Search Results - (( simulation optimization using algorithm ) OR ( learning classification problems algorithm ))

A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules by Rizauddin, Saian

“…In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. …”
Get full text
Get full text
Get full text
Get full text

BAT-BP: A new BAT based back-propagation algorithm for efficient data classification by Mohd. Nawi, Nazri, M. Z., Rehman, Hafifi, Nurfarian, Khan, Abdullah, Siming, Insaf Ali

“…Thus, this study investigates the use of Bat algorithm along with back-propagation neural network (BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to enhance the convergence rate. …”
Get full text
Get full text

Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

“…In the preliminary study, the algorithm is evaluated on the four different peak models of the three EEG signals using the artificial neural network (ANN) with particle swarm optimization (PSO) as learning algorithm. …”
Get full text
Get full text
Get full text

WCBP: A new water cycle based back propagation algorithm for data classification by Mohd. Nawi, Nazri, Khan, Abdullah, Firdaus, Naim, M. Z., Rehman, Siming, Insaf Ali

“…Water Cycle algorithm is a modern nature inspired meta-heuristic algorithm to provide derivative-free solution to optimize complex problems. …”
Get full text
Get full text

Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…Thus, seven metaheuristic algorithms: Barnacles Mating Optimizer (BMO), Marine Predators Algorithm (MPA), Moth–Flame Optimization (MFO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching–Learning-Based Optimization (TLBO) and Heap-Based Optimizer (HBO) are used to solve two objective functions: power loss and cost minimizations. …”
Get full text
Get full text
Get full text

New modified controlled bat algorithm for numerical optimization problem by Waqas Haider Bangyal, Abdul Hameed, Jamil Ahmad, Kashif Nisar, Muhammad Reazul Haque, Ag. Asri Ag. Ibrahim, Joel J. P. C. Rodrigues, M. Adil Khan, Danda B. Rawat, Richard Etengu

“…Bat algorithm (BA) is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems. …”
Get full text
Get full text
Get full text
Get full text
Get full text

Hybrid of swarm intelligent algorithms in medical applications by Abubakar, Adamu, Haruna, Chiroma, Abdullah Muaz, Sanah, Ya'u Gital, Abdulsalam, Baba Dauda, Ali, Joda Usman, Muhammed

“…The capabilities of six (6) global optimization learning algorithms were studied and their performances in training as well as testing were compared. …”
Get full text
Get full text
Get full text
Get full text
Get full text

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

“…The second test evaluates IFS in a controlled study using simulated datasets. Moreover, the third test used ten natural domain datasets obtained from UCI Repository, in about fifteen different experiments, using three to four different Machine Learning Algorithms for performance evaluation. …”
Get full text
Get full text
Get full text
Get full text

Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. …”
Get full text
Get full text

A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

“…Then, this study aims to optimize the hyperparameters of the developed DNN model using the Arithmetic Optimization Algorithm (AOA) and, lastly, to evaluate the performance of the newly proposed deep learning model with Simulated Kalman Filter (SKF) algorithm in solving image encryption application. …”
Get full text
Get full text

A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets by Mohd Razali, Muhamad Hasbullah, Saian, Rizauddin, Yap, Bee Wah, Ku-Mahamud, Ku Ruhana

“…This study proposed an enhanced algorithm called hellingerant-tree-miner (HATM) which is inspired by ant colony optimization (ACO) metaheuristic for imbalanced learning using decision tree classification algorithm. …”
Get full text
Get full text
Get full text

The effect of pre-processing techniques and optimal parameters on BPNN for data classification by HUSSEIN, AMEER SALEH

Get full text
Get full text
Get full text
Get full text

Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

“…The problems in applying unsupervised learning/clustering is that this method requires teacher during the classification process and it has to learn independently which may lead to poor classification. …”
Get full text
Get full text

Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems by Abdul Hamid, Norhamreeza, Mohd Nawi, Nazri, Ghazali, Rozaida, Mohd Salleh, Mohd Najib

“…The back propagation (BP) algorithm is a very popular learning approach in feedforward multilayer perceptron networks. …”
Get full text
Get full text
Get full text

Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

“…Meanwhile, Kmeans clustering algorithm has also been reported has widely known for solving most unsupervised classification problems. …”
Get full text
Get full text
Get full text

Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach by Moheb Pour, Majid Reza

“…This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. …”
Get full text
Get full text

Classification model for water quality using machine learning techniques by Azilawati, Rozaimee, Azrul Amri, Jamal, Azwa, Abdul Aziz

“…This article proposes a suitable classification model for classifying water quality based on the machine learning algorithms. …”
Get full text
Get full text

Grid base classifier in comparison to nonparametric methods in multiclass classification by Moheb Pour, Majid Reza, Jantan, Adznan, Saripan, M. Iqbal

“…This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. …”
Get full text
Get full text
Get full text

A Comparison Between Levenberg-Marquardt (LM) Intelligent System And Bayesian Regularization (BR) Intelligent System For Flow Regime Classification by Sa'ad, Mohamad Iqbal

“…The purpose of this project is to study the performance, leaning time and, output of Levenberg-Marquardt (LM) intelligent system and Bayesian Regularization (BR) intelligent system through a classification problem. These studies will help in choosing the right training algorithm for classification problem involved. …”
Get full text
Get full text

Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

“…One of the problems addressed by machine learning is data classification. …”
Get full text
Get full text