Gold vs Gold Exchange Traded Funds: An Empirical Study in India

This study aim of this is to estimate the relationship between gold and Gold Exchange Traded Fund (ETF) and the performance of Gold ETFs in India by using various statistical models. The data for the study covers a period of three years from 2015 to 2018. The data was collected from the National Stock Exchange database and other sources. The outcome of this study was to find out whether there is a relationship between gold and Gold ETFs. It was found out that Gold ETFs has more returns than the physical gold; Axis ETF performed the best among those Gold ETFs selected for the study. This study will be beneficial for the market researchers and investors who find the best opportunities in the Gold ETFs

Semi-Parametric Likelihood Functions for Bivariate Survival Data

Because of the numerous applications, characterization of multivariate survival distributions is still a growing area of research. The aim of this thesis is to investigate a joint probability distribution that can be derived for modeling nonnegative related random variables. We restrict the marginals to a specified lifetime distribution, while proposing a linear relationship between them with an unknown (error) random variable that we completely characterize. The distributions are all of positive supports, but one class has a positive probability of simultaneous occurrence. In that sense, we capture the absolutely continuous case, and the Marshall-Olkin type with a positive probability of simultaneous event on a set of measure zero. In particular, the form of the joint distribution when the marginals are of gamma distributions are provided, combining in a simple parametric form the dependence between the two random variables and a nonparametric likelihood function for the unknown random variable. Associated properties are studied and investigated and applications with simulated and real data are given

IOT-Based Pest Classification And Automatic Irrigation For Precision Agriculture Using Wireless Sensor Networks

Automatic pest classification is a very important step to protect the agricultural crops against the attack of pest. This helps to increase the crop yield by reducing the pests that affect agricultural productivity. In addition, automatic irrigation systems aid in the enhancement of agricultural land productivity by computing optimal amount of water required by the plants. In this research, we proposed a new technique called IoT-based pest classification and automatic irrigation algorithm (IPCAI) using wireless sensor networks. In this technique, sensors like moisture sensor, temperature sensor and camera sensors are integrated to Arduino Microcontroller module. The data acquired by these sensors are processed using Raspberry Pi module that is connected to the cloud. The proposed IPCAI machine learning algorithm is embedded into the Raspberry Pi module that classifies the type of pest and also computes the optimal amount of water required by the crops. Based on the type of pest being detected, suitable pesticide is sprayed to the crops to improve the crop yield. This helps in the prevention of spreading of pests. It was found that the proposed algorithm classifies 40 different type of pest with very high accuracy. In addition, the proposed automatic irrigation system helps to conserve enormous quantities of water. The IoT-module connected to the Raspberry Pi helps to upload the data collected by the sensors, pest classification result, and water requirement result to the cloud. From the cloud, the data is transmitted to the farmer’s mobile, using which the famers can continuously monitor the crop land from remote locations. The proposed pest classification algorithm achieved high specificity of 95.86% with a precision rate of 96.69%

Effect of Sea Level Rise in Gulf of Khambhat, West Coast of India

Climate Change, Adaptation and Long-Term Prediction