Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A QoS and QoE based integrated model for bidirectional web service recommendation(Institute of Electrical and Electronics Engineers Inc., 2018) Jhaveri, S.; Soundalgekar, P.M.; George, K.; Kamath S․, S.K.For a given requirement, identifying relevant Web services and recommending the best ones is an important task in Service-oriented application development. In this paper, a composite model that leverages Quality of Service (QoS) and Quality of Experience (QoE) for bidirectional Web service recommendation (bi-WSR) is proposed. The QoS based recommendation model is built on degree of user satisfaction, calculated using a special normalization technique and user satisfaction functions like response time and throughput. The QoE model is trained on a dataset containing positive, negative and neutral textual reviews of web services for sentiment analysis and mapped to each service's QoS values using a clustering method. This further optimizes the recommendation of web services to consumers, as the sentiment score of reviews is integrated with the user satisfaction using weighted average scoring. To describe the relationship between both web services consumers and providers consumers, a cube model is built. For recommending services to consumers and recommending potential consumers to service providers, hybrid collaborative filtering based techniques were used. The results obtained when only QoS is used, and when QoS and sentiment analysis scores are integrated to form QoE showed significant improvement in the quality of recommendation. © 2018 Pacific Neighborhood Consortium (PNC).Item A robust approach to open vocabulary image retrieval with deep convolutional neural networks and transfer learning(Institute of Electrical and Electronics Engineers Inc., 2018) Padmakumar, V.; Ranga, R.; Elluru, S.; Kamath S․, S.K.Enabling computer systems to respond to conversational human language is a challenging problem with wideranging applications in the field of robotics and human computer interaction. Specifically, in image searches, humans tend to describe objects in fine-grained detail like color or company, for which conventional retrieval algorithms have shown poor performance. In this paper, a novel approach for open vocabulary image retrieval, capable of selecting the correct candidate image from among a set of distractions given a query in natural language form, is presented. Our methodology focuses on generating a robust set of image-text projections capable of accurately representing any image, with an objective of achieving high recall. To this end, an ensemble of classifiers is trained on ImageNet for representing high-resolution objects, Cifar 100 for smaller resolution images of objects and Caltech 256 for challenging views of everyday objects, for generating category-based projections. In addition to category based projections, we also make use of an image captioning model trained on MS COCO and Google Image Search (GISS) to capture additional semantic/latent information about the candidate images. To facilitate image retrieval, the natural language query and projection results are converted to a common vector representation using word embeddings, with which query-image similarity is computed. The proposed model when benchmarked on the RefCoco dataset, achieved an accuracy of 68.8%, while retrieving semantically meaningful candidate images. © 2018 Pacific Neighborhood Consortium (PNC).Item A Supervised Approach for Patient-Specific ICU Mortality Prediction Using Feature Modeling(Springer Verlag service@springer.de, 2019) S. Krishnan, G.S.; Kamath S․, S.K.Intensive Care Units (ICUs) are one of the most essential, but expensive healthcare services provided in hospitals. Modern monitoring machines in critical care units continuously generate huge amount of data, which can be used for intelligent decision-making. Prediction of mortality risk of patients is one such predictive analytics application, which can assist hospitals and healthcare personnel in making informed decisions. Traditional scoring systems currently in use are parametric scoring methods which often suffer from low accuracy. In this paper, an empirical study on the effect of feature selection on the feature set of traditional scoring methods for modeling an optimal feature set to represent each patient’s profile along with a supervised learning approach for ICU mortality prediction have been presented. Experimental evaluation of the proposed approach in comparison to standard severity scores like SAPS-II, SOFA and OASIS showed that the proposed model outperformed them by a margin of 12–16% in terms of prediction accuracy. © 2019, Springer Nature Singapore Pte Ltd.Item TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes(Springer Verlag service@springer.de, 2019) Gangavarapu, T.; Jayasimha, A.; S. Krishnan, G.S.; Kamath S․, S.K.Accurate risk management and disease prediction are vital in intensive care units to channel prompt care to patients in critical conditions and aid medical personnel in effective decision making. Clinical nursing notes document subjective assessments and crucial information of a patient’s state, which is mostly lost when transcribed into Electronic Medical Records (EMRs). The Clinical Decision Support Systems (CDSSs) in the existing body of literature are heavily dependent on the structured nature of EMRs. Moreover, works which aim at benchmarking deep learning models are limited. In this paper, we aim at leveraging the underutilized treasure-trove of patient-specific information present in the unstructured clinical nursing notes towards the development of CDSSs. We present a fuzzy token-based similarity approach to aggregate voluminous clinical documentations of a patient. To structure the free-text in the unstructured notes, vector space and coherence-based topic modeling approaches that capture the syntactic and latent semantic information are presented. Furthermore, we utilize the predictive capabilities of deep neural architectures for disease prediction as ICD-9 code group. Experimental validation revealed that the proposed Term weighting of nursing notes AGgregated using Similarity (TAGS) model outperformed the state-of-the-art model by 5% in AUPRC and 1.55% in AUROC. © 2019, Springer Nature Switzerland AG.