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Rao-SVM machine learning algorithm for intrusion detection system
Published 2020“…Most of the intrusion detection systems are developed based on optimization algorithms as a result of the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in the performance of the human-based methods in terms of their training time and classification accuracy. …”
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…The aim of this work is to develop an improved optimization method for IDS that can be efficient and effective in subset feature selection and parameters optimization. …”
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Extending the decomposition algorithm for support vector machines training
Published 2003“…Numerical problems will cause the training to give non- optimal decision boundaries. Using a conventional optimizer to train SVM is not the ideal solution. …”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm
Published 2023“…SVM is a classification technique developed by Vapnik [1] but a practical difficulty of using SVM is the selection of parameters such as C and kernel parameter, � in Gaussian RBF kernel. …”
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Prediction of COVID-19 outbbreak using Support Vector Machine / Muhammad Qayyum Mohd Azman
Published 2024“…A prototype architecture and a user-friendly graphical interface tailored for SVM-based outbreak predictions are developed, accompanied by detailed code snippets elucidating essential steps in data loading, encoding, scaling, and SVM model training. …”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol
Published 2023“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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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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Logistic regression methods for classification of imbalanced data sets
Published 2012“…However, the imbalanced LR-based methods are not extensively developed such as imbalanced SVM-based methods. Hence, it is required to develop effective imbalanced LR-based methods to be widely used in data mining applications. …”
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Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill
Published 2019“…These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. …”
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Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves
Published 2024“…It ensures that plants receive the necessary nutrients for optimal growth and development.…”
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Support vector machine and neural network based model for monthly stream flow forecasting
Published 2023“…Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. …”
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Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data
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Performance improvement through optimal location and sizing of distributed generation / Zuhaila Mat Yasin
Published 2014“…Next, a novel hybrid Artificial Neural Network - Quantum-Inspired Evolutionary Programming (QIEP-ANN) is developed for comparison. Later, a Least-Squares Support Vector Machine (LS-SVM) model was developed using cross-validation technique. …”
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Classification of heart disease with machine learning: a comparison of grid search, random search, and Bayesian Optimization
Published 2026“…The results of the study show that hyperparameter optimization significantly improves prediction accuracy compared to baseline models, with the optimal method varying across algorithms. …”
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Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia
Published 2019“…The general objective of the study was the development of optimized hybrid debris flow models using airborne laser scanning data and Machine learning algorithms in Malaysia. …”
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RSA Encryption & Decryption using JAVA
Published 2006“…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The locations of landslides were detected accurately by employing two Machine learning classifiers, namely, SVM and RF, decision rule and hierarchal rules sets were developed by applying decision tree (DT) algorithm to provide improved landslide inventory. …”
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