Search Results - (( shape classification modeling algorithm ) OR ( java application learning algorithm ))
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…Experimental results show that the developed methods and model are able to classify the Harumanis quality with accuracy of 79% using fuzzy classification based on shape and size.…”
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Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)
Published 2018“…Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning
Published 2016“…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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Behavioural features for mushroom classification
Published 2018“…The classification accuracy, coefficient metric, and time taken to build a classification model on a standard Mushroom dataset were measured. …”
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Classification of Citrus (Rutaceae) by Using Image Processing
Published 2019“…A machine learning algorithms, SVM have been used to build species identification models. …”
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Undergraduate Final Project Report -
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Classification of brain tumors: using deep transfer learning
Published 2023“…To achieve the goal, a modified GoogleNet model was used. Various learning algorithms were tested. …”
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Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…The performance of algorithms was measured based on classification accuracy, error rate, and precision and recall. …”
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Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Several simulation experiments and analytical models have been presented with respect to traffic classification environments and scenarios. …”
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Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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Improving Brain MR Image Classification for Tumor Segmentation using Phase Congruency
Published 2018“…The tumor is segmented using Fuzzy C-mean and reconstructed tumor 3D model to measure the volume, location and shape accurately. …”
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An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks
Published 2013“…This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works
Published 2025Conference paper -
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Comparison of fully automated and semi-automated methods for species identification
Published 2018“…In semi-automated models, a classification accuracy of 96.67 % was obtained using the k-nearest neighbour and 97.5 % using the artificial neural network, whereas in fully automated models, a classification accuracy of 90 % was obtained using the k-nearest neighbour and 98.8 % using the artificial neural network. …”
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Automated Detection and Classification of Retinal Vein Occlusion Using Ultra-widefield Retinal Fundus Images and Transfer Learning
Published 2024“…The approach seeks to utilise knowledge from pretraining to enhance the performance of the segmentation model. The study also evaluates the classification model trained with lesion masks to classify images accurately into the respective categories. …”
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