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.

Chandra Segar T

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Monica C

Department of Mathematics

School of Advanced Sciences

Vijayalakshmi A

Computer Science

School of Computer Science and Engineering

Chennai Campus

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 of Electronics, Communication and Aerospace Technology (ICECA)

Stock price forecasting is a prevalent and critical subject in financial and academic studies. The objective of this paper is to forecast today's high price in the market from open of today, open from yesterday and closing of yesterday. We are using the real dataset from NSE NIFTY. We will be implementing a fuzzy intuition system such as Mamdani, Sugeno, and machine learning algorithms like CNN and LSTM. Cost optimization would be used for Sugeno. This paper would compare and improvise the algorithm which will show a higher prediction accuracy rate and would be proved statistically. © 2019 IEEE.

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

Prediction of Share Market using FIS

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.

Optimized machine learning model using Decision Tree for cancer prediction

This paper is carried on the heuristic prediction of rainfall using machine learning techniques. As we know agriculture was the predominant of our country and economy. While a regular rain pattern is usually played vital for healthy agriculture but too much rainfall or too little rainfall can be harmful, even it led to devastating of crops. This paper discusses the rate of rainfall in previous years according to various crops seasons like rabi, Kharif, zaid and predicts the rainfall in future seasons. The paper also measures the different categories of data by linear regression method in metrics for effective understanding of agriculture in India. We have selected a real dataset which consists of past year's rainfall rate according to various seasons. Results of this application help farmers to make a correct decision to harvest a particular crop accordingly to crops seasons. Linear regression helps to find. © 2017 IEEE.

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

Heuristic prediction of rainfall using machine learning techniques

In statistical analysis, we have a collection of data, with the use of these data, we have to do analysis based on our requirements. With the collection of data using Statistical analysis, we deal collection, analysis, presentation and organizing the data. With the help of statistical analysis, we can find underlying patterns, relationships, and trends between data samples. The R system for statistical computing is an environment for data analysis and graphics. Here we are going to implement boxplot method and control chart methods for sales analysis in a stationary shop dataset. With the help of boxplot, we can easily make relations between samples and we can find the outliers. © 2017 IEEE.

Data analysis using box and whisker plot for stationary shop analysis

Nature inspired algorithm

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

A new variant of RSA has been proposed called Memory Efficient Multi Key (MEMK) generation scheme. For sensitive data, our scheme will aid in exchanging the information between cloud to IoT and IoT to IoT devices. When cryptography belongs to the asymmetric type, then it has public and private keys. For memory efficiency, our scheme reuses the RSA scheme with a Diophantine form of the nonlinear equation. Moreover, our scheme performance comparatively performs well and this mainly due to the use of RSA public key alone. Due to this, our MEMK does not require multiplicative inverse function or Extended Euclid's algorithm. Finally, we have made an experimental result on various phases of MEMK PKC such as key generation, encryption, and decryption by varying the N-bit modulo bits from 1K to 10K. © 2017 IEEE.

2017 Innovations in Power and Advanced Computing Technologies (i-PACT)

Memory efficient multi key (MEMK) generation scheme for secure transportation of sensitive data over cloud and IoT devices

IoT devices are everywhere. Shortly, every 'thing' is going to be connected and interact with each other. What are these things? They can be your microwaves, heaters, air conditioners or even your refrigerators. It assessed that there would be 50 billion connected IoT devices by 2020 which is about 7 IoT gadgets for every individual on the planet. IoT devices are low powered devices which have a minimal amount of processing capabilities. With ever increasing number of IoT devices, there is a subsequent need for secure communication among them. In this paper, we aim to propose an encryption algorithm used for secure communication by taking into account the limitations of IoT devices. © 2017 IEEE.

Multi key distribution scheme by diophantine form for secure IoT communications

Decision making is a difficult task over the complex, spirited and competitive environment. A variety of research provides diverse notification for individual failure in making logical decisions under certain circumstances by applying normative approaches. In today's competitive market, the advance of technology is closely related to consumer needs for the extraction of good quality products at less cost which is in rarely finding a model in Consumer to Business (C2B). The aim of the paper is to optimize the C2B model by intuitionistic Fuzzy to resolve multi decision making problems. It derives multi criteria decision making to support complex environments in C2B using membership and non-membership attributes which is built to generate the intuitionistic fuzzy priority weighting vector based on minimum distance and similarity measures. This technique classifies the object type as per the customer needs with a ranking in the optimized way make efficient for the customer satisfaction. © 2017 IEEE.

2017 4th International Conference on Electronics and Communication Systems (ICECS)

An assessment framework of intuitionistic fuzzy network for C2B decision making

This work aims to study the relation between Precipitation and six of its various climatic factors. The analysis shows how much is the contribution of Reference Crop Evapotranspiration, Average Temperature, Wet Day frequency, Potential Evapotranspiration, Vapor Pressure and the Cloud Cover towards precipitation. For the purpose of finding the percentage of contribution by every parameter on Precipitation, we use a method called Analytical Hierarchical Processing. Basically, Analytical Hierarchical Processing is used for decision making. © 2017 IEEE.

Journal	Data powered by Typeset2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)
Publisher	Data powered by TypesetIEEE
Open Access	0