Search Results - (( java application mining algorithm ) OR ( variable training toward algorithm ))

Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms by Lee, Quan

“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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Enhancing the entrepreneurial intention of the retiring military personnel through entrepreneurial training by Yusuf, Lamidi

“…It was also found that the entrepreneurial training moderated the relationship between two of the independent variables (entrepreneurial finance and physical facilities) and entrepreneurial intention. …”
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Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms by Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A.

“…Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results. ? …”

Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

“…The ANN model topology is designed using selection from the best training algorithm, transfer function, number of training runs (1000-5000), number of hidden layers (1-3) and nodes (5-15). …”
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An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

“…The research implements stock prediction analysis as a case study for training the neural network by adopting MGWO algorithm. …”
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A multi-nets ANN model for real-time performance-based automatic fault diagnosis of industrial gas turbine engines by Tahan, M., Muhammad, M., Abdul Karim, Z.A.

“…Two back-propagation training algorithms, namely the Levenberg–Marquardt and Bayesian regularization algorithms, and the k-fold cross-validation technique, were employed to train the optimal networks using a training data set. …”
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Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications by Shanmugam Y., Narayanamoorthi R., Ramachandaramurthy V.K., Bernat P., Shrestha N., Son J., Williamson S.S.

“…The learning algorithms consider variables such as core structure, cross-coupling effect, and coil flux pipe length. …”

Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling by N.S. Suhaimi, J. Teo, J. Mountstephens

“…Secondly, the data will then be tested and trained with KNN and SVM algorithms. We conduct subject-dependent as well as subject-independent classifications in order to compare intra-against inter-subject variability, respectively in VR EEG-based emotion modeling. …”
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Price prediction model of green building based on machine learning algorithms / Nur Syafiqah Jamil by Jamil, Nur Syafiqah

“…Meanwhile, experiments using five common algorithms, Random Forest Regressor Model outperforms four (4) other algorithms in predicting the price of green building condominium, by training and validating the data-set using Split approach. …”
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Bayesian network of influence of sociodemographic variables on dengue related knowledge, attitude, and practices in selected areas in Selangor, Malaysia by Ajibola, Lamidi-Sarumoh Alaba

“…Knowledge, attitude and practice Bayesian network model (KAPBNM) was used to visualize and quantify the pattern of KAP on dengue infection and vectors given the socio-demographic variables of the respondents. Quantifying the KAP dengue based socio-demographic variables from KAPBNM, individuals with primary school education training had low knowledge on dengue. …”
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An artificial neural network based analysis of factors controlling particle size in a virgin coconut oil-based nanoemulsion system containing copper peptide by Samson, Shazwani, Basri, Mahiran, Fard Masoumi, Hamid Reza, Abdul Malek, Emilia, Karjiban, Roghayeh Abedi

“…Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake. by Malek, S., Syed Ahmad, S. M., Singh, S. K., Milow, P., Salleh, A.

“…Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake. by Syed Ahmad Abdul Rahman, Sharifah Mumtazah, Malek, Sorayya, Kashmir Singh, Sarinder Kaur, Milow, Pozi, Salleh, Aishah

“…Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. …”
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Model Prediction Of Pm2.5 And Pm10 Using Machine Learning Approach by Hamid, Norfarhanah

“…The model was developed as a generic use where data pre-processing using two separate methods of calculating a correlation coefficient and variable importance in projection (VIP) scores managed to select significant input toward output for model development. …”
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