Search Results - (( java application mining algorithm ) OR ( using data machine algorithm ))
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…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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Direct approach for mining association rules from structured XML data
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…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
Published 2007“…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
Published 2022“…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
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Prediction of Machine Failure by Using Machine Learning Algorithm
Published 2019“…Then, the data is cluster by using K Means to produce labeled input that will be trained by using Gradient Boosting Machine, a decision tree algorithm to make prediction. …”
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process
Published 2004“…The most widely used source of such data is the Machining Data Handbook (MDH) published by Metcut Research Association, (1980). …”
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Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
Published 2019“…Landsat 8 images of resolutions 15 x 15 m were used and classified via machine learning and non-machine learning algorithms. …”
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Octane number prediction for gasoline blends using convolution neural network / Zhu Yue
Published 2021“…Machine performance learning models depend to a large extant to the data quality used train the model. …”
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12
Comparative study of machine learning algorithms in data classification
Published 2025“…The results will help with real-world data mining applications of machine learning and be a useful guide for further study and practical applications.…”
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Comparison of malware detection model using supervised machine learning algorithms / Syamir Mohd Shahirudin
Published 2022“…Then, the outcomes demonstrated that the best classifier for categorizing our data with 0.96% accuracy is the Decision Tree machine learning algorithm. …”
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Student Project -
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Performances of machine learning algorithms for binary classification of network anomaly detection system
Published 2018“…The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. …”
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Published 2023“…In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. …”
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Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
Published 2018“…The experiment shows that although the algorithm is stable and suitable for multiple domains, the imbalanced data distribution still manages to affect the outcome of the conventional machine learning algorithms.…”
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A comparative analysis of predicting heart disease using machine learning algorithms
Published 2026“…The purpose of this study is to investigate the potential of machine learning algorithms in forecasting heart disease and compare the performance of each algorithm. …”
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Privacy Preserving Features Selection for Data Mining using Machine Learning Algorithms
Published 2023Conference Paper -
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Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms
Published 2022“…Data resampling technique (SMOTETomek) is applied and shows further improvement on the accuracy of predictions by machine learning algorithms when deal with imbalance data. …”
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