CIT Gubbi

Gubbi, India

CIT Gubbi

Gubbi, India
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Nagaraja P.,CIT Gubbi | Sadashivappa G.,R V C E
Smart Innovation, Systems and Technologies | Year: 2018

This paper focuses on the Fault Diagnosing methodologies crucial for attaining reliability and maintainability of all electronic circuits is implemented for analog to digital converter (ADC) with a wide range of faults. Fault, Diagnosis (FD) is considered as the pattern recognition problem and solved by machine learning theory. Functional, test is, is needed instead of structural test for, testing complex circuits. Fault diagnosis using Fault Dictionary, Neural Networks and Fuzzy logic are enigmatic or inconclusive diagnosis results which have more debug duration and even inaccurate repair actions that exponentially rises service overhead. The effectiveness of these methods are considered, which cover ability in detecting, identifying and localization of faults, the ability of analysing linear and nonlinear circuits, etc. Recent machine learning techniques like support vector machines (SVM) with kernel functions improve the preciseness of functional FD which reduces the product cost through correct repair process. The proposed Multikernel SVM (MKSVM) methodology gives better results than earlier methods as it works with the fundamentals of machine learning and generalization for FD. © 2018, Springer International Publishing AG.


Thara D.K.,CIT Gubbi | Premasudha B.G.,SIT | Ram V.R.,SIT | Suma R.,SIT
Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016 | Year: 2017

As the volume of data generating is increasing day by day in this internet world, the term Big Data is becoming a very popular buzzword in today's market. Big Data is used in various sectors of the internet world. In this paper an effort is made to demonstrate that even the healthcare industries are stepping into Big Data pool to take all benefits from its various advanced tools and technologies. The paper presents the review of various research efforts made in healthcare domain using Big Data concepts and methodologies. The thought of Big Data can be used for better health planning. Its methodologies can be used for healthcare data analytics which helps in better decision making to increase the business value and customer interest and to provide eHealth services among various healthcare stakeholders by using messaging standards like Health Level?, Digital Imaging and Communications in Medicine (DICOM), Health Insurance Portability and Accountability (HIPAA), message broker etc. Big Data techniques can be applied to develop systems for the early diagnosis of disease, understand connection between HATS (HIV/AIDS Tuberculosis and Silicosis) and also to develop integrated data analytics platforms. After presenting these many positive progresses of Big Data on healthcare, the paper also presents the hurdles faced by healthcare systems in using Big Data technologies. Further the paper includes the list of various Big Data tools, few case studies, few applications which are worth implementing using Big Data in healthcare followed by the concluding remarks. © 2016 IEEE.

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