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  1. 1
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…These data are processed and stored in appropriate formats in a MySQL server database. Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  2. 2
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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  3. 3
    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
    “…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. …”
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  4. 4
    An alternative approach to normal parameter reduction algorithms for decision making using a soft set theory / Sani Danjuma

    An alternative approach to normal parameter reduction algorithms for decision making using a soft set theory / Sani Danjuma by Sani , Danjuma

    Published 2017
    “…The proposed algorithm was able to avoid the use of parameter important degree, decision partition and finding the multiple of the universe within the sets. …”
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  5. 5
    An Alternative Algorithm for Soft Set Parameter Selection using Special Order

    An Alternative Algorithm for Soft Set Parameter Selection using Special Order by Mohammed, Mohammed Adam Taheir, Wan Maseri, Wan Mohd, Ruzaini, Abdullah Arshah, Mungad, M., Sutoyo, Edi, Chiroma, Haruna

    Published 2015
    “…Comparative analysis were performed between the proposed algorithm and the state-of-the-art parameter reduction algorithm using several soft set in terms of parameter reduction…”
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  6. 6
    Clustering chemical data set using particle swarm optimization based algorithm

    Clustering chemical data set using particle swarm optimization based algorithm by Triyono, Triyono

    Published 2008
    “…Two chemical data sets were used and downloaded from MDDR (MDL Drug Database Report). …”
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  7. 7
    Application of the Genetic Algorithms (GAs) in determining the optimal transformer tap setting for loss minimization using MATLAB / Mohd Syful Adly Abd. Wahab

    Application of the Genetic Algorithms (GAs) in determining the optimal transformer tap setting for loss minimization using MATLAB / Mohd Syful Adly Abd. Wahab by Abd. Wahab, Mohd Syful Adly

    Published 2003
    “…This project report presents a new approach to the use of load flow by proposing the incorporation of the Genetic Algorithms (GAs) to search the optimal transformer tap setting in order to minimise the line losses. …”
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  8. 8
    Tap setting transformer for loss minimization using genetic algorithm / Mazyudin Mokhtar

    Tap setting transformer for loss minimization using genetic algorithm / Mazyudin Mokhtar by Mokhtar, Mazyudin

    Published 2007
    “…This paper presents an alternative approach to the use of load flow by incorporation Genetic Algorithm (GA) to search the optimal transformer tap setting in order to minimize losses. …”
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  9. 9
    Optimized clustering with modified K-means algorithm

    Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

    Published 2021
    “…Besides, some real data sets were examined to validate the proposed algorithm. …”
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  10. 10
    The new efficient and accurate attribute-oriented clustering algorithms for categorical data

    The new efficient and accurate attribute-oriented clustering algorithms for categorical data by Qin, Hongwu

    Published 2012
    “…Four real-life data sets obtained from University of California Irvine (UCI) machine learning repository and ten synthetically generated data sets are used to evaluate MGR and IG-ANMI algorithms, and other four algorithms are used to compare with these two algorithms. …”
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  11. 11
    Optimization of attribute selection model using bio-inspired algorithms

    Optimization of attribute selection model using bio-inspired algorithms by Basir, Mohammad Aizat, Yusof, Yuhanis, Hussin, Mohamed Saifullah

    Published 2019
    “…The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. …”
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  12. 12
    Efficient genetic partitioning-around-medoid algorithm for clustering

    Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

    Published 2019
    “…The rest sets of experiments carried out to evaluate the proposed algorithms. …”
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  13. 13
    LCSS-Based Algorithm for Computing Multivariate Data Set Similarity: A Case Study of Real-Time WSN Data

    LCSS-Based Algorithm for Computing Multivariate Data Set Similarity: A Case Study of Real-Time WSN Data by Khan, Rahim, Ali, Ihsan, Altowaijri, Saleh, Zakarya, Muhammad, Rahman, Atiq Ur, Ahmedy, Ismail, Khan, Anwar, Gani, Abdullah

    Published 2019
    “…To address this issue, we propose an efficient algorithm to measure the similarity indexes of multivariate data sets using a non-metric-based methodology. …”
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  14. 14
    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
    “…By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks.Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima.Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues.Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. …”
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  15. 15
    Detection and extraction features for signatures images via different techniques

    Detection and extraction features for signatures images via different techniques by Fatma Susilawati, M., Alsuhimat, F.M., Iqtait, M.

    Published 2019
    “…In this paper, three widely used feature detection algorithms, SURF, BRISK and FAST, these algorithms are compared to calculate the processing time and accuracy for set of signatures correctly. …”
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  16. 16
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…The K-means algorithm has been around for over a century. While a rather simplistic and dated algorithm, it remains widely used and taught till this day. …”
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  17. 17
    DTWFF-pitch feature and faster neural network convergence for speech recognition

    DTWFF-pitch feature and faster neural network convergence for speech recognition by Sudirman, Rubita, Salleh, Sh. Hussain, Salleh, Shaharuddin

    Published 2007
    “…Another task is to align the input frames (test set) to the reference template (training set) using DTW fixing frame (DTW-FF) algorithm. …”
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  18. 18
    A new ant based rule extraction algorithm for web classification

    A new ant based rule extraction algorithm for web classification by Ku-Mahamud, Ku Ruhana, Saian, Rizauddin

    Published 2011
    “…Using Classifier-based attribute subset selection will reduce more attributes, but sacrifice the performance of the classifier.A hybrid ant colony optimization with simulated annealing algorithm to discover rules from data is proposed.The simulated annealing technique will minimize the problem of low quality discovered rule by an ant in a colony.The best rule for a colony will then be chosen and later the best rule among the colonies will be included in the rule set.The best rule for a colony will then be chosen and later the best rule among the colonies will be included in the rule set.The rule set is arranged in decreasing order of generation.Thirteen data sets which consist of discrete and continuous data were used to evaluate the performance of the proposed algorithm in terms of accuracy, number of rules and number of terms in the rules.Experimental results obtained from the proposed algorithm are comparable to the results of the Ant-Miner algorithm in terms of rule accuracy but are better in terms of rule simplicity.…”
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  19. 19
    Pre-processing of input features using LPC and warping process

    Pre-processing of input features using LPC and warping process by Sudirman, Rubita, Sh-Hussain, Salleh, Ming, Ting Chee

    Published 2005
    “…Another task is to align the input frames (test set) to the reference template (training set) using a modified DTW algorithm called DTW fixing frame (DTW-FF)algorithm. …”
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  20. 20
    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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