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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Asadi Srinivasulu
Sree Vidyanikethan Engineering College
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Disease prediction in big data healthcare using extended convolutional neural network techniques Asadi Srinivasulu; Asadi Pushpa
International Journal of Advances in Applied Sciences Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (676.343 KB) | DOI: 10.11591/ijaas.v9.i2.pp85-92

Abstract

Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes.  The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Clustering Large Data with Mixed Values Using Extended Fuzzy Adaptive Resonance Theory Asadi Srinivasulu; Gadupudi Dakshayani
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 3: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i3.pp617-628

Abstract

Clustering is one of the technique or approach in content mining and it is used for grouping similar items. Clustering software datasets with mixed values is a major challenge in clustering applications. The previous work deals with unsupervised feature learning techniques such as k-Means and C-Means which cannot be able to process the mixed type of data. There are several drawbacks in the previous work such as cluster tendency, partitioning, less accuracy and less performance. To overcome all those problems the extended fuzzy adaptive resonance theory (EFART) came into existence which indicates that the usage of fuzzy ART with some traditional approach. This work deals with mixed type of data by applying unsupervised feature learning for achieving the sparse representation to make it easier for clustering algorithms to separate the data. The advantages of extended fuzzy adaptive resonance theory are high accuracy, high performance, good partitioning, and good cluster tendency. This EFART adopts unsupervised feature learning which helps to cluster the large data sets like the teaching assistant evaluation, iris and the wine datasets. Finally, the obtained results may consist of clusters which are formed based on the similarity of their attribute type and values.
Co-Authors Asadi Pushpa Gadupudi Dakshayani