Search Results - (( features selection system algorithm ) OR ( parameter optimization method algorithm ))

Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
Get full text
Get full text
Get full text

Rao-SVM machine learning algorithm for intrusion detection system by Abd, Shamis N., Alsajri, Mohammad, Ibraheem, Hind Raad

“…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. …”
Get full text
Get full text
Get full text

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

“…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. …”
Get full text
Get full text

Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Mustapha, Norwati, Perumal, Thinagaran, Mohamed, Raihani

“…The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. …”
Get full text
Get full text
Get full text

A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting by Razak I.A.W.A., Abidin I.Z., Yap K.S., Abidin A.A.Z., Rahman T.K.A., Nasir M.N.M.

Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification by Mohammed, Alhassan Afnan

“…Fuzzy clustering-based filtering methods are introduced for essential feature selection. …”
Get full text
Get full text

Plant leaf recognition algorithm using ant colony-based feature extraction technique by Ghasab, Mohammad Ali Jan

“…Moreover, the ant colony optimisation technique be applied as an expert algorithm to make a decision for the selection of optimal features in order to enhance the performance of a classifier for recognition of diverse species of plants. …”
Get full text
Get full text

Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
Get full text
Get full text
Get full text
Get full text

Optimizing deep neuro-fuzzy classifier with a novel evolutionary arithmetic optimization algorithm by Talpur, N., Abdulkadir, S.J., Alhussian, H., Hasan, M.H., Abdullah, M.H.A.

“…Therefore, this study aims on improving the model's accuracy by proposing Arithmetic Optimization Algorithm. The outcomes using the Arithmetic Optimization Algorithm for feature selection have not only reduced the burden of implementing a huge dataset, but the Arithmetic Optimization-based deep neuro-fuzzy system has outperformed with 95.14 accuracy compared to the standard method with 94.52. …”
Get full text
Get full text

Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

“…By integrating with the HS (or GSA) algorithm, the proposed metaheuristic neural networks (i.e., RBFN-DDA-HS and RBFN-DDA-GSA) can optimize the RBFN-DDA parameters and improve classification performances from the original RBFN-DDA up to 28.69% in two benchmarks datasets, which are numerical records from a bearing and steel plate system and a condition-monitoring system in a power plant (i.e., the circulating water (CW) system). …”
Get full text
Get full text
Get full text

Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui by Yang , Dong Rui

“…It applies the characteristic of ReliefF algorithm to rank and select top scoring features for feature selection. …”
Get full text
Get full text
Get full text

Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting by Intan Azmira , Abdul Razak, Izham , Zainal Abidin, Keem Siah, Yap, Titik Khawa, Abdul Rahman

“…A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. …”
Get full text
Get full text

A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant by Bunyamin M.A., Yap K.S., Aziz N.L.A.A., Tiong S.K., Wong S.Y., Kamal M.F.

Subjects:

Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…Also, self-adaptive scaling factor and crossover probability control parameters are introduced to diminish time of finding an optimal parameter to produce the best population. …”
Get full text
Get full text

A hybrid method of least square support vector machine and bacterial foraging optimization algorithm for medium term electricity price forecasting by Razak I.A.W.A., Ibrahim N.N.A.N., Abidin I.Z., Siah Y.K., Abidin A.A.Z., Rahman T.K.A.

“…So far, no literature has been found on feature and parameter selections using the LSSVM-BFOA method for medium term price prediction. …”

Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia by Lay, Usman Salihu

“…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. …”
Get full text
Get full text

Feature selection and parameter optimization with GA-LSSVM in electricity price forecasting by Intan Azmira W.A.R., Izham Z.A., Keem Siah Y., Titik Khawa A.R.

“…A comparative study of proposed approach with other techniques and previous research was conducted in term of forecast accuracy, where the results indicate that (1) the LSSVM with GA outperforms other methods of LSSVM and Neural Network (NN), (2) the optimization algorithm of GA gives better accuracy than Particle Swarm Optimization (PSO) and cross validation. …”

Short term electricity price forecasting with multistage optimization technique of LSSVM-GA by Razak I.A.W.A., Abidin I.Z., Siah Y.K., Abidin A.A.Z., Rahman T.K.A.

“…So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction. …”

An optimization method of genetic algorithm for lssvm in medium term electricity price forecasting by Abdul Razak I.A.W., Abidin I.Z., Siah Y.K., Abidin A.A.Z., Rahman T.K.A., Baharin N., Jali H.B.

“…So far, no literature has been found on feature and parameter selections using the method of LSSVM-GA for medium term price prediction. …”

Short Term Electricity Price Forecasting With Multistage Optimization Technique Of LSSVM-GA by Wan Abdul Razak, Intan Azmira, Zainal Abidin, Izham, Keem Siah, Yap, Zainul Abidin, Aidil Azwin, Abdul Rahman, Titik Khawa

“…Price prediction has now become an important task in the operation of electrical power system.In short term forecast,electricity price can be predicted for an hour-ahead or day-ahead.An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour.It is useful for an effective bidding strategy where the quantity of bids can be revised or changed prior to the dispatch hour.However,only a few studies have been conducted in the field of hour-ahead forecasting.This is due to most of the power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than singlesettlement system (real time).Therefore,a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features.So far,no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction.All the models are examined on the Ontario power market;which is reported as among the most volatile market worldwide.A huge number of features are selected by three stages of optimization to avoid from missing any important features.The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.…”
Get full text
Get full text
Get full text