Search Results - (( parallel classification learning algorithm ) OR ( using rectification using algorithm ))
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1
The forecasting of poverty using the ensemble learning classification methods
Published 2023“…This research was conducted to forecast poverty using classification methods. Random Forest and Extreme Gradient Boosting (XGBoost) algorithms were applied to forecast poverty since they are supervised learning algorithms that use the ensemble learning approach for classification. …”
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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Robust tweets classification using arithmetic optimization with deep learning for sustainable urban living
Published 2024“…In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. …”
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Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Combining deep and handcrafted image features for MRI brain scan classification
Published 2019“…In this paper, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. …”
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Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
Published 2019“…The incorporation of a single parallel hidden layer feed-forward neural network to the Fast Learning Network (FLN) architecture gave rise to the improved Extreme Learning Machine (ELM). …”
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8
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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Graph-based conflict rectification using stroke gesture approach in timetabling system (CORECTS)
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Investigation And Development Of Flattening Algorithms For Curved Latent Fingerprint Images
Published 2015“…Boundary of interested area can be acquired using a predefined algorithm or define by user using interactive drawing. …”
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OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2009“…Abstract This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a DC–DC converter with synchronous rectification normally used in battery charge/discharge circuits in DC uninterruptible power supply systems. …”
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OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2009“…This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a DC–DC converter with synchronous rectification normally used in battery charge/discharge circuits in DC uninterruptible power supply systems. …”
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13
A Practical Method for Camera Calibration in Stereo Vision Mobile Robot Navigation
Published 2012“…The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software.…”
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A Pixel to Pixel Correspondence and Region of Interest in Stereo Vision Application
Published 2012“…The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software. …”
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Simulation and design of A DC-DC synchronous converter by intelligent optimization techniques
Published 2010“…The design and analysis of the converter with the Genetic Algorithm are performed using software tools within the MATLAB/ SIMULINK environment. …”
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An Analysis Of Multiple Size Region Of Interest In Disparity Mapping For Stereo Vision Application
Published 2011“…The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software. …”
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A Pixel Matching Process And Multiples Roi For Stereo Images In Stereo Vision Application
Published 2011“…The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software. …”
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Biceps brachii surface EMG classification using neural networks
Published 2012“…The acquisition module is used to acquire EMG signals from the subject. Several signal processing methods are carried out in the preprocessing module, where the EMG signal will undergo a series of processes like filtering, rectification and integration. …”
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Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
Published 2011“…ANNs are particularly useful for complex pattern recognition and classification tasks. The capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. …”
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