Search Results - (( _ evaluation step algorithm ) OR ( based classification using algorithmic ))*
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Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC)
Published 2015“…The algorithm involved three main steps, filtration, recognition and detection.Artificial Bee Colony, Fuzzy Logic, Otsu and Geometry formula were incorporated to achieve the goal.Ninety-four leaf images were used in this algorithm combination experiment.The study was conducted in four phases, filtration, recognition, detection and evaluation.Comparison was made with four other algorithms, Otsu, Canny, Robert and Sobel. …”
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2
Autism Spectrum Disorder Classification Using Deep Learning
Published 2021“…Finally, the effectiveness of the algorithm is evaluated based on the accuracy performance. …”
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3
Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…The study verified that the FID3-DBD algorithm could classify the continuous data, and the BFID3-DBD algorithm overcame the overfitting issue, reduced high variance, and increased test data classification accuracy.…”
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4
Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…This proposed classifier achieved 97.9% classification accuracy on the ISIC dataset. In the third classification algorithm, hybrid features are extracted using AlexNet and VGG-16 through a transfer learning approach where parameter manipulation is implemented to simplify the network. …”
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5
Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
Published 2024“…The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. …”
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6
A new ant based rule extraction algorithm for web classification
Published 2011“…Methods to reduce the number of attributes and discretization are two important data pre-processing steps before the data can be used for classification activity. …”
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7
Improved building roof type classification using correlation-based feature selection and gain ratio algorithms
Published 2017“…First, the feature importance was evaluated using gain ratio algorithm, and the result was ranked, leading to selection of the optimal feature subset. …”
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An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks
Published 2020“…Many algorithms have been developed for automated electrocardiogram (ECG) classification. …”
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Detection and classification of conflict flows in SDN using machine learning algorithms
Published 2021“…Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. …”
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10
Improved cuckoo search based neural network learning algorithms for data classification
Published 2014“…Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. …”
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11
Genetic algorithm based ensemble framework for sentiment analysis
Published 2018“…Machine Learning classification is commonly used in sentiment analysis and it requires plain text documents to be transformed to analyzable data through feature extraction and selection. …”
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12
Hybrid intelligent approach for network intrusion detection
Published 2015“…Clustering is the last step of processing before classification has been performed, using k-means algorithm. …”
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13
RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights
Published 2024journal::journal article -
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Analyzing CT images for detecting lung cancer by applying the computational intelligence-based optimization techniques
Published 2022“…Afterward, various statistical features are derived, and the Supervised Jaya Optimized Rough Set related Feature Selection (SJORSFS) process is used to select the lung features. Finally, the lung cancer is identified using Autoencoder based Recurrent Neural Network (ARNN) classification algorithm, successfully recognizing the lung cancer features. …”
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15
Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel
Published 2024“…The proposed algorithms that outperformed the state-of-the-art algorithms contributes to diagnosing early-stage Melanoma. …”
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16
Image classification of Aedes mosquitoes using transfer learning / Zetty Ilham Abdullah
Published 2021“…The architecture is also evaluated based on the performance produced by experiments conducted using different combination of hyperparameters. …”
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17
Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
Published 2021“…Two public online datasets of fundus image, e-Ophtha and DIARETDB1 are used to evaluate the performance of this system. …”
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18
Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image
Published 2016“…This study also explores the use and comparison of object-based classification, and two common pixel-based classification methods namely, maximum likelihood and support vector machines based on WorldView-2 satellite imagery to evaluate the potential of the object-based in compare to pixel-based to detect urban tree species. …”
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19
Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…Thus, TC suggested being the main step in data pre-processing for mountainous terrain before the RBF-based SVM classification process. …”
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20
Deep learning based emotion recognition for image and video signals: matlab implementation
Published 2021“…Evaluating results using confusion matrix, accuracy, F1-score, precision, and recall shows that video-based datasets obtained more promising results than image-based datasets.…”
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