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Classification of herbs plant diseases via hierarchical dynamic artificial neural network
Published 2010“…This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. …”
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2
Neural network paradigm for classification of defects on PCB
Published 2003“…The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed based on binary morphological image processing and Learning Vector Quantization (LVQ) neural network. …”
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Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework
Published 2011“…This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. …”
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4
Recent Advances in Classification of Brain Tumor from MR Images – State of the Art Review from 2017 to 2021
Published 2022“…Results: In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. …”
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Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification
Published 2014“…In this work, we are interested by the employment of 3D facial surface normals (3DFSN) to classify six basic facial expressions and the proposed approach was employed on the Bosphorus database. …”
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6
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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7
Non-invasive gliomas grading using swarm intelligence algorithm / Muhammad Harith Ramli
Published 2017“…Bat algorithm is chosen for the development of the prototype for segmentation and classification purpose. …”
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8
Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
Published 2019“…The approach was tested on the KDD Cup99 intrusion detection dataset and the results proved the proposed PSO-RKFLN as an accurate, reliable, and effective classification algorithm.…”
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9
Improved normalization and standardization techniques for higher purity in K-means clustering
Published 2016“…The K-means algorithm is a famous and fast technique in non-hierarchical cluster algorithms. …”
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Power line faults classification by neural network train by Ant Colony Optimization
Published 2017“…This proposal concentrates on arrangement of electrical cable issues. …”
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Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza
Published 2021“…Thus, the proposed BrC detection and classification models can assist doctors to serve on the basis of the second opinion for early diagnosis of BrC. …”
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12
Integrated artificial intelligence-based classification approach for prediction of acute coronary syndrome
Published 2014“…In the development of the “hybrid AI-based” classification models, the proposed model (K1-K2- NN), was basically introduced through combining AI approaches of modified K-NN, genetic algorithm (GA), Fisher’s discriminant ratio (FDR) and class separability criteria (CSC). …”
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A Novel Method for Fashion Clothing Image Classification Based on Deep Learning
Published 2023“…In actual experiments, the classification accuracy of the suggested method was 93 percent, 4.6 percent higher than that of the basic CNN model. …”
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14
Image Splicing Detection With Constrained Convolutional Neural Network
Published 2019“…Then the proposed CNN is recasted for block-wise splicing localization operation. …”
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15
An ensemble feature selection method to detect web spam
Published 2018“…In addition, it improves classification metrics in comparison to basic feature selection methods.…”
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A modified reweighted fast consistent and high-breakdown estimator for high-dimensional datasets
Published 2024“…The basic idea of our proposed method is to modify the Mahalanobis distance so that it uses only the diagonal elements of the scatter matrix in the computation of the RFCH algorithm. …”
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Satellite Image Segmentation Using Thresholding Technique
Published 2017“…Image segmentation is one of the basic techniques of image processing and computer vision. …”
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Modelling of clinical risk groups (CRGs) classification using FAM
Published 2006“…FAM is a fast learning algorithm and used less epoch training [4]. Based on its performance in doing the classification, FAM is theoretically suitable to do the CRGs classification. …”
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Raspberry Pi-Based Finger Vein Recognition System Using PCANet
Published 2018“…An enhancement version for kNN algorithm, k-General Nearest Neighbours (kGNN) have been proposed at initial stage. …”
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