Published in Drug information journal : DIJ / Drug Information Association
The adoption of the Medical Dictionary for Regulatory Activities (MedDRA) as the standardized international medical terminology presents industry with the enormous task of “MedDRA-sizing” legacy data. As a multifunctional, highly granular dictionary, MedDRA has the potential to facilitate the conversion process with minimal loss of specificity, and...
Published in Complex & Intelligent Systems
A unique technique is proposed based on sparse-autoencoders for automated fault detection and classification using the acoustic signal generated from internal combustion (IC) engines. This technique does not require any hand-engineered feature extraction and feature selection from acoustic data for fault detection and classification, as usually don...
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dime...
Published in Multimedia Tools and Applications
Three-Dimensional image-based human pose recovery tries to retrieves 3D poses with 2D image. Therefore, one of the key problem is how to represent 2D images. However, semantic gap exists for current feature extractors, which limits recovery performance. In this paper, we propose a novel feature extractor with deep neural network. It is based on den...
Published in Neural Computing and Applications
Tailoring the muckpile shape and its fragmentation to the requirements of the excavating equipment in surface mines can significantly improve the efficiency and savings through increased production, machine life and reduced maintenance. Considering the various blast parameters together to predict the throw is subtle and can lead to wrong conclusion...
For many real-world applications, labeled data can be costly to obtain. Semi-supervised learning methods make use of substantially available unlabeled data along with few labeled samples. Most of the latest work on semi-supervised learning for image classification show performance on standard machine learning datasets like MNIST, SVHN, etc. In this...
Published in Neuroscience and biobehavioral reviews
Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition. DL differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecu...
Guiding a representation towards capturing temporally coherent aspects present invideo improves object identity encoding. Existing models apply temporal coherenceuniformly over all features based on the assumption that optimal encoding of objectidentity only requires temporally stable components. We test the validity of this assumptionby exploring ...
Published in Neural Computing and Applications
Managing colossal image datasets with large dimensional hand-crafted features is no more feasible in most of the cases. Content based image classification (CBIC) of these large image datasets calls for the need of dimensionality reduction of features extracted for the purpose. This paper identifies the escalating challenges in the discussed domain ...
