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Sudhir Sawarkar

Bio: Sudhir Sawarkar is an academic researcher from Datta Meghe College of Engineering. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 5, co-authored 41 publications receiving 107 citations.

Papers
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12 Jun 2006
TL;DR: The proposed neural network model hold promise for radiologists, surgeons, and patients with information, which was previously available only through biopsy, thus substantially reducing the number of unnecessary surgical procedures.
Abstract: An artificial neural network (ANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information The key element of the ANN paradigm is the novel structure of the information processing system Learning in ANN typically occurs by example through training, or exposure to a set of input/output data where the training algorithm iteratively adjusts the connection weights (synapses) These connection weights store the knowledge necessary to solve specific problems In this work, we have used neural networks Support Vector Machine method for diagnosis of breast cancer SVMs can only be used for classification, not for function approximation The Support Vector Machine (SVM) is implemented using the kernel Adatron algorithm The kernel Adatron maps inputs to a high-dimensional feature space, and then optimally separates data into their respective classes by isolating those inputs, which fall close to the data boundariesThe proposed neural network model hold promise for radiologists, surgeons, and patients with information, which was previously available only through biopsy, thus substantially reducing the number of unnecessary surgical procedures For training and testing the neural network various databases available on the Internet as well as gathered information from hospitals is used

36 citations

01 Jan 2006
TL;DR: In this paper, the authors used neural networks Support Vector Machine (SVM) method for diagnosis of breast cancer, which can only be used for classification, not for function approximation, and implemented using the kernel Adatron algorithm.
Abstract: An artificial neural network (ANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information. The key element of the ANN paradigm is the novel structure of the information processing system. Learning in ANN typically occurs by example through training, or exposure to a set of input/output data where the training algorithm iteratively adjusts the connection weights (synapses). These connection weights store the knowledge necessary to solve specific problems. In this work, we have used neural networks Support Vector Machine method for diagnosis of breast cancer. SVMs can only be used for classification, not for function approximation. The Support Vector Machine (SVM) is implemented using the kernel Adatron algorithm. The kernel Adatron maps inputs to a high-dimensional feature space, and then optimally separates data into their respective classes by isolating those inputs, which fall close to the data boundaries.The proposed neural network model hold promise for radiologists, surgeons, and patients with information, which was previously available only through biopsy, thus substantially reducing the number of unnecessary surgical procedures. For training and testing the neural network various databases available on the Internet as well as gathered information from hospitals is used.

17 citations

Proceedings ArticleDOI
06 Mar 2009
TL;DR: A novel fingerprint representation scheme that relies on describing the orientation field of the fingerprint pattern with respect to each minutia detail is presented and developed and tested with a series of experiments conducted on collections of fingerprint images.
Abstract: This paper presents a reliable method of computation for minutiae feature extraction from fingerprint images. We present a novel fingerprint representation scheme that relies on describing the orientation field of the fingerprint pattern with respect to each minutia detail. A fingerprint image is treated as a textured image. Improved algorithms for enhancement of fingerprint images, which have the adaptive normalization based on block processing, are proposed. An orientation flow field of the ridges is computed for the fingerprint image. To accurately locate ridges, a ridge orientation based computation method is used. After ridge segmentation a method of computation is used for smoothing the ridges. The ridge skeleton image is obtained and then smoothed using morphological operators to detect the features. A post processing stage eliminates a large number of false features from the detected set of minutiae features. A fingerprint matching algorithm, based on the proposed representation, is developed and tested with a series of experiments conducted on collections of fingerprint images. The results reveal that our method can achieve good performance on these data collections

16 citations

Proceedings ArticleDOI
01 Aug 2018
TL;DR: A comprehensive literature survey where social media analytic have been used for smart city implementation in some of the interesting areas include improving quality of life of citizens, smart mobility and transparent and inclusive governance .
Abstract: Urbanization offers opportunities in terms of economic and social life but as the population increases, urban issues like traffic jam, pollution, overcrowding, resource management like water, energy are also increased. Continuous monitoring of city’s infrastructure and automated collection of daily incidents are required to improve quality of life of citizens. Every day, vast no. of citizens are taking part in shaping the smart city using social network by expressing their thoughts, views, feelings & experiences. Although physical sensors planted across the city help to keep eye on the city, it will only answer the question "What happened" but the question "why" remains unanswered sometimes. In this scenario "Social Media" proves to be the goldmine of the information. So this paper presents a comprehensive literature survey where social media analytic have been used for smart city implementation. Some of the interesting areas include improving quality of life of citizens, smart mobility and transparent and inclusive governance .It further suggests some opportunities which can be used for future research.

8 citations


Cited by
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Journal ArticleDOI
TL;DR: Computer and Robot Vision Vol.
Abstract: Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

1,426 citations

01 Jan 2011
TL;DR: An overview of the current research being carried out on various breast cancer datasets using the data mining techniques to enhance the breast cancer diagnosis and prognosis is presented.
Abstract: Breast Cancer Diagnosis and Prognosis are two medical applications pose a great challenge to the researchers. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer Diagnosis and Prognosis. Breast Cancer Diagnosis distinguishes benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is likely to recur in patients that have had their cancers excised. Thus, these two problems are mainly in the scope of the classification problems. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis. In this paper we present an overview of the current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis. Breast cancer has become the leading cause of death in women in developed countries. The most effective way to reduce breast cancer deaths is detect it earlier. Early diagnosis requires an accurate and reliable diagnosis procedure that allows physicians to distinguish benign breast tumors from malignant ones without going for surgical biopsy. The objective of these predictions is to assign patients to either a "benign" group that is non- cancerous or a "malignant" group that is cancerous. The prognosis problem is the long-term outlook for the disease for patients whose cancer has been surgically removed. In this problem a patient is classified as a 'recur' if the disease is observed at some subsequent time to tumor excision and a patient for whom cancer has not recurred and may never recur. The objective of these predictions is to handle cases for which cancer has not recurred (censored data) as well as case for which cancer has recurred at a specific time. Thus, breast cancer diagnostic and prognostic problems are mainly in the scope of the widely discussed classification problems. These problems have attracted many researchers in computational intelligence, data mining, and statistics fields. Cancer research is generally clinical and/or biological in nature, data driven statistical research has become a common complement. Predicting the outcome of a disease is one of the most interesting and challenging tasks where to develop data mining applications. As the use of computers powered with automated tools, large volumes of medical data are being collected and made available to the medical research groups. As a result, Knowledge Discovery in Databases (KDD), which includes data mining techniques, has become a popular research tool for medical researchers to identify and exploit patterns and relationships among large number of variables, and made them able to predict the outcome of a disease using the historical cases stored within datasets. The objective of this study is to summarise various review and technical articles on diagnosis and prognosis of breast cancer. It gives an overview of the current research being carried out on various breast cancer datasets using the data mining techniques to enhance the breast cancer diagnosis and prognosis.

140 citations

Journal ArticleDOI
30 Apr 2012
TL;DR: In this paper, the authors have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis and discussed the current research being carried out using the data mining techniques.
Abstract: Breast cancer is one of the leading cancers for women in developed countries including India. It is the second most common cause of cancer death in women. The high incidence of breast cancer in women has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.

117 citations

Journal ArticleDOI
TL;DR: Three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer and PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively.
Abstract: Among cancers, breast cancer causes second most number of deaths in women. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. In clinical diagnosis, the use of artificial intelligent techniques as neural networks has shown great potential in this field. In this paper, three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer. Decision making is performed in two stages: training the classifiers with features from Wisconsin Breast Cancer database and then testing. The performance of the proposed structure is evaluated in terms of sensitivity, specificity, accuracy and ROC. The results revealed that PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively. MLP was ranked as the second classifier and was capable of achieving 97.80 and 96.34 % classification accuracy for training and validation phases, respectively, using scaled conjugate gradient learning algorithm. However, RBF performed better than MLP in the training phase, and it has achieved the lowest accuracy in the validation phase.

104 citations