scholarly journals Effective Data Mining Technique for Classification Cancers via Mutations in Gene using Neural Network

2016 ◽  
Vol 7 (7) ◽  
Author(s):  
Ayad Ghany ◽  
Dina Yousif
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Mining Technique ◽  
Mining Technique

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 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.

scholarly journals Heart disease forecast using neural network data mining technique

2017 ◽  
Vol 25 (1) ◽  
pp. 20-32
Author(s):  
Maryam Kazemi ◽  
Hossein Mehdizadeh ◽  
Ardeshir Shiri ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Network Data ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Disease Forecast
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