Search Results - (( initial solution method algorithm ) OR ( using classification model algorithm ))

Improved cuckoo search based neural network learning algorithms for data classification by Abdullah, Abdullah

“…Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. …”
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Master-leader-slave cuckoo search with parameter control for ANN optimization and its real-world application to water quality prediction by Jaddi N.S., Abdullah S., Malek M.A.

Digital economy tax compliance model in Malaysia using machine learning approach by Raja Azhan Syah Raja Wahab, Azuraliza Abu Bakar

“…Based on the validation of training data with the presence of seven single classifier algorithms, three performance improvements have been established through ensemble classification, namely wrapper, boosting, and voting methods, and two techniques involving grid search and evolution parameters. …”
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Using genetic algorithms to optimise land use suitability by Pormanafi, Saeid

“…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying by Khaw , Hui Ying

“…An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output, while preserving significant pixel information. …”
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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

“…In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. …”
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Design and implementation of a deep learning based hand gesture recognition system for Rehabilitation Internet-Of-Things (RIOT) environments using MediaPipe by Mohd Dhuzuki, Nurul Hanis, Zainuddin, Ahmad Anwar, Kamarul Zaman, Nur Anis Sofea, Ahmad Razmi, Alin Nur Maisarah, Kaitane, Wonderful Shammah, Ahmad Puzi, Asmarani, Johar, Mohd Naqiuddin, Yazid, Maslina, Mohd Nordin, Nor Azlin, Sidek, Shahrul Naim, Mohd Zaki, Hasan Firdaus

“…Several limitations, including latency and distance sensitivity, are addressed in this system with edge computing alongside adaptive algorithms. The key contributions of this research are as follows: First, developing a real-time and cost-effective solution for remote stroke rehabilitation. …”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

“…The classification is performed using the derived features from the thermal images and the backscatter features. …”
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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

“…EM and K-means clustering algorithms are used to cluster the multi-class classification attribute according to its relevance criteria and afterward, the clustered attributes are classified using an ensemble random forest classifier model. …”
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An adaptive ant colony optimization algorithm for rule-based classification by Al-Behadili, Hayder Naser Khraibet

“…Various classification algorithms have been developed to produce classification models with high accuracy. …”
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Predicting breast cancer using ant colony optimisation / Siti Sarah Aqilah Che Ani by Che Ani, Siti Sarah Aqilah

“…This study implements a machine learning algorithm called Ant Colony Optimization (ACO) algorithm to develop an accurate classification model for predicting breast cancer cells. …”
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Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

“…In addition, experiments prove that incremental genetic-based clustering ensemble algorithm speed up to converge into an optimal clustering solution, where pattern ensemble learning method and the cluster partitions produced by the threshold fuzzy c-means clustering algorithm are employed as recombination operator and initial population, respectively.…”
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Multistage optimal homotopy asymptotic method for solving initial-value problems by Anakira, N. R., Alomari, A. K., Jameela, Ali, Hashim, Ishak

“…In this paper, a new approximate analytical algorithm namely multistage optimal homotopy asymptotic method (MOHAM) is presented for the first time to obtain approximate analytical solutions for linear, nonlinear and system of initial value problems (IVPs).This algorithm depends on the standard optimal homotopy asymptotic method (OHAM), in which it is treated as an algorithm in a sequence of subinterval. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Classification model for water quality using machine learning techniques by Azilawati, Rozaimee, Azrul Amri, Jamal, Azwa, Abdul Aziz

“…In assessing the result, the Lazy model using K Star algorithm was the best classification model among the five models had the most outstanding accuracy of 86.67%. …”
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Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms by Sirajun Noor, Noor Azmiya

“…Whereas for the German Frankfurt dataset, best DM classification model was found using Random Forest algorithm with an accuracy of 98.77%.…”
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Academic leadership bio-inspired classification model using negative selection algorithm by Jantan, Hamidah, Sa’dan, Siti ‘Aisyah, Che Azemi, Nur Hamizah Syafiqah

“…Several experiments were carried out by using different set of training and testing data-sets to evaluate the accuracy of the proposed model.As a result, the accuracy of the proposed model is considered excellent for academic leadership classification.For future work, in order to enhance the proposed bio-inspired classification model, a comparative study should be conducted using other established artificial immune system classification algorithms i.e. clonal selection and artificial immune network.…”
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Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie by Mat Saffie, Nur Amira

“…Furthermore, most classification algorithms, using either fuzzy or non-fuzzy approaches, produce results in the form of crisp or categorical classification outcomes. …”
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Combining approximation algorithm with genetic algorithm at the initial population for NP-complete problem by Razip, H., Zakaria, M.N.

“…In Genetic Algorithm (GA), the prevalent approach to population initialization are heuristics and randomization. …”
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Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza by Khaimuza, Haris Hidayatullah

“…Second objective is to develop the prototype of daisy species classification based on image using CNN algorithm. The last objective is to evaluate the accuracy of CNN model in the daisy species classification based on image. …”
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