Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Modified segmentation algorithm and its feature extraction of cancer affected white blood cells

Abstract: The cancer cells are multiplicative in nature. Doctors face difficulties in counting the white blood cells (WBCs) at a particular stage due to crowding of cells. This paper proposes the robust segmentation algorithm that can reliably separate touching cells. Segmentation is the main important step in medical image processing. Precisely locating the area of interest in an image, in the presence of inherent uncertainty and ambiguity, is a challenging problem in medical imaging. Hence, one is often faced with a situation that demands proper segmentation. The algorithm is composed of two steps. It begins with a detecting and finding the cells in the region that utilizes level set algorithm. Next, the contour of big cell is obtained using modified level set active contour based on a piecewise smooth function. Feature extraction process follows Segmentation. The required information from the Geomentry and Texture features were obtained. The Feature Selection process is carried out by using Minimum-Redundancy And Maximum-Relevance(MRMR) technique. BPN is used as a classifier for the classification process. Finally, the proposed algorithm is compared with several images which aids in applications such as locating and identifying the tumours and other pathologies.

Real-time day ahead energy management for smart home using machine learning algorithm

Abstract: Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error.

A Survey on Various Optimization Algorithms to Solve Vehicle Routing Problem

Abstract: The purpose of Vehicle Routing Problem (VRP) is to find the optimal routes to reach the destination for the customers. Effects of transports on the environment are dangerous nowadays. It is important to reduce the distance traversed by the vehicles. Over the past few years many algorithms have been proposed for vehicle routing problem. This paper outlines the basic concepts of few algorithms and these algorithms are compared based on the outcomes. In addition to finding the best route during travel, vehicle speed, carbon dioxide emission rates can also be examined. The study indicates that each algorithm has some specific features and is suitable for particular applications.

Sodium alginate incorporated with magnesium nitrate as a novel solid biopolymer electrolyte for magnesium-ion batteries

Abstract: Solid biopolymer electrolytes have gained much attention in recent years. Due to their various advantages, it can be used in advanced electrochemical devices. The present study focuses on synthesizing and characterizing natural solid biopolymer electrolytes that consist of sodium alginate as the host polymer and magnesium nitrate (Mg(NO3)2.6H2O) as the ionic dopant via solution casting technique. X-ray diffraction analysis of prepared solid biopolymer electrolytes validates the increase in the amorphous nature as salt concentration increases. The interaction and the complexation between the host biopolymer and the magnesium salt are confirmed by Fourier transforms infrared spectroscopy. The solid biopolymer electrolyte composition of 40 M wt.% NaAlg:60 M wt.% Mg(NO3)2·6H2O possesses optimum ionic conductivity value of the order of 4.58 × 10−3 S cm−1 as observed by the AC impedance spectroscopy analysis at room temperature. The glass transition temperature (Tg) of the prepared solid biopolymer electrolytes has been studied using differential scanning calorimetry. Linear sweep voltammetry study reveals that the highest magnesium ion-conducting membrane has electrochemical stability of 3.5 V. Further, an optimum ionic conducting solid biopolymer membrane (40 M wt.% NaAlg:60 M wt.% Mg(NO3)2·6H2O) has been utilized to fabricate a primary magnesium ion conducting battery. The open circuit voltage of the proposed solid biopolymer membrane is 1.93 V, and the performance of the battery has been studied.