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N. Subhash Chandra

Bio: N. Subhash Chandra is an academic researcher from Jawaharlal Nehru Technological University, Hyderabad. The author has contributed to research in topics: Software as a service & Cloud computing. The author has an hindex of 7, co-authored 13 publications receiving 156 citations.

Papers
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Journal ArticleDOI
TL;DR: The generally used techniques for Heart Disease Prediction and their complexities are summarized in this paper and it is observed that Fuzzy Intelligent Techniques increase the accuracy of the heart disease prediction system.
Abstract: The Healthcare trade usually clinical diagnosis is ended typically by doctor’s knowledge and practice. Computer Aided Decision Support System plays a major task in medical field. Data mining provides the methodology and technology to alter these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. Among the increasing research on heart disease predicting system, it has happened to significant to categories the research outcomes and gives readers with an outline of the existing heart disease prediction techniques in each category. Data mining tools can answer trade questions that conventionally in use much time overriding to decide. In this paper we study different papers in which one or more algorithms of data mining used for the prediction of heart disease. As of the study it is observed that Fuzzy Intelligent Techniques increase the accuracy of the heart disease prediction system. The generally used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

56 citations

Book ChapterDOI
01 Jan 2015
TL;DR: It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.
Abstract: Data mining technique in the history of medical data found with enormous investigations resulted that the prediction of heart disease is very important in medical science. The data from medical history has been found as heterogeneous data and it seems that the various forms of data should be interpreted to predict the heart disease of a patient. Various techniques in Data Mining have been applied to predict the patients of heart disease. But, the uncertainty in data was not removed with the techniques available in data mining. To remove uncertainty, it has been made an attempt by introducing fuzziness in the measured data. A membership function was designed and incorporated with the measured value to remove uncertainty. Further, an attempt was made to classify the patients based on the attributes collected from medical field. Minimum distance K-NN classifier was incorporated to classify the data among various groups. It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.

41 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: To remove uncertainty of unstructured data, an attempt was made by introducing fuzziness in the measured data and fuzzified data was used to predict the heart disease patients and Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.
Abstract: Data mining technique in the history of medical data found with enormous investigations found that the prediction of heart disease is very important in medical science In medical history it is observed that the unstructured data as heterogeneous data and it is observed that the data formed with different attributes should be analyzed to predict and provide information for making diagnosis of a heart patient Various techniques in Data Mining have been applied to predict the heart disease patients But, the uncertainty in data was not removed with the techniques available in data mining and implemented by various authors To remove uncertainty of unstructured data, an attempt was made by introducing fuzziness in the measured data A membership function was designed and incorporated with the measured value to remove uncertainty and fuzzified data was used to predict the heart disease patients Further, an attempt was made to classify the patients based on the attributes collected from medical field Minimum Euclidean distance Fuzzy K-NN classifier was designed to classify the training and testing data belonging to different classes It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques

31 citations

Journal ArticleDOI
TL;DR: A novel feature descriptor called local oppugnant color space extrema patterns (LOCSEP) for image indexing retrieval is proposed and shows a significant improvement in terms of their evaluation measures as compared to state-of-the-art methods on respective databases.
Abstract: This paper proposes a novel feature descriptor called local oppugnant color space extrema patterns (LOCSEP) for image indexing retrieval. The existing directional local extrema pattern (DLEP) extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. The proposed method integrates the concepts of color and texture features. First, the color image is converted into RGB (red, green and blue) and HSV (hue, saturation and value) color spaces. Then oppugnant color spaces, RV, GV, BV are used for the extract of oppugnant DLEP features (LOCSEP). The performance of the proposed method is tested by conducting two experiments on benchmark databases viz. Corel-1K and MIT VisTex databases. The performance of the proposed method is compared with the state-of-the-art methods for image retrieval and face recognition applications in terms of average retrieval precision (ARP) and average retrieval rate (ARR). The results after being investigated show a significant improvement in terms of their evaluation measures as compared to state-of-the-art methods on respective databases.

26 citations

Journal ArticleDOI
TL;DR: This paper majorly discusses the distributed privacy preservation techniques which provide secure solutions using primitive operations of cryptographic protocols such as secure multi-party computation (SMPC), secret sharing schemes (SSS) and homomorphic encryption (HC).
Abstract: distributed data mining settings, leakage of the real data is not adequate because of privacy issues. To overcome this problem, numerous privacy-preserving distributed data mining practices have been suggested such as protect privacy of their data by perturbing it with a randomization algorithm and using cryptographic techniques. In this paper, we review and provide extensive survey on different privacy preserving data mining methods and analyses the representative techniques for privacy preserving data mining. We majorly discuss the distributed privacy preservation techniques which provide secure solutions using primitive operations of cryptographic protocols such as secure multi-party computation (SMPC), secret sharing schemes (SSS) and homomorphic encryption (HC). Keywordsmining, K-means clustering, Data privacy, Privacy preserving, Multiparty computation, Threshold Cryptography.

14 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper proposes a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease with the hybrid random forest with a linear model (HRFLM).
Abstract: Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

783 citations

Journal ArticleDOI
TL;DR: Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers on noise removed feature extracted signal for beat classification.
Abstract: Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.

177 citations

Journal ArticleDOI
01 Nov 2020
TL;DR: This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naive Bayes, decision tree, K-nearest neighbor, and random forest algorithm, using the existing dataset from the Cleveland database of UCI repository of heart disease patients.
Abstract: Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naive Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.

144 citations

Journal ArticleDOI
TL;DR: This study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused will help to determine the diagnostic aspects of medical disciplines that are being neglected.

118 citations

Book
01 Jan 2001
TL;DR: Inelastic scattering in Electron Microscopy-Effects, Spectrometry and Imaging as discussed by the authors, Quantitative Analysis of High-Resolution Atomic Images, Electron Crystallography-Structure determination by combining HREM, Crystallographic image processing and electron diffraction.
Abstract: 1 The Modern Microscope Today.- 2 The Quest for Ultra-High Resolution.- 3 Z-Contrast Imaging in the Scanning Transmission Electron Microscope.- 4 Inelastic Scattering in Electron Microscopy-Effects, Spectrometry and Imaging.- 5 Quantitative Analysis of High-Resolution Atomic Images.- 6 Electron Crystallography-Structure determination by combining HREM, Crystallographic image processing and electron diffraction.- 7 Electron Amorphography.- 8 Weak-Beam Electron Microscopy.- 9 Point Group and Space Group Identification by Convergent Beam Electron Diffraction.- 10 Advanced Techniques in TEM Specimen Preparation.

107 citations