Search Results - (( _ classification based algorithm ) OR ( basic extraction method algorithm ))
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Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib
Published 2013“…Final result produced by the algorithm is 92.312% of average accuracy and the classification for the leaf was based on the leaf-shape information.…”
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2
Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…This reserarch adapted a methodology of computer vision and algorithms that exploit image segmentation, feature extraction and fuzzy classification to guide the research activities. …”
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3
Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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4
Disposable Biomimetic Array Sensor Strip Coupled With Chemometric Algorithm For Quality Assessment Of Orthosiphon Stamineus Benth Samples
Published 2006“…A disposable screen printed array sensor strip based on self-plasticized methacrylate acrylate PVC blend lipid membranes combined with chemometric algorithm has been developed and applied for qualitative and quantitative analysis of O.stamineus samples. …”
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5
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The RNN was used to detect patterns present in satellite image. A novel feature extraction algorithm was developed to extract the feature vectors. …”
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6
Early detection of spots high water saturation for landslide prediction using thermal imaging analysis
“…Therefore, after the ROI has been detected, feature extraction and classification must be performed. Statistical based featured namely minimum, maximum, mean and standard deviation were extracted from each image channels. …”
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Review of deep convolution neural network in image classification
Published 2017“…Then, the research status and development trend of convolution neural network model based on deep learning in image classification are reviewed, which is mainly introduced from the aspects of typical network structure construction, training method and performance. …”
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Article -
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Early detection of high water saturation spots for landslide prediction using thermal image analysis
Published 2018“…Therefore, after the ROI has been detected, feature extraction and classification must be performed. Statistical based features namely minimum, maximum, mean and standard deviation were extracted from each image channels. …”
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9
FEATURES EXTRACTION OF HEP-2 IMMUNOFLUORESCENCE PATTERNS BASED ON TEXTURE AND REGION OF INTEREST TECHNIQUES
Published 2013“…Image segmentation will be done after pre-processing completed to further enhance the image according to its edge or region to be used for the input image. Different methods of features extraction will be used and compared for better outcome. …”
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Final Year Project -
10
Prediction Of Antimicrobial Peptides Based On Sequence Alignment And Secondary Structure Sequence And Segment Sequence.pdf
Published 2015“…The sequence alignment is accomplished by the classification of test sequences based on the maximum high-scoring segment pairs (HSPs) score predicted by Basic Local Alignment Search Tool for protein (BLASTP). …”
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11
Real-time oil palm fruit bunch ripeness grading system using image processing techniques
Published 2013“…Image processing approaches, such as acquisition, pre-processing, segmentation,feature extraction, and classification as well as expert rule-based system, were developed to automate the ripeness grading for oil palm fruit bunches. …”
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12
An adaptive ant colony optimization algorithm for rule-based classification
Published 2020“…The algorithm’s performance was compared with other variants of Ant-Miner and state-of-the-art rules-based classification algorithms based on classification accuracy and model complexity. …”
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13
Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction
Published 2021“…Second, three multi-verse optimizer algorithm (MVOs), namely, basic MVO, modified MVO, hybrid MVO is proposed to solve the TDC problem; these algorithms are incremental improvements of the preceding versions. …”
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Case Slicing Technique for Feature Selection
Published 2004“…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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16
Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
Published 2024“…Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. …”
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Article -
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Agent-based extraction algorithm for computational problem solving
Published 2015“…The results show that the extraction algorithm has been able to extract 100 % of the information correctly. …”
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18
Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
Published 2023“…The performance of FESSIC was evaluated against ten benchmark image classification algorithms and six classifiers on four ground-based sky image datasets. …”
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Real-time Traffic Classification Algorithm Based on Hybrid of Signature Statistical and Port to Identify Internet Applications
Published 2014“…Based on certain priority rules, SSPC makes classification decisions for each flow. …”
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Conference or Workshop Item -
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Enhanced ontology-based text classification algorithm for structurally organized documents
Published 2015“…The third algorithm is the Ontology Based Text Classification (OBTC) and is designed to reduce the dimensionality of training sets. …”
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