In this paper, a prediction of diabetes disease for people of various age groups and genders is implemented using cost optimization and control chart. Generally, Diabetes is one of the metabolic diseases caused due to increase in the sugar levels of blood over a prolonged period. Diabetes will be confirmed by examining albumin and bilirubin levels and protein content in blood. This paper discusses on predicting diabetes disease among various persons and existence of disease among them using Cost optimization method. © 2017 IEEE.

Chandra Segar T

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

G v saisharan

K vamsi krishna

Kota jayadev senapathi

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

2017 International Conference on Trends in Electronics and Informatics (ICEI)

Decision making is used for the selection oriented process. But can we use it in the field of healthcare is the biggest question. The answer is yes here also we can have with machine learning (ML) classifiers like Decision Tree (DT), random forest (RF), support vector machine (SVM), and neural network (NN). In the case of breast cancer (BC) disease, we can predict using machine learning classifiers. For this, original BC samples experiment. Our model named clustering based feature selection (CFS) intends to increase the accuracy and to reduce the feature dimension. We experiment the classifiers in three stages: 1. all features of BC, 2. DT based cluster, and 3. Feature selection. In all feature, RF achieves 99.10% accuracy, in DT based cluster RF, achieves 99.37% accuracy, and finally in cluster-based feature selection (CFS) with only four features the RF achieves 99.50% accuracy. The experimental results of our CFS approach along with DT, RF, SVM, and NN classifier along with its performance are presented. © 2019 IEEE.

2019 Innovations in Power and Advanced Computing Technologies (i-PACT)

Optimized machine learning model using Decision Tree for cancer prediction

In the computing world, the digital transformation of data grows exponentially with every year. However, these situations are predominantly tackled by leading enterprises through by adopting a hybrid IT model. This model effectively supports the organization through strategic design to provide standard delivery to customers from independent multi-sourced entities. An experimental design method is applied using hybrid IT architecture with gene-based cryptomata (HITAGC) which is well suited to real-time cloud environments to store and retrieve the cloud space data efficiently. The CSC primarily relies on the service of CSP to keep their confidential data, and this system ultimately uses the PKC crypts. To address this security demand, we propose two different schemes such as HITAGC-PKC and hybrid HITAGC. The first model is designed to generate robust asymmetric keys for the Internet of Things, and the other hybrid HITAGC is for efficient sharing of big data over the cloud in a secure manner. The performance of these crypts is compared with the traditional systems like standard RSA, ESRKGS, SED2, EDCon, and AES. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

Service Oriented Computing and Applications

Hybrid IT architecture by gene-based cryptomata (HITAGC) for lightweight security services

This paper mainly focuses on the design aspects of force balance measurement system for aerodynamic vehicle flying at hypersonic speed. When the speed of vehicle is hypersonic, additional aerodynamic heating creates difficulty in the system and needs change in the design geometry and material type .For the purpose of examining the model we have considered a simulation of blunt cone with following parameters like, length of the string, diameter of string, material of string, drag coefficient, pressure, velocity, density of air, Mach number, Reynolds’s number, stagnation enthalpy were suitably selected. CFD is used as a tool to calculate the drag force generated under the hypersonic conditions. A measurement system was developed for measuring the strain developed in the blunt cone when it is subjected to shock force. The measurement system was calibrated statically using hang weight and dynamically using impact load test performed on the string, from the calibration test impulse response function is calculated. And a fuzzy predication based approach was developed to predict the strain developed in the system.

Materials Today: Proceedings

One-Dimension Force Balance System for Hypersonic Vehicle an experimental and Fuzzy Prediction Approach

This article displays the different programming measurements of gaming applications utilizing the metric device code counter for C and C++. This contains absolutely three keeping money applications written in C++ each containing distinctive classes. This device assesses the measurements of every application and gives out the limit estimations of every measurement. The correlation between the gaming utilizations of different measurements reveals to us the coding benchmarks to be taken after while composing the code. © 2017 IEEE.

2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)

Evaluating software metrics of gaming applications using code counter tool for C and C++ (CCCC)

This paper determines methods to develop a novel technique for predicting a nation in view of the Olympic awards owned by 2012. It is the combination of three methods are Pearson correlation coefficient, Spearman correlation coefficient and along with linear regression. The main idea of the paper is to compare the value of Spearman and Pearson correlation coefficient as there in the same set of data. The example concerns the comparison of the total medals and the GDP (gross domestic product) that has been obtained by each country. The results from using these methods do the heuristics prediction of Olympic medals using machine learning. © 2017 IEEE.

Heuristics prediction of olympic medals using machine learning

Spearman's rank relationship coefficient is a nonparametric (dispersion free) rank measurement. Spearman's coefficient is not a measure of the direct relationship between two factors, as a few analysts proclaim. Pearson's relationship coefficient is the covariance of the two factors separated by the result of their standard deviations. The possibility of the paper is to look at the estimations of Pearson's concrete compressive strength coefficient and Spearman's rank relationship coefficient and in addition their factual hugeness for various arrangements of information depicting provincial records of the financial advancement. In this, the Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data. Spearman correlation is often used to evaluate relationships involving ordinal variables. The Pearson and Spearman's method is compared with the rank coefficient in the concrete compressive strength. © 2017 IEEE.

Journal	Data powered by Typeset2017 International Conference on Trends in Electronics and Informatics (ICEI)
Publisher	Data powered by TypesetIEEE
Open Access	0