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Dynamic video anomaly detection and localization using sparse denoising autoencoders
TLDR
A dynamic anomaly detection and localization system is proposed, which uses deep learning to automatically learn relevant features for detecting and localizing anomalies in videos. Expand
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A novel GA-ELM model for patient-specific mortality prediction over large-scale lab event data
TLDR
In this paper, a Genetic Algorithm based Wrapper Feature Selection technique is proposed for determining most-optimal lab events that contribute predominantly to mortality, even for large-scale patient cohorts. Expand
  • 15
Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes
TLDR
We propose an approach based on vector space and topic modeling, to structure the raw clinical data by capturing the semantic information in nursing notes. Expand
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An Approach for Multimodal Medical Image Retrieval using Latent Dirichlet Allocation
TLDR
We propose a Latent Dirichlet Allocation (LDA) based technique for encoding the visual features and show that these features effectively model the medical images. Expand
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Discovering composable web services using functional semantics and service dependencies based on natural language requests
TLDR
In this paper, a framework for discovering composable service sets as per user’s complex requirements is proposed. Expand
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FarSight: Long-Term Disease Prediction Using Unstructured Clinical Nursing Notes
TLDR
This paper presents significant observations from our benchmarking experiments on the applicability of deep learning models for the clinical task of ICD-9 code group prediction. Expand
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TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes
TLDR
We present a fuzzy token-based similarity approach to aggregate voluminous clinical documentations of unstructured nursing notes for the development of Clinical Decision Support Systems (CDSS). Expand
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SEMANTIC WEB SERVICES - DISCOVERY , SELECTION AND COMPOSITION TECHNIQUES
TLDR
This paper discusses the significance and importance of service discovery & selection to business logic, and the requisite current research in the various phases of the semantic web service lifecycle like discovery and selection. Expand
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Ontology-driven Text Feature Modeling for Disease Prediction using Unstructured Radiological Notes
TLDR
We use word embeddings and clinical ontologies to model the textual features of the patient data for training a feed-forward neural network for ICD9 disease group prediction. Expand
  • 2
A Multi-Space Approach to Zero-Shot Object Detection
TLDR
In this paper, we propose a novel multi-space approach to solve ZSD where we combine predictions obtained in two different search spaces. Expand
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