Author
G. N. Balaji
Other affiliations: K L University, Annamalai University
Bio: G. N. Balaji is an academic researcher from CVR College of Engineering. The author has contributed to research in topics: Ventricle & Ad hoc On-Demand Distance Vector Routing. The author has an hindex of 7, co-authored 21 publications receiving 160 citations. Previous affiliations of G. N. Balaji include K L University & Annamalai University.
Papers
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TL;DR: The experimental results reveal that the proposed method can be used as an effective tool for detection of heart muscle damage or MI automatically.
Abstract: In this work, an approach for heart muscle damage detection from echocardiography sequences is proposed. To exemplify the approach, a system is presented which involves image denoising and enhancement and segmentation of the left ventricle (LV) for extracting the heart wall boundaries. Using the heart wall boundaries global LV parameters are calculated followed by statistical pattern recognition and classification to identify the heart muscle damage or myocardial ischemia (MI). The performance of this algorithm is assessed in 60 real patient data with both normal and abnormal conditions. The experimental results reveal that the proposed method can be used as an effective tool for detection of heart muscle damage or MI automatically.
40 citations
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TL;DR: A fully automatic classification of cardiac view in echocardiogram is proposed based on a machine learning approach which characterizes two features 1) Histogram features and 2) Statistical features.
38 citations
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01 Jan 2018TL;DR: An attempt has been made and a system which involves five image processing steps namely, denoising using high boost filter, enhancement using adaptive histogram equalization, statistical feature extraction, and classification using artificial neural network can be used as an effective tool for X-ray image classification.
Abstract: In recent days, computer-aided fracture detection system plays a role in aiding both orthopaedician and a radiologist by providing accurate and fast results. In order to detect the fracture automatically, classification of X-ray images should be automated and it becomes the initial step. Therefore, an attempt has been made and a system is presented in this paper, which involves five image processing steps namely, denoising using high boost filter, enhancement using adaptive histogram equalization, statistical feature extraction, and classification using artificial neural network. To classify the given input X-ray images into the categories head, neck, skull, foot, palm, and spine, the probabilistic neural network, backpropagation neural network, and support vector machine classifiers are employed in classifying X-ray images. The results ascertain an overall accuracy of 92.3% in classifying X-ray images and the presented system can be used as an effective tool for X-ray image classification.
25 citations
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23 citations
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TL;DR: In this paper, a logistic regression based machine learning approach is utilized to detect credit card fraud, which can be effectively used for fraud investigators, and the results show that the approach outperformed with the highest accuracy.
Abstract: With the extensive use of credit cards, fraud appears as a major issue in the credit card business. It is hard to have some figures on the impact of fraud, since companies and banks do not like to disclose the amount of losses due to frauds. At the same time, public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. Another problem in creditcard fraud loss estimation is that we can measure the loss of only those frauds that have been detected, and it is not possible to assess the size of unreported/undetected frauds. Fraud patterns are changing rapidly where fraud detection needs to be re-evaluated from a reactive to a proactive approach. In recent years, machine learning has gained lot of popularity in image analysis, natural language processing and speech recognition. In this regard, implementation of efficient fraud detection algorithms using machine-learning techniques is key for reducing these losses, and to assist fraud investigators. In this paper logistic regression, based machine learning approach is utilized to detect credit card fraud. The results show logistic regression based approaches outperforms with the highest accuracy and it can be effectively used for fraud investigators.
18 citations
Cited by
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09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.
2,069 citations
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01 Jan 1982
TL;DR: "Graefe's Archive" is a distinguished international journal that presents original clinical reports and clinically relevant experimental studies and provides rapid dissemination of clinical and clinically related experimental information.
Abstract: "Graefe's Archive" is a distinguished international journal that presents original clinical reports and clinically relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, "Graefe's Archive" provides rapid dissemination of clinical and clinically related experimental information.
750 citations
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TL;DR: A deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber echo, which has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment.
Abstract: Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert’s manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.
82 citations
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TL;DR: Preliminary experience with fracture detection and classification using AI shows promising performance, and AI may enhance processing and communicating probabilistic tasks in medicine, including orthopaedic surgery.
Abstract: BackgroundArtificial-intelligence algorithms derive rules and patterns from large amounts of data to calculate the probabilities of various outcomes using new sets of similar data. In medicine, artificial intelligence (AI) has been applied primarily to image-recognition diagnostic tasks and
75 citations
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TL;DR: An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters and gives a better performance than PSO and DE in terms of time efficiency.
Abstract: As one of the major problems is that the time taken for executing the traditional algorithm is larger and that it is very difficult for processing large amount of data. Clusters possess high degree of similarity among each cluster and have low degree of similarity among other clusters. Optimization algorithm for clustering is the art of allocating scarce resources to the best possible effect. The traditional optimization algorithm is not suitable for processing high dimensional data. The main objective of proposed Artificial Bee Colony (ABC) approach is to minimize the execution time and to optimize the best cluster for the various sizes of the dataset. To deal with this, we are normalizing to distributed environment for time efficiency and accuracy. The proposed ABC algorithm simulates the behavior of real bees for solving numerical optimization problems particularly in clustering. The dataset size is varied for the algorithm and is mapped with its appropriate timings. The result is observed for various fitness and probability value which is obtained from the employed and the onlooker phase of ABC algorithm from which the further calibrations of classification error percentage is done. The proposed ABC Algorithm is implemented in Hadoop environment using mapper and reducer programming. An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters. The results show that the proposed ABC scheme gives a better performance than PSO and DE in terms of time efficiency.
67 citations