy, and granular tissue, respectively. Although the training phase is time consuming, the trained classifier performs fast enough to be implemented on the mobile device. This will allow comprehensive monitoring of skin lesions and wounds. � 2018 SPIE.Item Jamura: A Conversational Smart Home Assistant Built on Telegram and Google Dialogflow(2019) Salvi, S.; Geetha, V.; Sowmya, Kamath S.With an ever-increasing number of smart connected devices for various applications, there is a need for finding a new, smarter way of communicating with all the homogeneous and heterogeneous devices in a particular network. Conversational Bots, also known as Chatbots, are currently a popular solution in many applications, as they provide a user-friendly interface and more intuitive recommendations to user queries. In this work, the domain of home automation is considered from the area of the Internet of Things, and a Chatbot application built using technologies like Natural Language Processing, Machine Learning, and Service-Oriented Computing is designed as an intuitive user-interface for Smart home products. The aim of this paper is to build easy to implement and integrate DIY Smart Home Assistant using available technologies. The proposed Conversational Artificial Intelligence system can aid the user in smart decision making, predictive and preventive analytics, and showed promising results during experimental evaluation. � 2019 IEEE.