Search Results - (( problem using ((task algorithm) OR (tree algorithm)) ) OR ( java application sensor algorithm ))

Refine Results
  1. 1

    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
  2. 2

    Wireless sensor nodes deployment using multi-robot based on improved spanning tree algorithm by Arezoumand, Reza

    Published 2015
    “…One of the solutions to this problem is by using mobile robots with concern on exploration algorithm for mobile robot. …”
    Get full text
    Get full text
    Thesis
  3. 3
  4. 4

    An efficient and effective case classification method based on slicing by Shiba, Omar A. A., Sulaiman, Md. Nasir, Mamat, Ali, Ahmad, Fatimah

    Published 2006
    “…The algorithms are: Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5). …”
    Get full text
    Get full text
    Get full text
    Article
  5. 5
  6. 6

    Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking by Boroujeni, Farsad Zamani

    Published 2012
    “…Over the last decade, many algorithms have been developed to address this problem. …”
    Get full text
    Get full text
    Thesis
  7. 7

    An Intelligent System Approach to the Dynamic Hybrid Robot Control by Md. Yeasin, Md. Mahmud Hasan

    Published 1996
    “…The problem was considered as a blind-tracking task by a human.…”
    Get full text
    Get full text
    Thesis
  8. 8

    Waste management using machine learning and deep learning algorithms by Sami, Khan Nasik, Amin, Zian Md Afique, Hassan, Raini

    Published 2020
    “…The model that we have used are the classification models. For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  9. 9

    A direct ensemble classifier for imbalanced multiclass learning by Sainin, Mohd Shamrie, Alfred, Rayner

    Published 2012
    “…Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain.Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers.In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  10. 10

    Optimized tree-classification algorithm for classification of protein sequences by Iqbal, M.J., Faye, I., Said, A.M., Belhaouari Samir, B.

    Published 2016
    “…In this work, we have proposed an optimized tree-classification technique which uses cluster k nearest neighbor classification algorithm to classify protein sequences into superfamilies. …”
    Get full text
    Get full text
    Conference or Workshop Item
  11. 11

    Optimized tree-classification algorithm for classification of protein sequences by Iqbal, M.J., Faye, I., Said, A.M., Belhaouari Samir, B.

    Published 2016
    “…In this work, we have proposed an optimized tree-classification technique which uses cluster k nearest neighbor classification algorithm to classify protein sequences into superfamilies. …”
    Get full text
    Get full text
    Conference or Workshop Item
  12. 12

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
    Get full text
    Get full text
    Get full text
    Thesis
  13. 13

    Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem by Kader, Nur Izzati Ab

    Published 2019
    “…Due to the success of many classification problems been proposed with good result, k-Nearest Neighbour, Artificial Neural Network, and Support Vector Machine algorithms are used in this research.…”
    Get full text
    Get full text
    Thesis
  14. 14

    Energy-aware task scheduling for streaming applications on NoC-based MPSoCs by Abd Ishak, Suhaimi Abd Ishak, Wu, Hui, Tariq, Umair Ullah

    Published 2024
    “…Our approach is supported by a set of novel techniques, which include constructing an initial schedule based on a list scheduling where the priority of each task is its approximate successor-tree-consistent deadline such that the workload across all the processors is balanced, a retiming heuristic to transform intraperiod dependencies into inter-period dependencies for enhancing parallelism, assigning an optimal discrete frequency for each task and each message using a Non-Linear Programming (NLP)-based algorithm and an Integer-Linear Programming (ILP)-based algorithm, and an incremental approach to reduce the memory usage of the retimed schedule in case of memory size violations. …”
    Get full text
    Get full text
    Get full text
    Article
  15. 15

    The effectiveness of bottom up technique with probabilistic approach for a Malay parser by Muhammad Azhar Fairuzz Hiloh, Mohd Juzaiddin Ab Aziz, Lailatul Qadri Zakaria

    Published 2018
    “…However, a problem occurs when the parsing process produces two or more parse trees in which the parser unable to represent a precise parse tree. …”
    Get full text
    Get full text
    Get full text
    Article
  16. 16

    Fraud detection in shipping industry based on location using machine learning comparison techniques by Ganesan Subramaniam, Mr.

    Published 2023
    “…A number of popular existing algorithms were used to execute the model developed in Rapid tool such as Naïve Bayes , Neural Net , Deep Learning, Decision Tree, Logistic Regression, SVM and k-NN. …”
    text::Thesis
  17. 17

    Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation by Lateh, Masitah bdul

    Published 2020
    “…To test the effectiveness of the proposed algorithm, the real and generated samples is added to training phase to build a prediction model using M5 Model Tree. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  18. 18

    Development of a Reliable Multicast Protocol in Mobile Ad Hoc Networks by Alahdal, Tariq A. A.

    Published 2008
    “…The first algorithm is developed to avoid the buffer overflow in the FS nodes. …”
    Get full text
    Get full text
    Thesis
  19. 19

    Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm by Muhammad Syahril Mohamad Sainal

    Published 2022
    “…Task scheduling problem also categorized as NP-hard problem where optimization technique can be used to solve it. …”
    Get full text
    Get full text
    Get full text
    Academic Exercise
  20. 20

    Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd by Nur Farahaina, Idris

    Published 2022
    “…One of the most powerful machine learning methods to handle classification problems is the decision tree. There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. …”
    Get full text
    Get full text
    Thesis