Search Results - (( knowledge implementation learning algorithm ) OR ( based classifications using algorithm ))

Refine Results
  1. 1

    Kernel and multi-class classifiers for multi-floor wlan localisation by Abd Rahman, Mohd Amiruddin

    Published 2016
    “…The variant AP selection is further improved by grouping AP based on signal distribution. In this work, two AP selection algorithms are proposed which are Max Kernel and Kernel Logistic Discriminant that implement the knowledge of kernel density estimate and logistic regression machine learning classification. …”
    Get full text
    Get full text
    Thesis
  2. 2

    Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.] by Mohd, Thuraiya, Jamil, Syafiqah, Masrom, Suraya, Ab Rahim, Norbaya

    Published 2021
    “…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
    Get full text
    Get full text
    Conference or Workshop Item
  3. 3
  4. 4

    Security alert framework using dynamic tweet-based features for phishing detection on twitter by Liew, Seow Wooi

    Published 2019
    “…This model is then embedded into the detection algorithm together with the inclusion of dynamic tweet-based features which are not as part of the features used to train a classification model for phishing tweet detection. …”
    Get full text
    Get full text
    Thesis
  5. 5

    Comparison on machine learning algorithm to fast detection of malicious web pages by Wan Nurul Safawati, Wan Manan, Mohd Nizam, Mohmad Kahar, Noorlin, Mohd Ali

    Published 2021
    “…Therefore, implementing the principle of the machine learning, which is training the classification algorithm will be perform to improve the detection accuracy. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  6. 6

    An Artificial Intelligence-Based Knowledge Management System for Outcome-Based Education Implementing in Higher Education Institutions by Gerhana, Yana Aditia

    Published 2025
    “…Recommendation system on learning analysis was implemented in a hybrid algorithm combines Rule-based and Content-based filtering algorithms. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  7. 7

    Identification model for hearing loss symptoms using machine learning techniques by Nasiru Garba Noma

    Published 2014
    “…The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  8. 8

    Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

    Published 2020
    “…The hybrid Wavelet Multiresolution Analysis and Machine learning algorithm (WMRA-ML) is used to extracts the useful hidden knowledge from decomposed one-cycle fault transient signals (voltage & current) from four Matlab/Simulink CIGRE models. …”
    Get full text
    Get full text
    Thesis
  9. 9

    Comparing the knowledge quality in rough classifier and decision tree classifier by Mohamad Mohsin, Mohamad Farhan, Abd Wahab, Mohd Helmy

    Published 2008
    “…Theoretically, different classifiers will generate different sets of rules via knowledge even though they are implemented to the same classification problem.Hence, the aim of this paper is to investigate the quality of knowledge produced by Rc and DTc when similar problems are presented to them.In this case, four important performance metrics are used as comparison, the accuracy of classification, rules quantity, rules length and rules coverage.Five dataset from UCI Machine Learning are chosen and then mined using Rc toolkit namely ROSETTA while C4.5 algorithm in WEKA application is chosen as DTc rule generator. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  10. 10

    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

    Published 2004
    “…The neural network learns the rough set’s upper and lower approximations as feature extractors simultaneously with classification. …”
    Get full text
    Get full text
    Thesis
  11. 11

    Ontology enrichment with causation relations by Amaal Saleh Hassan, Al Hashimy, Narayanan, Kulathuramaiyer

    Published 2014
    “…Ontology learning is considered a potential approach that can help to reduce the bottleneck of knowledge acquisition. …”
    Get full text
    Get full text
    Get full text
    Article
  12. 12

    Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning by Sainin, Mohd Shamrie, Alfred, Rayner, Alias, Suraya, Lammasha, Mohamed A.M.

    Published 2018
    “…There are two feature selection approaches implemented which are filter-based (CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval) and wrapper-based (WrapperSubsetEval). …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  13. 13

    Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset by Abd Rahman, M. Hafiz Fazren, Wan Salim, Wan Wardatul Amani, Abd-Wahab, Firdaus

    Published 2020
    “…This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using several well-known machine learning algorithm such as Decision Tree, Support Vector Machine and Naïve Bayers. …”
    Get full text
    Get full text
    Get full text
    Article
  14. 14

    Integrated artificial intelligence-based classification approach for prediction of acute coronary syndrome by Salari, Nader

    Published 2014
    “…The following results were obtained when classification of the ACS types used the conventional “single AI-based” methods. …”
    Get full text
    Get full text
    Get full text
    Thesis
  15. 15
  16. 16

    Problem restructuring in interger programming for reduct searching by Ungku Chulan, Ungku Azmi Iskandar

    Published 2003
    “…The thesis emphasizes mainly on the improvement of the original SIP/DRIP algorithm in term of performance. By using problem restructuring, the searching time and memory are minimized. …”
    Get full text
    Get full text
    Thesis
  17. 17
  18. 18

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

    Published 2004
    “…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
    Get full text
    Get full text
    Thesis
  19. 19

    An adaptive ant colony optimization algorithm for rule-based classification by Al-Behadili, Hayder Naser Khraibet

    Published 2020
    “…Ant-Miner is a variant of ant colony optimisation and a prominent intelligent algorithm widely use in rules-based classification. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  20. 20

    Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor by Intan Noradybah Md Rodi

    Published 2019
    “…Thehighest accuracy for classification map of Gunung Basor is by using maximum likelihood algorithm with an accuracy of 82.90%. …”
    Get full text
    Get full text
    Undergraduate Final Project Report