Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning

Crop decision is a very complex process. In Agriculture it plays a vital role. Various biotic and abiotic factors affect this decision. Some crucial Environmental factors are Nitrogen Phosphorus, Potassium, pH, Temperature, Humidity, Rainfall. Machine Learning Algorithm can perfectly predict the crop necessary for this environmental condition. Various algorithms and model are used for this process such as feature selection, data cleaning, Training, and testing split etc. Algorithms such as Logistic regression, Decision Tree, Support vector machine, K- Nearest Neighbour, Navies Bayes, Random Forest. A comparison based on the accuracy parameter is presented in this paper along with various training and testing split for optimal choice of best algorithm. This comparison is done on two tools i.e., on Google collab using python and its libraries for implementation of Machine Learning Algorithm and WEKA which is a pre-processing tool to compare various algorithm of machine learning

Dislocation Dynamics for Plasticity Boundary Value Problems

Plastic deformation of crystalline metals mainly takes place by the nucleation and motion of dislocations, line defects in the atomic lattice. At a length scales of micrometers, dislocations can conveniently be idealized as discrete line defects in an elastic continuum and plastic deformation can be represented by the evolution of the dislocation structure. This mesoscopic description is commonly referred to as discrete dislocation plasticity. The focus in the present article is on the solution of initial/boundary value problems using discrete dislocation plasticity. A brief summary of the underlying dislocation dynamics is given, methods used to describe plasticity in terms of evolving dislocation structures are described, some results are presented that illustrate unique capabilities of discrete dislocation plasticity, and needs as well as opportunities for future development are indicated