Ultrasonic Studies of Binary Liquid Mixture of Diethylenetriamine (DETA) and 1-Chloronapthalene
Authors: SARWADE M. P
Abstract
Density ( ), Ultrasonic velocity (U) and viscosity ( ) of the binary mixture containing Diethylenetriamine (DETA) and 1-Chloronapthalene were measured over entire composition range. These measurements were done at constant temperature 308 K and at frequency 1MHZ. These are used to compute intermolecular free length ( ), adiabatic compressibility ( ), relaxation time ( ), real volume ( ), Vanderwaal’s constant (b) and free volume (Vf). Along with the above parameters excess parameters were computed. The excess parameters evaluated are excess intermolecular free length ( ), excess free volume ( ) and excess adiabatic compressibility ( ). These parameters have been interpreted in terms of intermolecular interactions at frequency 1MHZ & at constant temperature 308K. These excess parameters have been used to discuss the nature and extent of interaction between the component molecules in the binary mixture
DETERMINATION OF BLACKSPOTS IN MEERUT CITY USING GIS
Authors: Nikhil, Abhishek Raghav, Abhay Singh, Abhishek Singh
Abstract
Road safety is an important aspect which plays a key role in sustainable transportation development strategy. The adverse impact of modern road transportation systems is injuries and loss of life due to accidents, with the number of vehicles on the roads growing rapidly results in more traffic accidents & deaths. While the road accident situation is improving in the high income industrialized countries, most developing countries are still working to control the situation.
DIABETES AND TIME PREDICTION USING DIFFERENT MACHINE LEARNING CLASSIFIERS
Authors: Drakshayini C R, Mrs. Pushpalatha S, Dr. G. F. Ali Ahammed
Abstract
Diabetes is also known as diabetes mellitus; it is a group of metabolic disorders which affected millions of people. The detection of diabetes is very importance, due to its severe complications. There are lot of research studies about diabetes identification, many of researches are based on the Pima Indian diabetes data set. The data set is based on the studying women in Pima Indian population which was started from 1965, where diabetes rates is comparatively high. Before many research studies done are mainly focused on one or two particular complex techniques to test the data, but comprehensive research over many common techniques is missing. This work predicts diabetes disease prediction as well as time prediction using machine learning algorithms. In machine algorithms we use two types of algorithms that is Naïve Bayes algorithm for diabetes prediction and K-nearest neighbour algorithm for time prediction. It provides better results on the trained data sets which can be lead to better performance in diabetes prediction. In this paper the source code for diabetes prediction is made by publicly available.