Search Results - (( evolution optimization parallel algorithm ) OR ( binary classification clustering algorithm ))

Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier by Siow, Shien Loong

“…There are a lot of feature extraction methods and classification methods for iris classification. Classic local binary pattern (LBP) is one of the most useful feature extraction methods. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

“…Based on the above components and circumstances, many studies have been performed on data clustering problems. Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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Optimization of extractive Automatic Text Summarization using Decomposition-based Multi-objective Differential Evolution and parallelization by Hazmi Wahab, Muhammad Hafizul

“…The central challenge in Automatic Text Summarization (ATS) is efficiently generating machine-generated text summaries through optimization algorithms, a critical component for systems dealing with textual information processing. …”
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Global and local clustering soft assignment for intrusion detection system: a comparative study by Mohd Rizal Kadis, Azizi Abdullah

“…The results show that the global clustering approach outperforms the local clustering approach for binary classification. …”
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Spatial Clustering Algorithm for Time Series Rainfall Data Using X-Means Data Splitting by Ali, Noor Rasidah, Ku Mahamud, Ku Ruhana

“…The performance of the clustering algorithm gets even worse if it relies on actual data and many clustering algorithms are often faced with conflict in handling high dimensional data. …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance by Hammoudeh, S. Alamri

“…To evaluate the PMT’s performance and adaptability, the PMT was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. …”
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Feature clustering for pso-based feature construction on high-dimensional data by Swesi, Idheba Mohamad Ali Omer, Abu Bakar, Azuraliza

“…The clustering of each features are proven to be accurate in feature selection (FS), however, only one study investigated its application in FC for classification. …”
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Development of compound clustering techniques using hybrid soft-computing algorithms by Salim, Naomie, Shamsuddin, Siti Mariyam, Salleh @ Sallehuddin, Roselina, Alwee, Razana

“…The hierarchical fuzzy clustering algorithm developed in this work assign the overlapping structures (structures having more than one activity) to more than one clusters if their fuzzy membership values are significantly high for those clusters. …”
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PMT: opposition-based learning technique for enhancing meta-heuristic performance by Alamri, Hammoudeh S., Kamal Z., Zamli

“…To evaluate the PMT's performance and adaptability, the PMT has been applied to four contemporary meta-heuristic algorithms, differential evolution (DE), particle swarm optimization (PSO), simulated annealing (SA), and whale optimization algorithm (WOA), to solve 15 well-known benchmark functions. …”
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OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM by WAHYUNGGORO, OYAS WAHYUNGGORO

“…In this thesis, a new optimization GA-based algorithm that emanates from modification of conventional GA to reduce the iterations number and the duration time, namely, semi-parallel operation genetic algorithm (SPOGA) is proposed. …”
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Application Of Genetic Algorithms For Robust Parameter Optimization by Belavendram, N.

“…Genetic algorithms (GA) are fairly recent in this respect but afford a novel method of parameter optimization. …”
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Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Machine learning approach for stress detection based on alpha-beta and theta-beta ratios of EEG signals by Altaf, Hunain, Ibrahim, Siti Noorjannah, Mohd Azmin, Nor Fadhillah, Asnawi, Ani Liza, Walid, Balqis Hanisah, Harun, Noor Hasmiza

“…This study will ultimately contribute to society's development with improved robust machine learning algorithm for binary classification.…”
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Enhancement of new smooth support vector machines for classification problems by Santi Wulan, Purnami

“…The results of this study showed that MKS-SSVM was effective to diagnose medical dataset and this is promising results compared to the previously reported results. SSVM algorithms are developed for binary classification. …”
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach by Mustakim, Nurul Ain

“…The framework uses machine learning methods, including classification, clustering, feature selection, and parameter tuning, to improve accuracy and reliability. …”
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The identification of oreochromis niloticus feeding behaviour through the integration of photoelectric sensor and logistic regression classifier by Mohamad Radzi, Mohd Sojak, Mohd Azraai, Mohd Razman, Anwar, P. P. Abdul Majeed, Rabiu Muazu, Musa, Ahmad Shahrizan, Abdul Ghani, Ismed, Iskandar

“…The signals acquired from the sensors are converted into binary data. The hunger behaviour classes are determined through k-means clustering algorithm, i.e., satiated and unsatiated. …”
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A study on component-based technology for development of complex bioinformatics software by Ali Shah, Zuraini, Deris, Safaai, Othman, Muhamad Razib, Zakaria, Zalmiyah, Saad, Puteh, Hassan, Rohayanti, Muda, Mohd. Hilmi, Kasim, Shahreen, Roslan, Rosfuzah

“…From the enriched GO tree, the BTreeBicluster algorithm is applied during the clustering process. …”
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