Search Results - (( parameter adaptation method algorithm ) OR ( using vectorization machine algorithm ))
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Easy to use remote sensing and GIS analysis for landslide risk assessment
Published 2018“…We discussed different type of algorithms and factors for modeling the prediction of landslide risk assessment such as SVM (support vector machine), DT (decision tree), ANFIS (adaptive neural-fuzzy inference system), AHP (analytic hierarchy process), ANN (artificial neural network), probability frequency of landslides occurrence factors model and empirical model. …”
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A new descriptor for smile classification based on cascade classifier in unconstrained scenarios
Published 2021“…We suggest a histogram-based cascade smile classification method utilizing different facial features. The candidate feature set was designed based on the first order histogram probability, and a cascade classifier with a variety of parameters was used at the classification stage. …”
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A New Method Of Speed Sensorless Control For Permanent Magnet Synchronous Motor
Published 2019“…Comparison with two other methods used in the adaptation from the previous researchers shows that MRAS based on TS-FIS is capable of detecting the rotor position efficiently, by giving 0 rad reading for rotor position error in steady state and transient conditions. …”
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Application of computational intelligence methods in modelling river flow prediction: A review
Published 2023Conference Paper -
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Classification of transient disturbance using Wavelet based support vector machine / Fahteem Hamamy Anuwar
Published 2012“…After the feature extraction stage, Support Vector Machine (SVM) is used to classify the transient disturbance waveforms. …”
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The influence of sentiments in digital currency prediction using hybrid sentiment-based Support Vector Machine with Whale Optimization Algorithm (SVMWOA)
Published 2021“…Support Vector Machine (SVM) technique is used with the Whale Optimization Algorithm (WOA) which is inspired by the swarm optimization algorithms. …”
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Proceeding Paper -
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Using the bees algorithm to optimise a support vector machine for wood defect classification
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Optimizing support vector machine parameters using continuous ant colony optimization
Published 2012“…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous parameters, there is a need to discretize the continuous value into a discrete value.This discretization process results in loss of some information and, hence, affects the classification accuracy and seek time.This study proposes an algorithm to optimize Support Vector Machine parameters using continuous Ant Colony Optimization without the need to discretize continuous values for Support Vector Machine parameters.Seven datasets from UCI were used to evaluate the performance of the proposed hybrid algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques.Experimental results of the proposed algorithm also show promising performance in terms of computational speed.…”
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Enhancing time series prediction with Hybrid AFSA-TCN: A unified approach to temporal data and optimization
Published 2025“…Time series data, with its sequential dependencies presents a unique challenge for traditional machine learning methods such as Random Forest (RF), Support Vector Machines (SVM), and Decision Trees (DT), which often struggle to capture temporal patterns effectively. …”
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Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr...
Published 2024“…For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. …”
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Landslide risk zoning using support vector machine algorithm
Published 2024“…The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. …”
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Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Published 2013“…Support Vector Machines are considered to be excellent patterns classification techniques. …”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm
Published 2023“…This paper presents an intelligent system to reduce Non Technical Loss (NTL) using hybrid Support Vector Machine (SVM) and Genetic Algorithm (GA). …”
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Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features
Published 2024journal::journal article -
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Face recognition using eigenfaces and smooth support vector machine
Published 2011“…Face is one of the unique features of human body which has complicated characteristic.Facial features (eyes, nose, and mouth) can be used for face recognition. Support Vector Machine (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning community. …”
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Undergraduates Project Papers -
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Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm
Published 2021“…The proposed model of support vector machine (SVM) with improved dragonfly algorithm(IDA) is used to forecast the PV power. …”
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Predicting uniaxial compressive strength using Support Vector Machine algorithm
Published 2019“…This paper presents the application of Support Vector Machine (SVM) algorithm to predict the UCS. …”
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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
Published 2014“…The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. …”
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