Paper Title

THE UNUSUAL BEHAVIOR OF GIBBS’ REAGENT VERSUS NITROFURAL

Abstract

A novel optical sensor for the rapid and direct determination of permethrin preservatives in treated wood was designed. The optical sensor was fabricated from the immobilization of 2,6-dichloro-p-benzoquinone-4-chloroimide (Gibbs reagent) in nafion/sol–gel hybrid film and the mode of detection was based on absorption spectrophotometry. Physical entrapment was employed as a method of immobilization.
KEYWORDS : 2,6-dichloro-p-benzoquinone-4-chloroimide; Permethrin; Nafion; Sol–gel; Optical sensors

Paper Title

CHEMICAL POLLUTION ASSEMENTS OF MUSI RIVER WITH SEASONAL VARIATION – 5 YEARS STUDY

Abstract

Most of the industrialization in India was at the banks of rivers to fulfil its water needs for companies. MUSI river was not exceptional among that, most of urban industrialization at river banks, such that risk assessment’s needed for the level of contamination in river water which effects soil and agricultural also. The
KEYWORDS : Chemical additions, quality assessment, recent survey.

Paper Title

AN OPTIMAL STUDY AND ANALYTICAL WORK ON STAMPING TOOL STUDY

Abstract

To understand the wear progression, accelerated stamping tests were performed using a semi-industrial stamping setup which can perform clamping, piercing, stamping in a single cycle. The time domain features related to stamping were computed for the acoustic emissions signal of each part. The sidewalls of the stamped parts were scanned using an optical profilometer to obtain profiles of the worn part, and they were qualitatively correlated to that of the acoustic emissions signal. Based on the wear behaviour, the wear data can be divided into three stages: In the first stage, no wear is observed, in the second stage, adhesive wear is likely to occur, and in the third stage severe abrasive plus adhesive wear is likely to occur. Scanning electron microscopy showed the formation of lumps on the stamping tool, which represents galling behavior. Correlation between the time domain features of the acoustic emissions signal and the wear progression identified in this study lays the basis for tool diagnostics in stamping industry.
KEYWORDS :

Paper Title

A NEW K-NEAREST NEIGHBORS CLASSIFIER FOR N-DIMENSIONAL DATASETS USING MAP REDUCE

Abstract

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. This topic is known as big data classification, in which standard data mining techniques normally fail to tackle such volume of data. In this contribution we propose a Map Reducebased approach for k-Nearest neighbor classification.
KEYWORDS : K-nearest neighbors; KNN; classifier, classification.