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Radha, P.
- Multi-Resolution Efficient Photography Image Fusion Based on Gradient Exposure
Authors
1 LRG Government College for Women, Tirupur, IN
2 Department of Computer Science, LRG Govt. Arts College for Women, Tirupur, IN
Source
Digital Image Processing, Vol 6, No 6 (2014), Pagination: 282-285Abstract
A class of Image fusion techniques are automatically combined under different exposure level.image fusion approach is based on a gradient technique that conserves important local perceptual signals. The images that are reconstructed from integration and the gradients attain a smooth merge of the input images and at the same time possess its important features. In the proposed system the series of images are captured by digital camera which has bracketed features (Long Exposure and Short Exposure) and uses a Standard Dynamic Range (SDR) device and synthesizes an image suitable for SDR displays. The SDR device traces scene details like contrasts and gradient direction in a sequence of SDR images with dissimilar coverage levels. The depth of field is first calculated, which helps to find the distance between the nearest and farthest objects in a scene which appears sharp in an images. The scene gradient measure, luminance measure is carried out in order to measure the gradient and the contrast of image and last step is integrating the results to get the fusion result. The fusion algorithm techniques are used for fusion of images based on contrast and gradient level. This is done in a multi-resolution of brightness increase variation in the sequence. The gradient field is then integrated within dynamic range image. Experimental results prove that the proposed scheme does not need any human interaction or limit tuning for different scenes.
Keywords
Dynamic Range, Gradient, Image Fusion, Multi-Resolution.
- Classifying the Depression Data Polynomial Discriminant Vectors
Authors
1 Department of Computer Science and Engineering, Alagappa University, Karaikudi, IN
2 Computer Center, Alagappa University, Karaikudi, IN
3 Udaya School of Engineering, 629204, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 4 (2012), Pagination: 212-217Abstract
This paper discusses the preprocessing and classification of depression data using back propagation algorithm (BPA). In general, input vectors will not be orthogonal to each other. The proposed method of preprocessing the input vector makes possible BPA learn the input vectors. The classification performance of BPA have been shown for a minimum 80%.Keywords
Depression Data, Back Propagation Algorithm, Polynomial Discriminant Vector (PDV).- Stress Management
Authors
1 Management Studies, RVS College of Engineering & Technology, Kannampalayam, Coimbatore-641 402, IN
Source
Asian Journal of Management, Vol 2, No 4 (2011), Pagination: 202-203Abstract
Stress is a term basically used in physical sciences, which means pressure of one object on another. From physical sciences, the term stress came to medical sciences and finally to social science. As per medical explanation, the term stress is the body’s general responses to environmental situations. In today’s life everyone is striving to manage stress. Stress Management has become a hot topic for each and every human being in any profession1.- Environmental Stress in Banking Sector
Authors
1 RVS-IMS, Kumaran Kottam Campus, Kannampalayam, Coimbatore 641 402, IN
Source
Asian Journal of Management, Vol 3, No 1 (2012), Pagination: 14-17Abstract
Stress management is an essential step for one has to take is facing stressful situations in their life, regardless of the cause. Although there are helpful types of stress that enables them to cater this added burst of energy into something positive and productive, it is not recommended for the body. Long term stress can specifically produce negative impacts on the health and is recognized to deteriorate your health faster than some other diseases.- A Method to Detection of Prostate Cancer and Treatments
Authors
1 Department of Information Technology, Government Arts College, Coimbatore, IN
Source
Automation and Autonomous Systems, Vol 10, No 3 (2018), Pagination: 41-49Abstract
Data mining refers to the extracting or mining knowledge from large amounts of data. Classification according to kinds of database mind. Classification is a two step process only, using all fields. In spite of increased prostate cancer patients, little is known about impact of treatments for prostate cancer begins when healthy cells in the prostate change and grow out of control forming a tumor. Here our proposed method works on finding the correct stages of prostate cancer so the best treatment can be given to the patients accordingly. Here, the existing system c4.5 algorithm has been simply applied on synthesized prostate cancer datasets. However, main drawback of this existing algorithm is that the discovery of interesting or useful rules. More over the number of rules less. So, here try to develop a new method by capturing the important attributes influence to get more accurate result. Here integrate the k-means algorithm and apriori algorithm with the c4.5 algorithm. Due to dealing with the large amount of database, a variety of decision tree classification algorithm has been considered. The advantages of c4.5 decision tree algorithm is significantly, so it can be choose.
Keywords
Apriori Algorithm, C 4.5 Algorithm, Decision Tree Algorithm, K-Means Algorithm, Mat Lab, Prostate Cancer Datasets, Prostate Cancer Datasets PSA (Prostate-Specific Antigen).- An Efficient Approach for the Classification of Medicinal Leaves using BFO and FRVM
Authors
1 Dept. of Electronics and Instrumentation Engg. National Engineering College, Kovilpatti, Tamil Nadu -628 503, IN
2 Dept. of Bio medical Engineering Saveetha Engineering College, Thandalam Chennai – 602 105, IN
3 Dept. of Electronics and Instrumentation Engg., National Engineering College, Kovilpatti, Tamil Nadu -628 503, IN
4 Siddha Clinical Research Unit (SCRU) Government Siddha Medical College Campus, Palayamkottai, Tamil Nadu-627002, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No 6 (2019), Pagination: 4105-4112Abstract
Herbal plants have been used for medicinal purposes since the ages. These plants also play a major role in medicines, food, perfumes and cosmetics. At present, the identification of herbal plants is purely based on the human perception of their knowledge. It may be probability of human error occurring. An efficient herb species classification system should be automatic and a convenient recognition of herbal plants which reduces the human error. The present research aims to predict the herbal plants in a very convenient and accurate way. This approach is based on the leaf shape, texture, color and its feature. Bacteria Foraging Optimization (BFO) for feature selection and Fuzzy Relevance Vector Machine (FRVM) for the classification of herbal plants are used in the proposed system. The data required for classification are computed using the MATLAB software. In the present work, ten different types of herbal leaves and twenty samples of each have been considered for the process and the classification accuracy is achieved as maximum with an efficient intelligence technique. The efficiency of the proposed method of classifying the different herbal plants gives better performance.Keywords
Detection, GLCM Texture Feature Extraction, BFO, FRVM Classifier.Full Text
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