scholarly journals Prediction for rice yield using data mining approach in Ranga Reddy district of Telangana, India

2021 ◽  
Vol 23 (2) ◽  
pp. 242-248
Author(s):  
BABY AKULA ◽  
R.S.PARMAR ◽  
M. P. RAJ ◽  
K. INDUDHAR REDDY
Keyword(s):  
Data Mining ◽  
Logistic Model ◽  
Yield Data ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Rice Yields ◽  
Using Data ◽  
Mlp Classifier ◽  
Mining Algorithms ◽  
Best Fit

In order to explore the possibility of crop estimation, data mining approach being multidisciplinary was followed. The district of Ranga Reddy, Telangana State, India has been chosen for the study and its year wise average yield data of rice and daily weather over a period of 31 years i.e. from 1988-2019 (30th to 47th Standard Meteorological Weeks). Data mining tool WEKA (V3.8.1). Min- Max Normalization technique followed by Feature Selection algorithm, ‘cfsSubsetEval’ was also adopted to improve quality and accuracy of data mining algorithms. Thus, after cleaning and sorting of data, five classifiers viz., Logistic, MLP (Multi Layer Perceptron), J48 Classifier, LMT (Logistic Model Trees) and PART Classifier were employed over the trained data. The results indicated that the function based and tree based models have better performance over rule based model. In case of function based two models examined, viz., Logistic and MLP, the later performed better over Logistic model. Between tree based two models, LMT performed better over J48. Thus, MLP classifier model found to be the best fit model in predicting rice yields as it recorded an accuracy of 74.19 %, sensitivity of 0.742 and precision of 0.743 as compared with other models. The MLP has also achieved the highest F1 score of (0.742) and MCC (0.581).

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

Analysis of the Samples with an Unknown Matrix Using Data Mining Algorithms

2017 ◽  
Vol 53 (14) ◽  
pp. 1454-1457
Author(s):  
E. I. Molchanova ◽  
E. N. Korzhova ◽  
T. V. Stepanova ◽  
V. V. Kuz’min
Keyword(s):  
Data Mining ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

A Data Mining Approach for Generation of Control Signatures

2002 ◽  
Vol 124 (4) ◽  
pp. 923-926 ◽  
Author(s):  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Metal Forming ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Forming Process ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Metal Forming Process ◽  
Material Forming ◽  
Mining Algorithms

Data mining offers methodologies and tools for data analysis, discovery of new knowledge, and autonomous process control. This paper introduces basic data mining algorithms. An approach based on rough set theory is used to derive associations among control parameters and the product quality in the form of decision rules. The model presented in the paper produces control signatures leading to good quality products of a metal forming process. The computational results reported in the paper indicate that data mining opens a new avenue for decision-making in material forming industry.

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