Search Results - (( using vectorization machine algorithm ) OR ( data visualization based algorithm ))

Search alternatives:

Refine Results
  1. 1

    Sentiment analysis on national cultural tourism using Linear Support Vector Machine (LSVM) / Nur Haida Hanna Samsuddin by Samsuddin, Nur Haida Hanna

    Published 2020
    “…The output will be the accuracy of the LSVM model and the visualization of sentiment analysis of new data that user will choose in the prototype. …”
    Get full text
    Get full text
    Thesis
  2. 2

    Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

    Published 2024
    “…The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  3. 3

    Wearable based-sensor fall detection system using machine learning algorithm by Ishak, Anis Nadia, Habaebi, Mohamed Hadi, Yusoff, Siti Hajar, Islam, Md. Rafiqul

    Published 2021
    “…In this project, a wearable sensor-based fall detection system using a machine-learning algorithm had been developed. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Proceeding Paper
  4. 4

    Sentiment Analysis of Sexual Harassment in Malaysia on Twitter Using Machine Learning Algorithms by Nurellezia, Suleiman

    Published 2023
    “…The transformed data is then modelled using machine learning algorithms such as Naïve Bayes classifier and Support Vector Machine to predict the overall sentiment of tweets, in which the finding depicted an overall positive sentiment surrounding the issue. …”
    Get full text
    Get full text
    Final Year Project Report / IMRAD
  5. 5

    Fast shot boundary detection based on separable moments and support vector machine by Idan, Zinah N., Abdulhussain, Sadiq H., Mahmmod, Basheera M., Al-Utaibi, Khaled A., Syed Abdul Rahman Al-, Syed Abdul Rahman Al-Hadad, Sait, Sadiq M.

    Published 2021
    “…As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. …”
    Get full text
    Get full text
    Get full text
    Article
  6. 6

    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network by Zafar, R., Kamel, N., Naufal, M., Malik, A.S., Dass, S.C., Ahmad, R.F., Abdullah, J.M., Reza, F.

    Published 2017
    “…General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). …”
    Get full text
    Get full text
    Article
  7. 7

    High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor by Abdul Rashid, Raghdah Rasyidah, Shaharudin, Shazlyn Milleana, Sulaiman, Nurul Ainina Filza, Zainuddin, Nurul Hila, Mahdin, Hairulnizam, Mohd Najib, Summayah Aimi, Hidayat, Rahmat

    Published 2024
    “…We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. …”
    Get full text
    Get full text
    Article
  8. 8

    Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization by Nanyonga Aziida, Sorayya Malek, Firdaus Aziz, Khairul Shafiq Ibrahim, Sazzli Kasim

    Published 2021
    “…Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. …”
    Get full text
    Get full text
    Get full text
    Article
  9. 9

    Predictive analytics for the sentiment of malaysian place of interest using machine learning models by Qiryn Adriana, Kharul Zaman

    Published 2023
    “…The data was then divided into training and testing sets, and was trained using three different supervised learning algorithms, namely Support Vector Machine, Random Forest, and Naive Bayes. …”
    Get full text
    Get full text
    Undergraduates Project Papers
  10. 10

    Exploration of COVID‑19 data in Malaysia through mapper graph by Carey Ling, Yu Fan, Piau, Phang, Liew, Siaw Hong, Vivek Jason, Jayaraj, Benchawan, Wiwatanapataphee

    Published 2024
    “…To keep with the expanding quantity and complexity of data while employing minimal assumptions, a topological data analysis tool known as the Mapper algorithm is used to explore Malaysia’s daily confirmed cases, deaths, and vaccination data from the onset of the pandemic to June 2022 via data visualization and clustering. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  11. 11

    Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

    Published 2020
    “…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
    Get full text
    Get full text
    Get full text
    Thesis
  12. 12

    Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment by Al-Doski, Jwan M. Mohammed

    Published 2013
    “…In conclusion, hybrid classification as a combination of k-means and support vector machine algorithms and post-classification comparison change detection technique can be used to monitor land cover changes in Halabja city, Iraq. …”
    Get full text
    Get full text
    Thesis
  13. 13
  14. 14

    Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

    Published 2020
    “…Furthermore, the efficacy of different models based on heuristic hyperparameter tuning is evaluated in which the different kernel function for Support Vector Machine, various distance metrics of k-Nearest Neighbors. …”
    Get full text
    Get full text
    Thesis
  15. 15

    Sentiment analysis on the place of interest in Malaysia by Qiryn Adriana, Khairul Zaman, Wan Nur Syahidah, Wan Yusoff, Qistina Batrisyia, Azman Shah

    Published 2025
    “…Pre-processing techniques and Natural Language Processing (NLP) methods were applied to handle missing values and prepare the text data for analysis. The dataset was then split into training and testing sets, and three supervised learning algorithms which are Support Vector Machine, Random Forest, and Naive Bayes were employed to evaluate the sentiment analysis models. …”
    Get full text
    Get full text
    Get full text
    Article
  16. 16

    P300 detection of brain signals using a combination of wavelet transform techniques by Motlagh, Farid Esmaeili

    Published 2012
    “…Wavelet transform (WT), student’s two-sample t-statistic (T-Test) and support vector machines (SVM) used in designing the algorithms. …”
    Get full text
    Get full text
    Thesis
  17. 17
  18. 18
  19. 19
  20. 20

    Optimizing support vector machine parameters using continuous ant colony optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous parameters, there is a need to discretize the continuous value into a discrete value.This discretization process results in loss of some information and, hence, affects the classification accuracy and seek time.This study proposes an algorithm to optimize Support Vector Machine parameters using continuous Ant Colony Optimization without the need to discretize continuous values for Support Vector Machine parameters.Seven datasets from UCI were used to evaluate the performance of the proposed hybrid algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques.Experimental results of the proposed algorithm also show promising performance in terms of computational speed.…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item