Search Results - optimal ((bayes algorithm) OR (((bee algorithm) OR (tree algorithm))))*

Search alternatives:

Refine Results
  1. 1
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…Consequently, during peak hours, finding a vacant parking bay is more of a difficult task. This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  2. 2
    A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets

    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

    Published 2021
    “…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
    Article
    Save to List
    Saved in:
  3. 3
    Optimisation of support vector machine hyperparameters using enhanced artificial bee colony variant to diagnose breast cancer

    Optimisation of support vector machine hyperparameters using enhanced artificial bee colony variant to diagnose breast cancer by Ravindran, Nadarajan

    Published 2023
    “…This algorithm named JAABC5ROC is the enhancement of Artificial Bee Colony (ABC) variant, JA-ABC5 by combining with Rate of Change (ROC)\. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  4. 4
    Intrusion Detection Systems, Issues, Challenges, and Needs

    Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

    Published 2021
    “…However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  5. 5
    Classification of Diabetes Mellitus using Ensemble Algorithms

    Classification of Diabetes Mellitus using Ensemble Algorithms by Noor, N.A.B.S., Elamvazuthi, I., Yahya, N.

    Published 2021
    “…The objective of this study is to perform DM classification using various machine learning algorithms. In this paper, individual classifiers such as Support Vector Machine, Naïve Bayes, Bayes Net, Decision Stump, k - Nearest Neighbors, Logistic Regression, Multilayer Perceptron and Decision Tree are experimented. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  6. 6
    Investigating optimal smartphone placement for identifying stairs movement using machine learning

    Investigating optimal smartphone placement for identifying stairs movement using machine learning by Muhammad Ruhul Amin, Shourov, Husman, Muhammad Afif, Toha, Siti Fauziah, Jasni, Farahiyah

    Published 2023
    “…The data was trained against 6 machine learning algorithms namely Decision Tree, Logistic Regression, Naive Bayes, Random Forest, Neural Networks and KNN. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  7. 7
    An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA

    An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA by Bian, Hui

    Published 2025
    “…Traditional machine learning algorithms, such as Decision Trees, Naive Bayes, Random Forest, Random Trees, Multi-Layer Perceptron, and Support Vector Machines, have been extensively applied to address these threats. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  8. 8
    A direct ensemble classifier for imbalanced multiclass learning

    A direct ensemble classifier for imbalanced multiclass learning by Sainin, Mohd Shamrie, Alfred, Rayner

    Published 2012
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  9. 9
    An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA

    An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA by Hui, Bian, Chiew, Kang Leng

    Published 2025
    “…This study investigates the performance of various conventional machine learning algorithms, including decision trees, naive Bayes, naive Bayes trees, random forest, random trees, MLP, and SVM, in detecting network intrusions using binary and multi-classification approaches. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  10. 10
    Towards a better feature subset selection approach

    Towards a better feature subset selection approach by Shiba, Omar A. A.

    Published 2010
    “…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  11. 11
    Prediction of Heart Disease Risk Using Machine Learning with Correlation-based Feature Selection and Optimization Techniques

    Prediction of Heart Disease Risk Using Machine Learning with Correlation-based Feature Selection and Optimization Techniques by Reddy, K.V.V., Elamvazuthi, I., Aziz, A.A., Paramasivam, S., Chua, H.N., Pranavanand, S.

    Published 2021
    “…Numerous machine learning classifiers, Decision Tree, Discriminant Analysis, Logistic Regression, Naïve Bayes, Support Vector Machines, k-Nearest Neighbors, Bagged Trees, Optimizable Tree, and Optimizable k-Nearest Neighbors are trained using 10-fold cross-validation for efficient heart disease risk prediction on the Correlation-based Feature Selection optimal set of the integrated heart dataset. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  12. 12
    Performance evaluation of intrusion detection system using selected features and machine learning classifiers

    Performance evaluation of intrusion detection system using selected features and machine learning classifiers by Raja Mahmood, Raja Azlina, Abdi, AmirHossien, Hussin, Masnida

    Published 2021
    “…These evolutionary-based algorithms are known to be effective in solving optimization problems. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  13. 13
    Characterisation of pineapple cultivars under different storage conditions using infrared thermal imaging coupled with machine learning algorithms

    Characterisation of pineapple cultivars under different storage conditions using infrared thermal imaging coupled with machine learning algorithms by Mohd Ali, Maimunah, Hashim, Norhashila, Abd Aziz, Samsuzana, Lasekan, Ola

    Published 2022
    “…Several types of machine learning algorithms were compared, including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and naïve Bayes, to obtain the best performance for the classification of pineapple cultivars. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  14. 14
    Support Vector Machines (SVM) in Test Extraction

    Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

    Published 2006
    “…There exist numerous algorithms to address the need of text categorization including Naive Bayes, k-nearest-neighbor classifier, and decision trees. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  15. 15
    Support Vector Machines (SVM) in Test Extraction

    Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

    Published 2006
    “…There exist numerous algorithms to address the need of text categorization including Naive Bayes, k-nearest-neighbor classifier, and decision trees. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  16. 16
    Optimal design of step – cone pulley problem using the bees algorithm

    Optimal design of step – cone pulley problem using the bees algorithm by Yusof, Noor Jazilah, Kamaruddin, Shafie

    Published 2021
    “…The Bees Algorithm is considered one of the recent optimization algorithms and it has been successfully solved various types of problems. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Book Chapter
    Save to List
    Saved in:
  17. 17
    Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function

    Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function by Hammash, Nayif Mohammed

    Published 2012
    “…This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  18. 18
    Assembly sequence optimization using the bees algorithm

    Assembly sequence optimization using the bees algorithm by Kamaruddin, Shafie, Azmi, Nabilah, Sukindar, Nor Aiman

    Published 2022
    “…As a result, the Bees Algorithm outperforms other algorithms in dealing with the multi-modal optimization problem of assembly sequence optimization.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Book Chapter
    Save to List
    Saved in:
  19. 19
    A hybrid algorithm based on artificial bee colony and artificial rabbits optimization for solving economic dispatch problem

    A hybrid algorithm based on artificial bee colony and artificial rabbits optimization for solving economic dispatch problem by Lee, Wei Wen, Hashim, Mohd Ruzaini

    Published 2023
    “…Hence, a hybrid optimization algorithm called Artificial Bee Rabbit Optimization (ABRO) is proposed in this paper. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  20. 20
    An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction

    An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction by Shah, Habib

    Published 2014
    “…Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. They are: Global Guided Artificial Bee Colony (GGABC), Improved Gbest Guided Artificial Bee Colony (IGGABC) and Artificial Smart Bee Colony (ASBC) algorithm. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:

Search Tools: