Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

An individual grape leaf disease identification using leaf skeletons and KNN classification

Abstract: The most challenging process in agricultural applications is identification of leaf individually In this paper, the classification of grape leaf diseases is proposed along with the leaf identification Initially, the leaf skeletons are identified based on grape images Since, the leaf skeletons are used for estimating the positions and directions of the leaves The Tangential Direction (TD) based segmentation algorithm is proposed for retrieval of skeletons If the grape leaf images are classified, then the histograms of H and a color channels are generated and the pixels values are observed to distinguish the healthy and diseased tissues Then, extract the features and classify by using the KNN classification algorithm in order to find the leaf diseases

A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems

Abstract: Wireless Sensor Networks (WSN) consists of sensors used for sensing environmental conditions and many more applications in real world. Air pollution is a threat to the life of humans. To control the air pollution it is necessary to monitor the pollutant gases in periodically. Various air pollution monitoring systems using sensor network have been developed, deployed and tested in the literature. This paper presents a comparative study about the literature for air pollution monitoring systems based on the classification such as stationary air pollution monitoring systems, dynamic air pollution monitoring systems and pollution data analysis techniques. These pollution monitoring systems are compared based on the methodologies followed, microcontroller used, communication device used, pollutants analyzed using sensors, evaluation attributes, tested location and performance of the system. This paper also discusses the merits and demerits of the air pollution monitoring systems.

Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning.

Abstract: Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and non-linear. In this work, machining process has been carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness has been measured using surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through feed-forward back-propagation network using Matlab (2009a) software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN has been used confidently. The results obtained conclude that ANN is reliable and accurate for predicting the values. The actual R a value has been obtained as 1.1999 μm and the corresponding predicted surface roughness value is 1.1859 μm, which implies greater accuracy.