A Family of Unbiased Modified Linear Regression Estimators

In this paper, a family of modified linear regression estimators has been proposed which are unbiased. The variance of the proposed estimators and the conditions for which the proposed estimators perform better than the classical ratio estimator and the existing modified ratio estimators have been obtained. Further, we have shown that the classical ratio estimator, the existing modified ratio estimators, and the usual linear regression estimator are the particular cases of the proposed estimators. It is observed from the numerical study that the proposed estimators perform better than the ratio estimator and the existing modified ratio estimators

Estimation of Variance Using Known Coefficient of Variation and Median of an Auxiliary Variable

A modified ratio type variance estimator for estimating population variance of a study variable when the population median and coefficient of variation of an auxiliary variable are known is proposed. The bias and mean squared error of the proposed estimator are derived and conditions under which the proposed estimator performs better than the traditional ratio type variance estimators and modified ratio type variance estimators are obtained. Using a numerical study results show that the proposed estimator performs better than the traditional ratio type variance estimator and existing modified ratio type variance estimators

An IOT based smart metering development for energy management system

The worldwide energy demand is increasing and hence necessity measures need to be taken to reduce the energy wastage with proper metering infrastructure in the buildings. A Smart meter can be used to monitor electricity consumption of customers in the smart grid technology. For allocating the available resources proper energy demand management is required. During the past years, various methods are being utilized for energy demand management to precisely calculate the requirements of energy that is yet to come. A large system presents a potential esteem to execute energy conservation as well as additional services linked to energy services, extended as a competent with end user is executed. The supervising system at the utilities determines the interface of devices with significant advantages, while the communication with the household is frequently proposing particular structures for appropriate buyer-oriented implementation of a smart meter network. Also, this paper concentrates on the estimation of vitality utilization. In this paper energy is measured in units and also product arrangement is given to create bill for energy consumption and implementing in LabVIEW software. An IOT based platform is created for remote monitoring of the metering infrastructure in the real time. The data visualization is also carried out in webpage and the data packet loss is investigated in the remote monitoring of the parameters

Implementation of Deep CNN Model for the Detection of Plant Leaf Disease

The potato is the most important tuber crop in the world, and it is grown in about 125 different nations. Potato is the crop that is most commonly consumed by a billion people worldwide, virtually every day, behind rice and wheat. However, a number of bacterial and fungal diseases are causing the potato crop's quality and yield to decline. Potato Leaf diseases must be promptly identified and prevented to increase production. Various researchers look for solutions to protect plants instead of   traditional processes which take more time. Recent technological developments have thrown up many alternates to traditional methods which are labour intensive. The application of AlexNet model Deep Convolutional Neural Network(CNN) to recognise diseases in potato plants avoids the disadvantages of selecting disease spot features artificially and makes more objective the plant disease feature extraction. It improves research efficiency and speeds up technology transformation. Accuracies ranging from 85% - to 95% were obtained using AlexNet model Deep

Graph-Search and Differential Equations for Time-Optimal Vessel Route Planning in Dynamic Ocean Waves

Time-optimal paths are evaluated by VISIR (\u201cdis- coVerIng Safe and effIcient Routes\u201d), a graph-search ship routing model, with respect to the solution of the fundamental differential equations governing optimal paths in a dynamic wind-wave environment. The evaluation exercise makes use of identical setups: topological constraints, dynamic wave environmental conditions, and vessel-ocean parametrizations, while advection by external currents is not considered. The emphasis is on predicting the time-optimal ship headings and Speeds Through Water constrained by dynamic ocean wave fields. VISIR upgrades regarding angular resolution, time-interpolation, and static nav- igational safety constraints are introduced. The deviations of the graph-search results relative to the solution of the exact differential equations in both the path duration and length are assessed. They are found to be of the order of the discretization errors, with VISIR\u2019s solution converging to that of the differential equation for sufficient resolution

Estimation of population mean using co-efficient of variation and median of an auxiliary variable

Abstract The present paper deals with a class of modified rat io estimators for estimation of population mean of the study variable using the linear comb ination of the known values of the Co-efficient of Variation and the Median of the auxiliary variable. The biases and the mean squared errors of the proposed estimators are derived and are compared with that of existing modified ratio estimators. Further we have also derived the conditions for which the proposed estimators perform better than the existing modified rat io estimators. The performances of the proposed estimators are also assessed with that of the existing estimators for certain natural populations. From the numerical study it is observed that the proposed modified ratio estimators perform better than the existing modified rat io estimators

Estimation of population mean using known median and co-efficient of skewness

Abstract The present paper deals with two modified ratio estimators for estimation of population mean of the study variable using the linear co mbination of the known population values of the Median and the Co-efficient of Skewness of the auxiliary variab le. The biases and the mean squared errors of the proposed estimators are derived and are compared with that of existing modified ratio estimators for certain natural populations. Further we have also derived the conditions for which the proposed estimators perform better than the existing mod ified rat io estimators. Fro m the emp irical study it is also observed that the proposed modified ratio estimators perform better than the existing modified ratio estimators