Search Results - (( java segmentation using algorithm ) OR ( missing rules machine algorithm ))
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1
Enhanced mechanism to handle missing data of Hadith classifier
Published 2011“…The correct branch to take is unknown if a feature tested is missing, and the algorithm must employed enhanced mechanisms to handle missing values. …”
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2
An Apriori-based Data Analysis on Suspicious Network Event Recognition
Published 2019“…Then, each missing value in the test data set is decided by using the obtained rules. …”
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3
Deep-learning-based detection of missing road lane markings using YOLOv5 algorithm
Published 2021“…With the recent technological advancement, especially in machine vision and artificial intelligence, automated or semi-automated missing road lane marking detection systems can potentially be developed. …”
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4
Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…Total rules number, rules length and rules accuracy for the generation rules are recorded. …”
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5
Image clustering comparison of two color segmentation techniques
Published 2010“…Finally, the algorithm found, which would solve the image segmentation problem.…”
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6
Automatic Number Plate Recognition on android platform: With some Java code excerpts
Published 2016“…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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7
Development of seven segment display recognition using TensorFlow on Raspberry Pi
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8
Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems
Published 2021“…Another problematic aspect is related to the intrusion detection solutions that are based on machine learning cluster algorithms to learn systems’ specifications and extract general state-based rules for attacks identification. …”
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9
An improved diabetes risk prediction framework : An Indonesian case study
Published 2018“…Pre-processing resolves the issue of missing data and hence normalizes the data.Outlier treatment employs k-mean clustering to validate the class.Suitable components were selected through comparison of classifier algorithms and feature selection.Attribute weighting based feature selection was selected for assigning weightage.Weighted risk factor was used on training dataset in order to improve accuracy and computation time of the prediction. …”
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