scholarly journals Detection of dengue disease using artificial neural network based classification technique

2017 ◽  
Vol 7 (1.3) ◽  
pp. 13 ◽  
Author(s):  
K. Balasaravanan ◽  
M. Prakash
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Mining Technique ◽  
Human Environment ◽  
Dengue Disease ◽  
Mining Technique ◽  
Classification Technique ◽  
Vector Borne ◽  
Cart Algorithm

The information about the patients can be maintained with clinical documents. By keeping huge volume of clinical documents we can easily predict the occurrence of any disease in the patients. Dengue is considered to be one of the vital disease which are spreading in more than 110 countries. It is a vector borne disease caused by the mosquito’s of female Aedes Albopictus and Aedes Aegypti which are well suited human environment. We have implemented a data mining technique called ANN which is a well-known technique for classification of data used here to classify diseases. We have analyzed the patients’ dataset for the occurrence of dengue and experimented with Weka and Netbeans IDE and the result is proved to be more accurate than the CART algorithm. 

scholarly journals Techniques for Image Segmentation: A Critical Review

2021 ◽  
Vol 9 (3) ◽  
pp. 1-4
Author(s):  
Harshita Mishra ◽  
Anuradha Misra
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Image Segmentation ◽  
Digital Image ◽  
Electronic Devices ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Mathematical Algorithms ◽  
Picture Element

In today’s world there is requirement of some techniques or methods that will be helpful for retrieval of the information from the images. Information those are important for finding solution to the problems in the present time are needed. In this review we will study the processing involved in the digitalization of the image. The set or proper array of the pixels that is also called as picture element is known as image. The positioning of these pixels is in matrix which is formed in columns and rows. The image undergoes the process of digitalization by which a digital image is formed. This process of digitalization is called digital image processing of the image (D.I.P). Electronic devices as such computers are used for the processing of the image into digital image. There are various techniques that are used for image segmentation process. In this review we will also try to understand the involvement of data mining for the extraction of the information from the image. The process of the identifying patterns in the large stored data with the help of statistic and mathematical algorithms is data mining. The pixel wise classification of the image segmentation uses data mining technique.

Classification of environmental factors potentially motivating for dairy cows to access shade

2021 ◽  
pp. 1-4
Author(s):  
Matheus Deniz ◽  
Karolini Tenffen de Sousa ◽  
Isabelle Cordova Gomes ◽  
Marcos Martinez do Vale ◽  
João Ricardo Dittrich
Keyword(s):  
Data Mining ◽  
Environmental Factors ◽  
Dairy Cows ◽  
Thermal Environment ◽  
Data Mining Technique ◽  
Machine Learning Technique ◽  
Mean Values ◽  
Mining Technique ◽  
Learning Technique

Abstract The aim of this Research Communication was to apply the data mining technique to classify which environmental factors have the potential to motivate dairy cows to access natural shade. We defined two different areas at the silvopastoral system: shaded and sunny. Environmental factors and the frequency that dairy cows used each area were measured during four days, for 8 h each day. The shaded areas were the most used by dairy cows and presented the lowest mean values of all environmental factors. Solar radiation was the environmental factor with most potential to classify the dairy cow's decision to access shaded areas. Data mining is a machine learning technique with great potential to characterize the influence of the thermal environment in the cows' decision at the pasture.

scholarly journals Provissional Access For Improving Classification Accuracy On Diabetes Dataset

2019 ◽  
Vol 8 (6) ◽  
pp. 5245-5248
Keyword(s):  
Data Mining ◽  
Experimental System ◽  
Classification Algorithms ◽  
Data Mining Technique ◽  
Real World Data ◽  
Mining Technique ◽  
Complete Set ◽  
The Impact ◽  
Speed And Accuracy

Data mining helps to solve many problems in the area of medical diagnosis using real-world data. However, much of the data is unrealizable as it does not have desirable features and contains a lot of gaps and errors. A complete set of data is a prerequisite for precise grouping and classification of a dataset. Preprocessing is a data mining technique that transforms the unrefined dataset into reliable and useful data. It is used for resolving the issues and changes raw data for next level processing. Discretization is a necessary step for data preprocessing task. It reduces the large chunks of numeric values to a group of well-organized values. It offers remarkable improvements in speed and accuracy in classification. This paper investigates the impact of preprocessing on the classification process. This work implements three techniques such as NaiveBayes, Logistic Regression, and SVM to classify Diabetes dataset. The experimental system is validated using discretize techniques and various classification algorithms.

scholarly journals Energy Prediction of Wheat Production Using Data Mining Technique in Iran

2021 ◽  
Vol 34 (1) ◽  
pp. 14-27
Author(s):  
Nasim Monjezi
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Energy Consumption ◽  
Training Data ◽  
Data Mining Technique ◽  
Essential Information ◽  
Wheat Production ◽  
Energy Prediction ◽  
Mining Technique ◽  
Practical Algorithms

Wheat is considered as one of the most important products in Iran. Concerning high cultivation area of wheat in Khuzestan, an instrument is required to process stored data in order to give information resulted from such processing to managers of agricultural sectors. Data mining technique is able to give essential information and models to producers of wheat for modelling energy consumption. One of the most practical algorithms is an artificial neural network. The main aim of this research is to predict output energy of wheat farms using a multilayer perceptron neural network. This is an analytic research and its database consists of 1240 records. Data required for the research was obtained from wheat farm during 2014-2018. Data analysis was done via IBM SPSS modeller 14.2 and standard CRISP. Concerning the model used in the research, it was found that variables of chemical fertilizers, machinery & diesel fuel with coefficients of 0.2987, 0.2064 and 0.1527 respectively had the highest effect on output variable (productive energy). Amount of prediction precision in neural network algorithm, meaning ratio of correctly predicted records to total records was 93.08%. Also, linear correlation between actual values and predicted values were 0.92 and 0.88 respectively, for training data and testing data suggesting strong correlation.  The results obtained can be effective for wheat farmers in direction of evaluation and optimization of energy consumption in process of wheat production and reduction of consumption of energy inputs.

Sign in / Sign up

Export Citation Format

Share Document