Search Results - (( model selection ((tree algorithm) OR (bat algorithm)) ) OR ( image classification _ algorithm ))
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1
Combining object-based classification and data mining algorithm to classify urban surface materials from worldview-2 satellite image
Published 2014“…In this study, Data Mining was performed using C4.5 algorithm to select the appropriate attributes for object-based classification. …”
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2
Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images
Published 2014“…A large number of attributes were discovered with the C4.5 DM algorithm, which also generated the classification model as a decision tree. …”
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Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image
Published 2016“…In this research, most of satisfactory results achieved from the generic model and proves it can be easily performed to different WorldView-2 images from different areas and provided the high accuracy through algorithms for tree species detection namely, Mesua Ferrea, Samanea Saman, and Casuarina Sumatrana without using any training data. …”
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4
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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5
The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification
Published 2015“…With the explosive growth of methods in this research area, this paper proposes 5 processing steps before image annotation using Amazon dataset, i.e., image segmentation, object identification, feature extraction, feature selection and image features classification. …”
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Imaging spectroscopy and light detection and ranging data fusion for urban features extraction
Published 2013“…The composite image was classified using unsupervised classification (k-mean algorithm) and the accuracy of the classification assessed. …”
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A hybrid deep CNN model for fast class-incremental food classification / Aymen Taher Ahmed al-Ashwal
Published 2019“…Features are then enhanced by using Tree-based feature selection to reduce the size of each feature and, therefore, enhance classification performance. …”
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8
Integration of object-based image analysis and data mining techniques for detailes urban mapping using remote sensing
Published 2015“…This algorithm represents the decision tree knowledge model, enables fast classification of intra-urban classes, and disables subjectivities related to the interaction with analysts. …”
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9
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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10
Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza
Published 2021“…On the other hand, BrT classification is aimed to develop an efficient and reliable model namely Biopsy Microscopic Image Cancer Network (BMIC-Net) to classify Hp images into eight subtypes of BrT through a DL-based hierarchical classification approach. …”
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11
Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi
Published 2022“…While the best predicted selection using RF is model 4 with mtry = p produced R2= 0.997 and RMSE=30.653kg/tree. …”
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12
Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation
Published 2025“…A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. …”
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Mixed waste classification based on vision inspection / Hassan Mehmood Khan
Published 2022“…The best 17 resulting features were used for the next process. Four classification algorithms specifically the Cubic SVM (C.SVM), Quadratic SVM (Q.SVM), Ensemble Bagged Trees (EBT) and k-Nearest Neighbor (kNN) are employed to test the classification accuracy. …”
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14
Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method
Published 2024Subjects:Conference Paper -
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Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine
Published 2022“…Classification is an essential task for many applications, including text classification, image classification, data classification, and so on. …”
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Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine
Published 2022“…Classification is an essential task for many applications, including text classification, image classification, data classification, and so on. …”
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Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
Published 2023“…The best performance obtained using Savitzky-Golay smoothing and LDA algorithms after the wavelength selection with the accuracy reported of 0.90. …”
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18
Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
Published 2025Subjects:Conference paper -
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Artificial intelligence system for pineapple variety classification and its quality evaluation during storage using infrared thermal imaging
Published 2022“…Several machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, support vector machine, decision tree, and Naïve Bayes were applied for the classification of pineapple varieties. …”
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20
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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