scholarly journals A Novel Intelligent Recommendation Algorithm based on Web Data Mining Technique under the Background of Deep Neural Network

2016 ◽  
Vol 10 (2) ◽  
pp. 437-450 ◽  
Author(s):  
Changchun Yang ◽  
Jun Wang ◽  
Min Yuan ◽  
Chenyang Lei
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Deep Neural Network ◽  
Web Data ◽  
Data Mining Technique ◽  
Recommendation Algorithm ◽  
Web Data Mining ◽  
Mining Technique

scholarly journals Personalized web search on e-commerce using ontology based association mining

2017 ◽  
Vol 7 (1.1) ◽  
pp. 286
Author(s):  
B. Sekhar Babu ◽  
P. Lakshmi Prasanna ◽  
P. Vidyullatha
Keyword(s):  
Data Mining ◽  
Web Search ◽  
Large Data ◽  
Association Mining ◽  
Data Sets ◽  
Data Mining Algorithm ◽  
Web Data ◽  
Data Mining Technique ◽  
Web Data Mining ◽  
The Web

 In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.

Research on E-Commerce Promotion System Based on Web Data Mining Technology

2014 ◽  
Vol 686 ◽  
pp. 311-315
Author(s):  
Guang Biao Deng
Keyword(s):  
Data Mining ◽  
Static Model ◽  
Current User ◽  
Site Management ◽  
Web Data ◽  
Recommendation Algorithm ◽  
Web Data Mining ◽  
Management Personnel ◽  
Promotion System ◽  
User Recommendation

This paper describes the use of Web data mining, and analyze the data on the web site (including the server log, commercial database, user database, the shopping cart, user mode) that access to relevant knowledge for goods, commodities such as preference relations. Secondly, the static model of the data mining methods, it is a manifestation of the site management personnel marketing thought. Based on these models, the paper proposed strategy for the site registered users, and produces the corresponding calculating formulas of a good recommendation and the corresponding recommendation algorithm for the current user, thus to get a user recommendation.

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.

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