Search Results - (( data classification learning algorithm ) OR ( variable detection based algorithm ))
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1
Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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2
Gas Identi cation by Using a Cluster-k-Nearest-Neighbor
Published 2009“…Therefore for that reason we develop another algorithm for clustering space which contributes a higher accuracy compares to K-means cluster with less subclass number, higher stability and bounded time of classification with respect to the variable data size. …”
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3
Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine
Published 2022“…The present study investigates the accuracy of different machine learning classification algorithms with three different data smoothing techniques for gas turbine fault detection and isolation task. …”
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4
Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine
Published 2022“…The present study investigates the accuracy of different machine learning classification algorithms with three different data smoothing techniques for gas turbine fault detection and isolation task. …”
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5
Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm
Published 2015“…These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). …”
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6
Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
Published 2023“…The primary goal is to use the Random Forest algorithm, an effective machine learning technique, to predict instances of cheating based on the behavioural patterns of participants. …”
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Final Year Project Report / IMRAD -
7
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The neural network learns the rough set’s upper and lower approximations as feature extractors simultaneously with classification. …”
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8
Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: a systematic review
Published 2021“…Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML-based image interpretation of rs-fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.…”
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Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi
Published 2022“…In this research, Object-Based Image Analysis (OBIA) method was applied on hyperspectral data to extract the crown of individual tree species for classification and estimation purposes. …”
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10
Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease
Published 2021“…As the ALOS PALSAR-2 image was evaluated with dual-polarization (HH and HV), each digitized point has two distinct backscatter data with four severity levels (T0 to T3). The machine learning algorithm consistently performs well when presented with a well-balanced dataset. …”
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11
Case Slicing Technique for Feature Selection
Published 2004“…One of the problems addressed by machine learning is data classification. Finding a good classification algorithm is an important component of many data mining projects. …”
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12
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. …”
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13
Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…In data mining, classification learning is broadly categorized into two categories; supervised and unsupervised. …”
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14
Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…Feature selection and classification are widely utilized for data analysis. …”
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15
Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach
Published 2009“…The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. …”
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16
Evaluation of multiple In Situ and remote sensing system for early detection of Ganoderma boninense infected oil palm
Published 2018“…In the next phase, the SVM classifier was trained to achieve the best classification using training data and test data integrated with selected features. …”
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17
New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Two different models for learning in data sets were proposed based on two different reduction algorithms. …”
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18
Performances of machine learning algorithms for binary classification of network anomaly detection system
Published 2018“…Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. …”
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Conference or Workshop Item -
19
Comparative study of machine learning algorithms in data classification
Published 2025“…This research conducts a comparative study of various machine learning algorithms for dataset classification to identify the most accurate and reliable classifier. …”
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Final Year Project / Dissertation / Thesis -
20
Evaluation of the Transfer Learning Models in Wafer Defects Classification
Published 2022“…Transfer Learning is one of the common methods. Various algorithms under Transfer Learning had been developed for different applications. …”
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Conference or Workshop Item