ramesh sahoo - Academia.edu (original) (raw)

Papers by ramesh sahoo

Multimedia Tools and Applications

Ontology represents relationships among set of terms and concepts in hierarchical fashion. Ontolo... more Ontology represents relationships among set of terms and concepts in hierarchical fashion. Ontology plays crucial role in formulization of information related to given domain. Understanding these ontologies without having sufficient knowledge of ontology editors is like working on project without knowing its requirements. Traditional text mining methods and aero-text systems for extracting key phrases have been used but it needs to be improved to support large scale ontology constitution for real world applications. An ample amount of documents present on web puts the users in state of dilemma. Relevance means how closely the given query matches large number of documents. The paper proposed fuzzy ontology based approach that retrieves information from web documents by using fuzzy relations and semantic context vectors. It discovers fuzzy ontology rather than textual descriptive ontology with crisp features only. The output membership fuzzy functions are produced by simulation tool n...

In Mobile ad hoc network (MANET) all nodes are battery operated, as battery power or batter energ... more In Mobile ad hoc network (MANET) all nodes are battery operated, as battery power or batter energy is limited resource therefore it requires special attention to minimize energy consumption in MANET. For MANETs, optimization of energy consumption has greater impact as it directly corresponds to lifetime of networks. In WSN, sensor nodes have a limited storage capacity and limited transmission range. Maintenance of routes in wireless sensor network is the responsibility of the routing protocols. In this paper we discuss about the various routing protocols and study their behavior in MANET.

A deep neural network such as convolutional neural network is a popular and most commonly applied... more A deep neural network such as convolutional neural network is a popular and most commonly applied technique in image processing for classification for the last few years. The overhead of the feature extraction step will be avoided due to the implicit feature extraction nature of convolutional neural network (CNN) and these extracted features contain substantial information that could be sufficient for an image classification problem. Fully connected (FC) layers in CNN take the results of the last convolution and/or pooling layer and then use them to recognize or classifying images into labels. In this paper, we present an associative memory-based model named Hopfield network as a fully connected layer to store patterns for classification in CNN architecture like LeNet-5. The main purpose of using Hopfield network is to avoid backpropagation as it is a fully connected recurrent network as the state-of-art results which we have obtained are comparable with other models. To measure the...

Auto-associative neural network is a well-known memory model for pattern storage and recall in th... more Auto-associative neural network is a well-known memory model for pattern storage and recall in the field of pattern recognition. In this paper, we present analysis of pattern storage capacity and recall efficiency for handwritten Odia characters with a recurrent auto-associative neural network named Hopfield network using histogram of oriented gradients (HOG) features. Correct and successful recalling of original stored patterns, noisy version of patterns and new patterns (not used in training) of each class are analysed in terms of recalling efficiency. Detail description of our experiment with NIT Rourkela Odia handwritten dataset is demonstrated in this paper, and the state-of-the-art result is remarkable and comparable with other proposed methods.

International Journal of Computer Applications, 2016

This paper analyzes the Hopfield neural network for storage and recall of fingerprint images. The... more This paper analyzes the Hopfield neural network for storage and recall of fingerprint images. The paper first discusses the storage and recall via hebbian learning rule and then the performance enhancement via the pseudo-inverse learning rule. Performance is measured with respect to storage capacity; recall of distorted or noisy patterns. Here we test the accretive behavior of the Hopfield neural network.

Wireless Personal Communications

International Journal of Advanced Computer Science and Applications

Automatic character recognition is one of the challenging fields in pattern recognition especiall... more Automatic character recognition is one of the challenging fields in pattern recognition especially for handwritten Odia characters as many of these characters are similar and rounded in shape. In this paper, a comparative performance analysis of Hopfield neural network for storing and recalling of handwritten and printed Odia characters with three different learning rules such as Hebbian, Pseudo-inverse and Storkey learning rule has been presented. An experimental exploration of these three learning rules in Hopfield network has been performed in two different ways to measure the performance of the network to corrupted patterns. In the first experimental work, an attempt has been proposed to demonstrate the performance of storing and recalling of Odia characters (vowels and consonants) in image form of size 30 X 30 on Hopfield network with different noise percentages. At the same time, the performance of recognition accuracy has been observed by partitioning the dataset into training and a different testing dataset with k-fold cross-validation method in the second experimental attempt. The simulation results obtained in this study express the comparative performance of the network for recalling of stored patterns and recognizing a new set of testing patterns with various noise percentages for different learning rules.

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY

This paper focused on the study of various feature extraction techniques applied for fingerprint ... more This paper focused on the study of various feature extraction techniques applied for fingerprint identification, verification and classification as it is the most important step for image processing. Feature extraction techniques are classified into local (low level) and global (high level) features. Global features such as arch, loop, delta and whorl where as local features such as ridge end and bifurcation called minutiae are majors of automatic fingerprint recognition system. In this study, it has been observed that most of the fingerprint recognition systems are based on minutiae features. In this paper we analyze the various feature extraction methods used so far with their mathematical background to the readers.