M Vignesh

Bio: M Vignesh is an academic researcher from VIT University. The author has contributed to research in topics: Control chart & Box plot. The author has an hindex of 1, co-authored 1 publications receiving 38 citations.

Data analysis using box and whisker plot for lung cancer

Abstract: 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 Lung cancer dataset. With the help of boxplot, we can easily make relations between samples and we can find the outliers.

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

Abstract: 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.

An adjusted boxplot for skewed distributions

Abstract: The boxplot is a very popular graphical tool for visualizing the distribution of continuous unimodal data. It shows information about the location, spread, skewness as well as the tails of the data. However, when the data are skewed, usually many points exceed the whiskers and are often erroneously declared as outliers. An adjustment of the boxplot is presented that includes a robust measure of skewness in the determination of the whiskers. This results in a more accurate representation of the data and of possible outliers. Consequently, this adjusted boxplot can also be used as a fast and automatic outlier detection tool without making any parametric assumption about the distribution of the bulk of the data. Several examples and simulation results show the advantages of this new procedure.

Software complexity analysis using halstead metrics

Abstract: Software Complexity influences inward connections. Higher the multifaceted nature, bigger the deformities. Programming complexity for any product or a program is hard to discover without utilizing any measurements. The unpredictability, time and exertion fluctuate starting with one program then onto the next. For this reason, Halstead measurements are presented which recognizes the product complexity of a program by utilizing source line with the assistance of operands and operators. This metric was produced by Maurice Halstead to decide a quantitative measure of complexity specifically from the operands and operators in the module. This article gives a correlation between a program that was composed in two unique dialects to distinguish unpredictability, time and exertion of both and furthermore to gauge which programming dialect was better in wording less time to execute, least exertion.

Heuristic prediction of rainfall using machine learning techniques

Abstract: 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

Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network

Abstract: Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, P V , apparent viscosity, A V , yield point, Y p , flow behavior index, n , and flow consistency index, k , has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2–3 h for taking measurements and cleaning the instruments). However, mud weight, M W , and Marsh funnel viscosity, M F , are periodically measured every 15–20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties of calcium chloride brine-based mud using M W and M F measurements then extract empirical correlations in a white-box mode to predict these properties based on M W and M F . Field measurements, 515 points, representing actual mud samples, were collected to build the proposed ANN models. The optimized parameters of these models resulted in highly accurate results indicated by a high correlation coefficient, R, between the predicted and measured values, which exceeded 0.97, with an average absolute percentage error, AAPE, that did not exceed 6.1%. Accordingly, the developed models are very useful for monitoring the mud rheology to optimize the drilling operation and avoid many problems such as hole cleaning issues, pipe sticking and loss of circulation.