Search Results for author:
Found 6 papers, 1 papers with code
piSAAC: Extended notion of SAAC feature selection novel method for discrimination of Enzymes model using different machine learning algorithm
The experimental results show that, probabilistic neural network algorithm with piSAAC feature extraction yields an accuracy of 98. 01%, sensitivity of 97. 12%, specificity of 95. 87%, f-measure of 0. 9812and AUC 0. 95812, over dataset S1, accuracy of 97. 85%, sensitivity of 97. 54%, specificity of 96. 24%, f-measure of 0. 9774 and AUC 0. 9803 over dataset S2.
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
In this paper, we propose a novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network.
Divide-and-Conquer based Ensemble to Spot Emotions in Speech using MFCC and Random Forest
Firstly, emotions are detected in the input speech signal by classifying it as neutral or emotional.
On Feature based Delaunay Triangulation for Palmprint Recognition
Authentication of individuals via palmprint based biometric system is becoming very popular due to its reliability as it contains unique and stable features.
Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey
In this paper we will present a detailed overview of some of the powerful color, texture and shape descriptors for content based image retrieval.
A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition
Studies have shown that retrieving similar images based on their shape is a very effective method.
