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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“…The proposed method is compared with Support Vector Machine Optimized by Genetic Algorithm (SVMGA) and the Support Vector Machine Optimized by Harmony Search (SVMHS). …”
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Proceeding Paper -
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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Thesis -
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Feedforward neural network for solving particular fractional differential equations
Published 2024“…This research aims to develop a scheme based on a feedforward neural network (FNN) with a vectorized algorithm (FNNVA) for solving FDEs in the Caputo sense (FDEsC) using selected first-order optimization techniques: simple gradient descent (GD), momentum method (MM), and adaptive moment estimation method (Adam). …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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Short term traffic forecasting based on hybrid of firefly algorithm and least squares support vector machine
Published 2015“…The goal of an active traffic management is to manage congestion based on current and predicted traffic conditions.This can be achieved by utilizing traffic historical data to forecast the traffic flow which later supports travellers for a better journey planning.In this study, a new method that integrates Firefly algorithm (FA) with Least Squares Support Vector Machine (LSSVM) is proposed for short term traffic speed forecasting, which is later termed as FA-LSSVM.In particular, the Firefly algorithm which has the advantage in global search is used to optimize the hyper-parameters of LSSVM for efficient data training. …”
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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Thesis -
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Plant leaf recognition algorithm using ant colony-based feature extraction technique
Published 2013“…Therefore, it can be concluded that the proposed method is capable of a high rate of classification of various plant species.…”
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Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
Published 2020“…This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches. …”
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Article -
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Kinect-based human gait recognition using locally linear embedded and support vector machine
Published 2018“…Further, to validate the effectiveness of LLE as optimization technique, the proposed method is then compared with another two gait features namely the original gait features known as DG and optimization using Principal Component Analysis labeled as DG-PCA. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
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Thesis -
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Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set
Published 2022“…The algorithms involved were K-Nearest Neighbor (KNN), Naïve Bayers, J48, Support Vector Machine (SVM), Sequential Minimal Optimization (SMO) and Multilayer Perceptron (MLP). …”
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Final Year Project / Dissertation / Thesis -
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Automated plant classification system using a hybrid of shape and color features of the leaf
Published 2016“…Several approaches have been proposed using different features and classifiers. However, the majority of the existing methods either rely on large numbers of training samples or select certain leaves within a dataset to achieve high accuracy rates. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…In the OGC framework, the exhibited explorative search behavior of the Gravitational Clustering (GC) algorithm has been addressed by (i) eliminating the agent velocity accumulation, and (ii) integrating an initialization method of agents using variance and median to subrogate the exploration process. …”
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Enhanced computational methods for detection and interpretation of heart disease based on ensemble learning and autoencoder framework / Abdallah Osama Hamdan Abdellatif
Published 2024“…Tested across four datasets, CAVE-SPFHD surpasses state-of-the-art methods in f1-score, providing improved not only predictive performance but also critical interpretative insights using the SHapley Additive explanation (SHAP) algorithm. …”
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Thesis -
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Optimized intrusion detection mechanism using soft computing techniques
Published 2011“…The selecting of an appropriate number of principal components is a critical problem. So, Genetic Algorithm (GA) is used in the optimum selection of principal components instead of using traditional method. …”
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Article -
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Crossover and mutation operators of real coded genetic algorithms for global optimization problems
Published 2016“…The rationale behind developing algorithms using real encoding of chromosome representations is the limitations of binary encoding. …”
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Thesis
