Search Results - (((( patterns ant algorithm ) OR ( pattern bees algorithm ))) OR ( deep learning algorithm ))

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  1. 1
    Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System

    Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System by Abidin, Zulkifli Zainal

    Published 2013
    “…It is actually inspired by understanding the decentralized mechanisms in the organization of natural swarms such as the birds, the ants, the bees, the glowworms, and the fireflies. …”
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  2. 2
    A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

    A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting by Ibrahim K.S.M.H., Huang Y.F., Ahmed A.N., Koo C.H., El-Shafie A.

    Published 2023
    Review
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  3. 3
    Incremental continuous ant colony optimization for tuning support vector machine’s parameters

    Incremental continuous ant colony optimization for tuning support vector machine’s parameters by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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  4. 4
    Optimizing support vector machine parameters using continuous ant colony optimization

    Optimizing support vector machine parameters using continuous ant colony optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…Support Vector Machines are considered to be excellent patterns classification techniques.The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and may be done experimentally through time consuming human experience.To overcome this difficulty, an approach such as Ant Colony Optimization can tune Support Vector Machine parameters.Ant Colony Optimization originally deals with discrete optimization problems. …”
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  5. 5
    Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification

    Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification by Nuaimi, Zakaria Noor Aldeen Mahmood Al, Abdullah, Rosni

    Published 2017
    “…Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. …”
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  6. 6
    Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer

    Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2025
    “…The study compares the proposed CNN-LSTM-BMO against other metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Differential Evolution (DE). …”
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  7. 7
    Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification

    Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification by Al Nuaimi, Zakaria Noor Aldeen Mahmood, Abdullah, Rosni

    Published 2017
    “…In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application.The performance of the HPABC algorithm was investigated on four benchmark pattern-classification data sets and the results were compared with other algorithms.The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT.HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy.…”
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  8. 8
    Application of the bees algorithm to the selection features for manufacturing data

    Application of the bees algorithm to the selection features for manufacturing data by Pham, D.T, Mahmuddin, Massudi, Otri, S., Al-Jabbouli, H.

    Published 2007
    “…The Bees Algorithm is employed to select an optimal set of features for a particular pattern classification task. …”
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  9. 9
    Predicting stroke using ant colony optimization algorithm / Azfaruddin Azri and Rizauddin Saian

    Predicting stroke using ant colony optimization algorithm / Azfaruddin Azri and Rizauddin Saian by Azri, Azfaruddin, Saian, Rizauddin

    Published 2023
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  10. 10
    Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective

    Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective by Abubakar, Adamu, Ya’u Gital, Abdulsalam, Chiroma, Haruna, Rana, Nadim, Abdulhamid, Shafi’i, Muhammad, Amina Nuhu, Umar, Aishatu Yahaya

    Published 2019
    “…The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. …”
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  11. 11
    Formulating new enhanced pattern classification algorithms based on ACO-SVM

    Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
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  12. 12
    Big data clustering using grid computing and ant-based algorithm

    Big data clustering using grid computing and ant-based algorithm by Ku-Mahamud, Ku Ruhana

    Published 2013
    “…This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm.…”
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  13. 13
    Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data

    Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data by Amri, A’inur A’fifah, Ismail, Amelia Ritahani, Zarir, Abdullah Ahmad

    Published 2018
    “…However, deep learning algorithms, such as deep belief networks showed promising results in many domains, especially in image processing. …”
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  14. 14
    Ant system-based feature set partitioning algorithm for classifier ensemble construction

    Ant system-based feature set partitioning algorithm for classifier ensemble construction by Abdullah, , Ku-Mahamud, Ku Ruhana

    Published 2016
    “…All of these approaches attempt to generate diversity in the ensemble.However, classifier ensemble construction still remains a problem because there is no standard guideline in constructing a set of accurate and diverse classifiers. In this study, Ant system-based feature set partitioning algorithm for classifier ensemble construction is proposed.The Ant System Algorithm is used to form an optimal feature set partition of the original training set which represents the number of classifiers.Experiments were carried out to construct several homogeneous classifier ensembles using nearest mean classifier, naive Bayes classifier, k-nearest neighbor and linear discriminant analysis as base classifier and majority voting technique as combiner. …”
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  15. 15
    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market

    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market by Mohd. Ridzuan Ab. Khalil, Azuraliza Abu Bakar

    Published 2023
    “…This study aims to develop a univariate and multivariate stock market forecasting model using three deep learning algorithms and compare the performance of those models. …”
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  16. 16
    Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal

    Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal by Fadzal, Ahmad Nazmi

    Published 2017
    “…Pheromone concept is the main criterion that distinguish ACO to other algorithms. Based on the concept, pheromone saturation is used to combine stackable solution pattern that is discovered while straying to different term node to build a path. …”
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  17. 17
    Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)

    Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023) by Hosseini E., Al-Ghaili A.M., Kadir D.H., Gunasekaran S.S., Ahmed A.N., Jamil N., Deveci M., Razali R.A.

    Published 2025
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  18. 18
    Optimisation of fed-batch fermentation process using deep reinforcement learning

    Optimisation of fed-batch fermentation process using deep reinforcement learning by Chai, Wan Ying

    Published 2023
    “…Fed-batch fermentation process has always been a challenge for optimisation because it is highly non-linear and complex. Deep reinforcement learning is a self-learning algorithm through trial and error and experience, without any prior knowledge. …”
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  19. 19
    Performance of correlational filtering and deep learning based single target tracking algorithms / ZhongMing Liao and Azlan Ismail

    Performance of correlational filtering and deep learning based single target tracking algorithms / ZhongMing Liao and Azlan Ismail by ZhongMing, Liao, Ismail, Azlan

    Published 2023
    “…Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. …”
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  20. 20
    Mobile machine vision for railway surveillance system using deep learning algorithm

    Mobile machine vision for railway surveillance system using deep learning algorithm by Kit, Guan Lim, Daniel Siruno, Min, Keng Tan, Chung, Fan Liau, Sha, Huang, Tze, Kenneth Kin Teo

    Published 2021
    “…In this paper, object detection model is developed and implemented with deep learning algorithm. Object classification model is produced through the model training with Deep Neural Networks (DNN). …”
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