International Journal of Computer and Applications

In data mining classification is to accurately predict the target class for each case in the data. Decision tree algorithm is one of the commonly used classification algorithm to make induction learning based on examples. In this paper we present the comparison of different classification techniques using WEKA. The aim of this paper is to investigate the performance of different classification methods on clinical data. The algorithm tested are Bayes Network, Navie bayes, Logistic, rule jrip, and J48.

Survey on Classification Methods using WEKA

In the current era of data-intensive services, the handling of big data is a crucial issue that affects almost every discipline and industry. In this study, we propose a classification method for large volumes of numeric data, which is implemented in a distributed programming framework, i.e., MapReduce. The proposed method partitions the data space into a grid structure and it then models the probability distributions of classes for grid cells by collecting sufficient statistics using distributed MapReduce tasks. The class labeling of new data is achieved by k-nearest neighbor classification based on Bayesian inference.

Big Numeric Data Classification Using Grid-based Bayesian Inference in the MapReduce Framework

The changing lifestyle, food habits and other bad habits like smoking, drinking has lead people to heart attack at young age. This paper aims in predicting the likelihood of cardiac disease in people based on the attributes such as the age, sex, cp, trestbps, chol, fbs, restecg, thalac, exang, oldpeak, slope, ca, thal. The data of existing patients within fifty years of age has to be collected and trained. Classification algorithms like Naive Bayes algorithms is applied to develop a model. This model will form a basis for predicting the chances of occurrence of CVD. If a person's data are given the classifier will predict if he is liable to CVD or not. The output is a binary classification which will give 1 or 0; 1 being the chances to occur and 0 being the chances not to occur.

Predicting the chances of occurrence of Cardio Vascular Disease (CVD) in people using classification techniques within fifty years of age

Deep learning field is helping world by providing solution to various disasters, one such disaster is forest fire In our current work we are using deep learning algorithm Region-Based Convolutional Neural Network(RCNN) to predict forest fires in images. The purpose of using such system is to construct a model for early wild fire detection and further helping in damage control caused by such instances Forest fires can be detected through various ways like sensor detection and geological data analysis in real time. But one of the best ways to detect fire is using image classification in which Deep learning is the most effective solution. By using deep learning techniques these response systems can be added or configured with drones so that images can be clicked easily form the sky on regular basis and detect smoke in dense forest and alert the authorities so that action can be taken as fast as possible. In our model we focused on fire detection using CNN and RCNN method and results are achieved in terms of accuracy. It is observed that RCNN has achieved an accuracy of 0.97 as compared to 0.92 accuracy of CNN at 20 epochs. Thus such deep learning based model can be a solution for handling disastrous situations.

RCNN Architecture for Forest Fire Detection

Data classification is the categorization of data for its most effective and efficient use. Data can be classified according to any criteria, not only relative importance or frequency of use. Classification can help an organization to meet legal and regulatory requirements for retrieving specific information within a set timeframe, and this is often the motivation behind implementing data classification methods and algorithms. The paper contains brief discussion of various classification methods that includes decision trees, K-nearest neighbor classifier, naïve bayes classifier and neural network. The paper also discusses some applications of classification model and at the end the paper is concluded with the brief observations of these classification models.

https://research.ijcaonline.org/volume53/number17/pxc3882213.pdf

Geetika

Papers

A Survey of Classification Methods and its Applications