361 research outputs found
Content Based Document Recommender using Deep Learning
With the recent advancements in information technology there has been a huge
surge in amount of data available. But information retrieval technology has not
been able to keep up with this pace of information generation resulting in over
spending of time for retrieving relevant information. Even though systems exist
for assisting users to search a database along with filtering and recommending
relevant information, but recommendation system which uses content of documents
for recommendation still have a long way to mature. Here we present a Deep
Learning based supervised approach to recommend similar documents based on the
similarity of content. We combine the C-DSSM model with Word2Vec distributed
representations of words to create a novel model to classify a document pair as
relevant/irrelavant by assigning a score to it. Using our model retrieval of
documents can be done in O(1) time and the memory complexity is O(n), where n
is number of documents.Comment: Accepted in ICICI 2017, Coimbatore, Indi
Image Captioning and Classification of Dangerous Situations
Current robot platforms are being employed to collaborate with humans in a
wide range of domestic and industrial tasks. These environments require
autonomous systems that are able to classify and communicate anomalous
situations such as fires, injured persons, car accidents; or generally, any
potentially dangerous situation for humans. In this paper we introduce an
anomaly detection dataset for the purpose of robot applications as well as the
design and implementation of a deep learning architecture that classifies and
describes dangerous situations using only a single image as input. We report a
classification accuracy of 97 % and METEOR score of 16.2. We will make the
dataset publicly available after this paper is accepted
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