Search Results - (( basic machine learning algorithm ) OR ( based optimization based algorithm ))
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1
Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
Published 2019“…As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. …”
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2
A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system
Published 2018“…This work proposes the extreme learning machine (ELM) is one of the poplar machine learning algorithms which, easy to implement with excellent learning performance characteristics. …”
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3
A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective
Published 2013“…Machine learning algorithms are iteration based algorithms, as the new knowledge is based on the previous predicted /calculated knowledge which helps to decrease errors in order to increase efficiency. …”
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4
Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection
Published 2019“…Those problems lend themselves to the realm of optimization. Considering the wide success of swarm intelligence methods in optimization problems, the main objective of this thesis is to contribute to the improvement of intrusion detection technology through the application of swarm-based optimization techniques to the basic problems of selecting optimal packet features, and optimal training of neural networks on classifying those features into normal and attack instances. …”
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Thesis -
5
Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach
Published 2022“…Deep Neural Network (DNN) is a combination method between two different subfields of Machine Learning application, including the Artificial Neural Network (ANN) and Deep Learning (DL). …”
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6
A new modern scheme for solving fractal–fractional differential equations based on deep feedforward neural network with multiple hidden layer
Published 2024“…Thus, this research aims to extend the application of ANN to solve FFDE with power law kernel in Caputo sense (FFDEPC) by develop a vectorized algorithm based on deep feedforward neural network that consists of multiple hidden layer (DFNN-2H) with Adam optimization. …”
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7
An Optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) for cryptocurrency forecasting
Published 2019“…However, forecasting result using basic SVM algorithms does not really promising. On the other hands, Particle Swarm Optimization (PSO) is known as a better algorithm for a static and simple optimization problem. …”
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Proceeding Paper -
8
Handover Parameter for Self-optimisation in 6g Mobile Networks: A Survey
Published 2024journal::journal article -
9
Comparative study on job scheduling using priority rule and machine learning
Published 2021“…We’ve achieved better for SJF and a decent machine learning algorithm outcome as well.…”
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10
Investigate And Analysis Of Deep Learning And Machine Learning Algorithm For Face Mask Detection System
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Undergraduates Project Papers -
11
Depression Detection Based on Features of Depressive Behaviour Through Social Media Analytic: A Systematic Literature Review
Published 2024“…Furthermore, it is also shown that various machine learning algorithms are used, and the most used are Neural Network and Support Vector Machine. …”
Conference Paper -
12
Estimation of electric vehicle turning radius through machine learning for roundabout cornering
Published 2021“…This paper presents an alternative approach for estimating the turning radius using machine learning technique. While on-board sensors are unable to offer adequate information on vehicle states to the algorithm, vehicle states other than those directly detected by on-board sensors can be inferred using machine learning (ML) approaches based on the collected data. …”
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13
Identification model for hearing loss symptoms using machine learning techniques
Published 2014“…The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
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14
Enhancement of network security by use machine learning
Published 2019“…By the end of the research, graphs and animation system have done shown to demonstrate the basic operation of the enhance network with machine learning system. …”
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15
Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning
Published 2024“…One of the prominent methods to improve machine learning accuracy is by using ensemble method which basically employs multiple base models. …”
Conference Paper -
16
Opposition-based Whale Optimization Algorithm
Published 2018“…In order to improve solution accuracy and reliability, this paper proposes a new algorithm based on WOA. The new algorithm called Opposition-based Whale Optimization (OWOA). …”
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Article -
17
Real-time oil palm fruit bunch ripeness grading system using image processing techniques
Published 2013“…These ROIs were based on the training and the testing of the ANN, KNN, and SVM supervised machine-learning classifiers. …”
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18
Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems
Published 2022“…To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. …”
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19
The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer
Published 2023“…Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. …”
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20
Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem
Published 2015“…In this study, rule-based multi-state gravitational search algorithm (RBMSGSA) algorithm is proposed to solve discrete combinatorial optimization problems. …”
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