Informatics Publishing Limited

Neelesh Gupta ¹, Neetesh Gupta ², Roopam Gupta ³

Affiliations
1 Department Of Electronics and Communication, Truba Institute of Technology, Bhopal (M.P.), IN
2 Department Of Information and Technology, Technocrats Institute of Technology, Bhopal (M.P.), IN
3 Department Of Information and Technology, UIT, RGPV, Bhopal (M.P.), IN

Abstract

The Energy Consumption of a Wireless Network is a major design issue since mobile devices are often embarrassed bythe battery. In local region after appropriate selection of one coordinator among numerous nodes an enormous amount of energy can be saved in ad hoc networks. We can attain energy conservation on several static and dynamic topologies by coordinator selection  technique, then implement on MANET routing protocols during the route discovery algorithms.

Keywords

MANET, Coordinator Selection, Localized Energy Conservation, Routing Protocols.

Full Text

     

Neetesh Gupta ¹, R. K. Singh ², P. K. Dey ³

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 ITM University, Gurgaon, Haryana, IN
3 Jodhpur University (Jodhpur), IN

Abstract

With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed.. This ever increasing amount of multimedia data creates a need for new stylish methods to get back the information one is looking for. Thus content-based image retrieval attracted many researchers of various fields. Retrieval of Images from Image library using appropriate features extracted from the content of Image is currently an active research area. For the intention of content-based image retrieval (CBIR) an up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is discussed. In this paper we propose A New Approach for Image Retrieval with interactive Relevance feedback based classification by Using Kernel Based Classifier .This Approach is applied to improve retrieval performance. Our aim is to select the most informative images with respect to the query image by ranking the retrieved images. This approach uses suitable feedback to repeatedly train the Histogram Intersection Kernel based Classifier. Proposed Approach retrieves mostly informative and correlated images.

Keywords

CBIR, Relevance Feedback, Color, Texture, Shape Feature Extraction.

Full Text

     

Neetesh Gupta ¹, Vijay Anant Athavale ², MD. Ilyas Khan ¹

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 SVS Group of Institutions, Ganga Nahar, Hastinapur Road, Mawana, Merrut, IN

Abstract

Digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. While most traditional image retrieval systems perform searches using comparisons of text based strings, content based systems extract features from the content of an image to judge its similarity with another. For the purpose of image retrieval is presented in this paper. The image retrieval problem is motivated by the need to search the exponentially increasing space of image and video databases efficiently and effectively. We Extract Low level feature like as color, Texture, shape etc. and calculate similarity or dissimilarity between archieve of images. Finally we implement a user friendly Graphical system with Relevance feedback of image retrieval and finally quality assessment of similarity is evaluated.

Keywords

CBIR, Color, Texture, Shape Feature Extraction, Image Assessment, GUI, Similarity Measurement.

Full Text

     

Neetesh Gupta ¹, Niket Bhargava ², Md. Ilyas Khan ¹, Shiv Kumar ³, Bhupendra Verma ⁴

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology, Bhopal (M.P.), IN
2 Department of Computer Science, Bansal Institute of Technology, Bhopal (M.P.), IN
3 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
4 Department of Computer Science, Technocrat Institute of Technology, Bhopal (M.P.), IN

Abstract

For the purpose of content-based image retrieval (CBIR) An up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is presented in this paper. The CBIR problem is identified by us because there is a need to search the huge databases having images efficiently and effectively. in this paper we suggest a color and texture feature extraction algorithms. Special attention is given for CBIR is the similarity measurement using correlation coefficient with distinct distance matrices properties. A New approach for image retrieval technique is proposed to improve retrieval performance, and reduce the extraction search times. Matching is performed between the test image and the object image and quality of matching is measured in terms of grading.

Keywords

Similarity, Measurement, CBIR, Low Level Feature Extraction, Correlation Coefficient.

Full Text

     

Shiv Kumar ¹, Vijay K. Chaudhari ², Md. Ilyas Khan ³, Neetesh Gupta ⁴, Bupendra Verma ⁵

Affiliations
1 Technocrat Institute of Technology-Bhopal (M.P.), IN
2 Department of Information Technology at Technocrat Institute of Technology-Bhopal (M.P.), IN
3 Technocrats Institute of Technology, Bhopal, IN
4 Department of CSE/IT at Technocrat Institute of Technology-Bhopal (M.P.), IN
5 Gandhi Technical University, Bhopal(M.P.), IN

Abstract

In this paper “Genes Analysis of Data by Using Hierarchical Quality Threshold Clustering” is an approach which proposed dynamically Growing Hierarchical Self Organizing Map (DGHSOM) with Nano array to identify co-expressed genes. The DGHSOM overcomes the problem of specifying the number of clusters and total number of iteration before the processing now, we are using QT (quality threshold) clustering is a method of partitioning data, which is invented for gene clustering. It requires more computing power than k- means, but does not require specifying the number of clusters. DNA Nano array technology is a challenging area in bioinformatics research, as we have to monitor millions of genes simultaneously. The expression profile of the gene can be useful in cancer disease analysis and its diagnosis. Gene expression data is very voluminous and very difficult to analyze. Several clustering algorithm have been proposed to identify co expressed genes. The Self-organizing-maps (SOM) is a powerful tool for recognizing and classifying features in complex, micro array data. But the interpretation of co- expression of genes are heavily depends on domain knowledge and SOM lacks since the number of clusters must be determined before training.

Keywords

Gene Expression Profile, Image Processing, Dynamically Growing Self Organizing Map, Nano Array, Qt Clustering.

Full Text

     

Manish Vyas ¹, Amit Singhal ¹, Neetesh Gupta ¹

Affiliations
1 Technocrat Institute of Technology, Bhopal (M.P), IN

Abstract

Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like India where multiple languages and scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. The hybrid system gives better recognition result due to better discrimination capability of the NN. A major problem in handwriting recognition is the huge variability and distortions of patterns. Elastic models based on local observations and dynamic programming such HMM are not efficient to absorb this variability. But their vision is local. But they cannot face to length variability and they are very sensitive to distortions. Then the SVM is used to estimate global correlations and classify the pattern. Support Vector Machine (SVM) is an alternative to NN. In Handwritten recognition, SVM gives a better recognition result. The aim of this paper is to develop an approach which improve the efficiency of handwritten recognition using artificial neural network.

Keywords

Handwriting Recognition, Support Vector Machine, Neural Network.

Full Text

     

Ashish Dehariya ¹, Neetesh Gupta ¹, Bhupendra Verma ²

Affiliations
1 IT Deptt., Technocrats Institute of Technology, Bhopal, IN
2 Technocrats Institute of Technology, Bhopal, IN

Abstract

In its previous part, contribution reviews over the application of artificial neural networks to medical diagnosis and characterizes its advantages and problems in the context of the medical background. Then paradigm of neural networks is introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the optimal Back propagation algorithm approach with its flow diagram is presented. On one hand growing neural network and on the other rules based systems works for Medical diagnosis purposes. Diagnostic capabilities of artificial neural network using optimal back propagation based medical diagnosis system is less error prone than fuzzified system of medical diagnosis so it is more advantageous than fuzzification based approach. Proposed diagnosis system has the capabilities to take intelligent decision based on given symptoms of diseases takes as a input and outcomes shows the bacterial infection level so this concept can be used for the purpose of developing Efficient medical diagnosis system and definitely will help to overcome the problems of lack of Experts in Medical fields, wrong diagnosis decisions due to human experts stress also it would help to enhance healthy population amount if such a system will reach to common peoples and use by them.

Keywords

Artificial Feed Forward Neural Networks, Back Propagation, Fuzzification, Medical Diagnosis, Optimal Back Propagation.

Full Text