Search Results - (( centered learning algorithm ) OR ( centered ((stemming algorithm) OR (clustering algorithm)) ))

Search alternatives:

Refine Results
  1. 1
    Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation

    Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation by Alia, Osama Moh’d Radi

    Published 2011
    “…However, two main issues plague these clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual clusters in the given dataset. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  2. 2
    Harmony search-based fuzzy clustering algorithms for image segmentation.

    Harmony search-based fuzzy clustering algorithms for image segmentation. by Alia, Osama Moh’d Radi

    Published 2011
    “…However, two main issues plague these clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual clusters in the given dataset. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  3. 3
    Penggunaan penggugusan subtraktif bagi menjana peraturan kabur

    Penggunaan penggugusan subtraktif bagi menjana peraturan kabur by Agus Priyono, Muhammad Ridwan, Ahmad Jais Alias, Riza Atiq O. K. Rahmat, Azmi Hassan, Mohd. Alauddin Mohd. Ali

    Published 2005
    “…Based on the study, it is found that the system was able to generate 8 cluster center at on 30(3x10) data value at 0.3 cluster radius and also able to generate 4 cluster center at 0.5 radius with average MSE of 0.005…”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  4. 4
    Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering

    Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering by Annisa Eka Haryati, ., Sugiyarto, Surono, Tommy Tanu, Wijaya, Goh, Khang Wen, Aris, Thobirin

    Published 2022
    “…Objective: Fuzzy clustering algorithm is a partition method used to assign objects from a data set to a cluster by marking the average location. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  5. 5
    A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective

    A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective by Ul haq, Riaz, Norrozila, Sulaiman, Muhammad, Alam

    Published 2013
    “…Machine learning algorithms are considered as an efficient way for decision making in computational environments. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  6. 6
    Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui

    Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui by Yang , Dong Rui

    Published 2019
    “…One of the major research problems is the computation resources required by machine learning algorithm used for classification for HAR. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  7. 7
    A hybrid Method Of Centroid-Based Clustering And Meta-Heuristic For Personalized Web Search

    A hybrid Method Of Centroid-Based Clustering And Meta-Heuristic For Personalized Web Search by Bourair Sadik Mohamad Taqi

    Published 2024
    thesis::doctoral thesis
    Save to List
    Saved in:
  8. 8
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  9. 9
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  10. 10
    RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION

    RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION by CATUR ANDRYANI, NUR AFNY

    Published 2010
    “…The proposed training algorithms discussed in this thesis are derived for fixed size RBF network and being compared with Extreme Learning Machine (ELM) as the ELM technique just randomly assigned centers and width of the hidden neurons and update the output connected weights. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  11. 11
    Penggunaan penggugusan subtraktif bagi menjana peraturan kabur

    Penggunaan penggugusan subtraktif bagi menjana peraturan kabur by Priyono, Agus, Ridwan, Purnomo, Alias, Ahmad Jais, Rahmat, Riza Atiq, Hassan, Azmi, Mohd Ali, Mohd Alaudin

    Published 2005
    “…Based on this study, it is found that the system was able to generate 8 cluster center on 30 (3 x 10) data value at 0.3 cluster radius and also able to generate 4 cluster center at 0.5 radit.rs with average MSE of 0.005). …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  12. 12
    Unsupervised chest X-ray opacity classification using minimal deep features

    Unsupervised chest X-ray opacity classification using minimal deep features by Che Azemin, Mohd Zulfaezal, Mohd Tamrin, Mohd Izzuddin, Md. Ali, Mohd. Adli, Jamaludin, Iqbal

    Published 2022
    “…A total of 3,504 CXRs were processed using a pre-trained deep learning convolutional neural network to output ten discriminatory features which were then used in the k-mean algorithm to find underlying similarities between the features for further clustering. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  13. 13
    A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning And Extended Mobility Features For VANETs

    A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning And Extended Mobility Features For VANETs by Talib, Mohammed Saad, Abdullah, Nihad Ibrahim, Hassan, Aslinda, Abal Abas, Zuraida, Mohammed Al-Khazraji, Ali Abdul-Jabbar, Alamery, Thamer, Ibrahim, Ali Jalil

    Published 2020
    “…An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively.…”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  14. 14
    Document clustering for knowledge discovery using nature-inspired algorithm

    Document clustering for knowledge discovery using nature-inspired algorithm by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

    Published 2014
    “…As the internet is overload with information, various knowledge based systems are now equipped with data analytics features that facilitate knowledge discovery.This includes the utilization of optimization algorithms that mimics the behavior of insects or animals.This paper presents an experiment on document clustering utilizing the Gravitation Firefly algorithm (GFA).The advantage of GFA is that clustering can be performed without a pre-defined value of k clusters.GFA determines the center of clusters by identifying documents with high force.Upon identification of the centers, clusters are created based on cosine similarity measurement.Experimental results demonstrated that GFA utilizing a random positioning of documents outperforms existing clustering algorithm such as Particles Swarm Optimization (PSO) and K-means.…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  15. 15
    Text-based emotion prediction system using machine learning approach

    Text-based emotion prediction system using machine learning approach by Ahmad Fakhri, Ab. Nasir, Eng, Seok Nee, Chun, Sern Choong, Ahmad Shahrizan, Abdul Ghani, Anwar, P. P. Abdul Majeed, Asrul, Adam, Mhd, Furqan

    Published 2020
    Get full text
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  16. 16
    MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm

    MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm by Balafar, Mohammad Ali, Ramli, Abdul Rahman, Saripan, M. Iqbal, Mahmud, Rozi, Mashohor, Syamsiah, Balafar, Hakimeh

    Published 2008
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  17. 17
    Adaptive firefly algorithm for hierarchical text clustering

    Adaptive firefly algorithm for hierarchical text clustering by Mohammed, Athraa Jasim

    Published 2016
    “…The proposed Adaptive Firefly Algorithm (AFA) consists of three components: document clustering, cluster refining, and cluster merging. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  18. 18
    Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz

    Cluster validity of the fuzzy C-means algorithm in mammographic image using adaptive cluster & partition entropy indexes / Azwani Aziz by Aziz, Azwani

    Published 2010
    “…This problem can be solved by cluster validity index. Cluster validity index is needed to find the suitable number of cluster, c in any fuzzy clustering algorithm. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  19. 19
    Incremental interval type-2 fuzzy clustering of data streams using single pass method

    Incremental interval type-2 fuzzy clustering of data streams using single pass method by Qaiyum, S., Aziz, I., Hasan, M.H., Khan, A.I., Almalawi, A.

    Published 2020
    “…Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  20. 20
    Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms

    Clustering High Dimensional Data Using Subspace And Projected Clustering Algorithms by Sembiring, Rahmat Widia, Jasni, Mohamad Zain, Abdullah, Embong

    Published 2010
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:

Search Tools: