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Anusuya, M. A.
- Expert System Design to Predict Heart and Diabetes Diseases
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Authors
Affiliations
1 Department of Computer Science and Engineering, Sjce, Mysore, IN
1 Department of Computer Science and Engineering, Sjce, Mysore, IN
Source
International Journal of Scientific Engineering and Technology, Vol 3, No 8 (2014), Pagination: 1054-1059Abstract
The Objective of this paper is to design an expert system that predicts the heart disease and diabetes disease with reduced number of attribute using data mining technique. Classification of knowledge objects is a knowledge mining and knowledge management process used in grouping similar knowledge objects together. There are plenty of classification algorithms available in literature but decision tree is the most often used because of its ease of implementation and simpler to understand, when compared to other classification algorithms. There are many classifiers but we have used C4.5 for more accuracy and less run time. The decision tree algorithm has been applied on the knowledge of heart and diabetes disease to foretell whether diseases present or not. The Simulation result obtained from the model enables us to establish significant patterns and relationships between the medical factors and clinical factors.Keywords
Data Mining, Artificial Intelligence, Decision Tree, Heart Disease, Diabetes, Classification, C4.5 Algorithm.Full Text
- Fuzzy Clustering Algorithms-Comparative Studies for Noisy Speech Signals
Abstract Views :232 |
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Authors
Affiliations
1 Department of Information Science and Engineering, JSS Science and Technology University, IN
2 Department of Computer Science and Engineering, JSS Science and Technology University, IN
3 Department of Computer Science and Engineering, JSS Academy of Technical Education, IN
1 Department of Information Science and Engineering, JSS Science and Technology University, IN
2 Department of Computer Science and Engineering, JSS Science and Technology University, IN
3 Department of Computer Science and Engineering, JSS Academy of Technical Education, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No 3 (2019), Pagination: 1920-1926Abstract
In the area of speech signal processing and recognition, application of soft computing techniques is one of the prominent techniques for clustering the overlapping data. Kernel FCM technique is one of the efficient method to cluster the data by computing the cluster centroids. This paper presents and compares the most important clustering techniques like k-means, Fuzzy C means and Kernel Fuzzy C Means algorithms for clustering noisy speech signals. The clustering performances of these techniques are tabulated for homogeneous and heterogeneous speech data sets. This paper highlights the importance of KFCM algorithm for clustering the overlapping data. It also demonstrates the computation time and recognition accuracies of each technique. Our study identifies the KFCM technique performs better than k-means and FCM techniques.Keywords
Additive Noise, Clustering, Convolved Noise, Fuzzy C Means (FCM), Heterogeneous Data, Homogeneous Data, K-Means, Kernel Fuzzy C Means (KFCM), Principal Component Analysis (PCA), Validity Measures.
References
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- Smart Gesture using Real Time Object Tracking
Abstract Views :239 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, IN
1 Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 1 (2019), Pagination: 2044-2048Abstract
Gesture can be used to interact with the computer without any physical contact. The use of keyboard and mouse can be minimized. Gesture can be of various types. One such type is movement of hand in a particular posture. To detect these type of gestures first it must be verified that the hand is present in frame and is present in the required posture. The first one is achieved by creating a mask of the frame considering the skin color range in the HSV color space. The later part involves shape matching with some template shape. The shape matching involves computing of central moments between the mask and the template shape. The hand posture defines the start and end of gesture. All the movement of hand between start and end of gesture is tracked and gesture is recognized from the tracked data. For the purpose of recognition, Convolution Neural Network is used. An application is built on recognition. Once a gesture is recognized an event will be triggered.Keywords
Contour Detection, Shape Matching, Hue Moments, Convolution Neural Network, Event Triggering.References
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