Search Results - (( process visualization means algorithm ) OR ( java application mining algorithm ))

Refine Results
  1. 1
  2. 2
  3. 3

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

    Published 2019
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
    Get full text
    Get full text
    Get full text
    Article
  4. 4

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  5. 5

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

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
    Get full text
    Get full text
    Final Year Project / Dissertation / Thesis
  6. 6

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In cluster labelling process, a cluster labelling algorithm based on calculation of minimum-distance (MD) between cluster mean and class mean was developed to label the clusters. …”
    Get full text
    Get full text
    Thesis
  7. 7

    Implementation of Parallel K-Means Algorithm to Estimate Adhesion Failure in Warm Mix Asphalt by Akhtar, M.N., Ahmed, W., Kakar, M.R., Bakar, E.A., Othman, A.R., Bueno, M.

    Published 2020
    “…To attain high accuracy from image processing algorithms, the loss of pixels plays an essential role. …”
    Get full text
    Get full text
    Article
  8. 8
  9. 9

    GMSD-based perceptually motivated non-local means filter for image denoising by Baqar, Mohtashim *, Lau, Sian Lun *

    Published 2019
    “…Further, the proposed methodology also helps in mitigating the patch jittering blur effect (PJBE) and over smoothing of denoised images as observed with conventional NLM algorithm. Experimental evaluations based on visual-quality assessment and least-square based metrics have shown that the proposed algorithm yields better denoised image estimates than the conventional NLM algorithm. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  10. 10

    Modified Contrast Limited Adaptive Histogram Equalization for high dynamic range images by Tung, Li Qian

    Published 2012
    “…As a result, a fully automatic local tone mapping algorithm was introduced to increase the local contrast and reduce the loss of visual visibility. …”
    Get full text
    Get full text
    Thesis
  11. 11

    Visualization of dengue incidences using expectation maximization (EM) algorithm by Mathur, N., Asirvadam, V.S., Dass, S.C., Gill, B.S.

    Published 2017
    “…Along with the prediction modeling on data using centroid model and distribution model based on K-means and Expectation Maximization (EM) algorithms respectively. …”
    Get full text
    Get full text
    Article
  12. 12
  13. 13

    Drone Based Image Processing For Precision Agriculture by Sharif, Muhammad Arif Syafiq Md

    Published 2019
    “…At first, the parallel K-means clustering algorithm was applied on the acquired image to segregate various components acquired using UAV. …”
    Get full text
    Get full text
    Monograph
  14. 14

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
    Get full text
    Get full text
    Thesis
  15. 15

    A hybrid spiking neural network model for multivariate data classification and visualization. by Ming, Leong Yii, Teh, Chee Siong, Chen, Chwen Jen

    Published 2011
    “…Recently, many extensions for SOM have been proposed for temporal processing. However, none of the extensions uses spikes as means of information processing. …”
    Get full text
    Get full text
    Get full text
    Proceeding
  16. 16
  17. 17

    Signal Noise Removal using Concurrent Algorithm by Hammuzamer Irwan , Hamzah, Azween, Abdullah

    Published 2008
    “…This research is in the early phase to solve the problem of how to develop a signal noise removal process using concurrent algorithm. The solution of this problem is shown by producing a high level conceptual model to visualize the architecture of this research. …”
    Get full text
    Get full text
    Conference or Workshop Item
  18. 18

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Huong, Audrey, Sathasivam, Saratha, Majahar Ali, Majid Khan

    Published 2023
    “…With the use of SRBFNN-2SAT, a training method based on these algorithms has been presented, then training has been compared among algorithms, which were applied in Microsoft Visual C++ software using multiple metrics of performance, including Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Systematic Error (SD), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). …”
    Get full text
    Get full text
    Get full text
    Article
  19. 19

    Extremal region selection for MSER detection in food recognition by Razali, Mohd Norhisham, Manshor, Noridayu, Abdul Halin, Alfian, Mustapha, Norwati, Yaakob, Razali

    Published 2021
    “…These interest points are considered as noises that lead to computation burden in the overall recognition process. Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  20. 20