Search Results - (( set selection method algorithm ) OR ( parameter detection method algorithm ))

Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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Model-based hybrid variational level set method applied to lung cancer detection by Jing, Wang, Liew, Siau-Chuin, Azian, Abd Aziz

“…This paper presents a novel model-based hybrid variational level set method (VLSM) tailored for lung cancer detection. …”
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

“…We modified the classical bootstrapping algorithm by developing a mechanism based on the robust LTS method to detect the correct number of outliers in the each bootstrap sample. …”
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Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

“…The second algorithm reduces the quantity of interest regions by using the Extremal Region Selection (ERS) algorithm. …”
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Mental stress classification based on selected EEG channels using Correlation Coefficient of Hjorth Parameters by Hag, Ala, Fares, Al-Shargie, Handayani, Dini Oktarina Dwi, Houshyar, Asadi

“…This study introduces a novel channel selection method aimed at identifying highly accurate channels for detecting mental stress. …”
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Model-based hybrid variational level set method applied to object detection in grey scale images by Wang, Jing

“…This algorithm leverages an improved multi-scale mean filter to mitigate grayscale inhomogeneity while reducing the impact of scale parameter selection. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

“…However, a drawback is that the performance of ANN is sensitive to the parameters (i.e., number of hidden neurons and the initial values of connection weights) in its architecture where the settings of these parameters are subject to tuning on a trial-and-error basis. …”
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Edge detection of MRI images using artificial neural network by Gunawan, Teddy Surya, Kartiwi, Mira, Abdul Malik, Noreha

“…Discussion The proposed ANN based edge detection produce better image segmentation for MRI images compared to other traditional edge detection algorithms. …”
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Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network by Sivarao, Subramonian

“…On the other hand, when we use computers to reduce uncertainty, the computer itself can become an expert in a specific field through a variety of methods. One such method is machine learning, which involves computer algorithm to capture hidden knowledge from data. …”
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Customer analysis with machine vision by Tiong, Wei Jie

“…To address the issue, the latest algorithms, such as YOLOv5, YOLOv8 and mobilenet ssd, were selected for retraining. …”
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Optimization of feature selection in Support Vector Machines (SVM) using recursive feature elimination (RFE) and particle swarm optimization (PSO) for heart disease detection by Bayuaji, Luhur, Kusnadi, Kusnadi, Amzah, Mochamad Yamin, Pebrianti, Dwi

“…Apart from that, the choice of C and gamma parameters can affect SVM performance. The RFE method is used to select the most informative and relevant subset of features from a given feature set. …”
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An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition by Al-Qammaz, Abdullah Yousef Awwad

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Optimal coordination of overcurrent relay protection using evolutionary programming / Noor Shah Rizal Abdul Manap by Noor Shah Rizal, Abdul Manap

“…The optimum time for each relay during the fault occurrence is the obtained from the optimization process. This meta-heuristic method is selected as it has less parameters to be adjusted and computationally simple to adopt with the coordination problem. …”
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Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor by Adilah, Abdul Ghapor

“…The simulation results indicate that the proposed method is suitable to detect a single outlier. As for the multiple outliers, a clustering algorithm is considered and a dendogram to visualise the clustering algorithm is used. …”
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Application of weighted average on modal parameters for damage detection algorithms: Case study on steel beam by Fayyadh, M.M., Abdul Razak, H.

“…This study verifies the use of the proposed weighting method by Fayyadh and Abdul Razak (2011a) for damage detection algorithms applied on cracked steel beam. …”
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Weighting method for modal parameter based damage detection algorithms by Fayyadh, M.M., Abdul Razak, H.

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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

“…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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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

“…This creates the jamming detection and classification parameters. The second stage is detecting jammers by integrating both lower layers by developing Integrated Combined Layer Algorithm (ICLA). …”
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Classification of transient disturbance using Wavelet based support vector machine / Fahteem Hamamy Anuwar by Anuwar, Fahteem Hamamy

“…Cross validation is used to find the best parameters related to kernels used followed by training and testing of the data sets. …”
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