Analyzing the real time electricity data using data mining techniques

2017 ◽  
Author(s):  
Aravindh Aki ◽  
D Krishna Mohan Reddy ◽  
Y Koushik Reddy ◽  
C. R. Kavitha ◽  
T. Sasikala
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Data Mining Techniques ◽  
The Real ◽  
Using Data

Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasim Ansari ◽  
Hossein Vakilimofrad ◽  
Muharram Mansoorizadeh ◽  
Mohamad Reza Amiri
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Recommender Systems ◽  
High Efficiency ◽  
Good Opportunity ◽  
Content Type ◽  
Data Mining Techniques ◽  
The Real ◽  
Using Data ◽  
Delay Prediction

Purpose This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques. Design/methodology/approach The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed. Findings The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty. Practical implications The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users. Originality/value The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.

Real-time reservoir operation using data mining techniques

2018 ◽  
Vol 190 (10) ◽  
Author(s):  
Omid Bozorg-Haddad ◽  
Mahyar Aboutalebi ◽  
Parisa-Sadat Ashofteh ◽  
Hugo A. Loáiciga
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Reservoir Operation ◽  
Data Mining Techniques ◽  
Using Data

Survey of Data Mining Techniques Used for Real Time Churn Prediction

2017 ◽  
Vol 7 (3) ◽  
pp. 219-222 ◽  
Author(s):  
Jean Claude Turiho ◽  
◽  
Wilson Cheruiyot ◽  
Anne Kibe ◽  
Irénée Mungwarakarama ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Churn Prediction ◽  
Data Mining Techniques

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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