Search Results - (( parallel optimization path algorithm ) OR ( using classification modeling algorithm ))
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Tool path generation of contour parallel based on ant colony optimisation
Published 2016“…An Ant Colony Optimisation (ACO) method is used to optimize the tool path length because of its capability to find the shortest tool path length. …”
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Minimizing machining airtime motion with an ant colony algorithm
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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. …”
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Restoration planning strategy of transmission system based on optimal energizing time of sectionalizing islands / Dian Najihah Abu Talib
Published 2019“…There are two discrete optimization techniques used in this work, which are the Artificial Bee Colony algorithm and Evolutionary Programming. …”
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Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…EM and K-means clustering algorithms are used to cluster the multi-class classification attribute according to its relevance criteria and afterward, the clustered attributes are classified using an ensemble random forest classifier model. …”
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An adaptive ant colony optimization algorithm for rule-based classification
Published 2020“…Various classification algorithms have been developed to produce classification models with high accuracy. …”
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Predicting breast cancer using ant colony optimisation / Siti Sarah Aqilah Che Ani
Published 2021“…This study implements a machine learning algorithm called Ant Colony Optimization (ACO) algorithm to develop an accurate classification model for predicting breast cancer cells. …”
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Student Project -
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Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Classification model for water quality using machine learning techniques
Published 2015“…In assessing the result, the Lazy model using K Star algorithm was the best classification model among the five models had the most outstanding accuracy of 86.67%. …”
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Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
Published 2021“…Whereas for the German Frankfurt dataset, best DM classification model was found using Random Forest algorithm with an accuracy of 98.77%.…”
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Final Year Project -
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Academic leadership bio-inspired classification model using negative selection algorithm
Published 2015“…Several experiments were carried out by using different set of training and testing data-sets to evaluate the accuracy of the proposed model.As a result, the accuracy of the proposed model is considered excellent for academic leadership classification.For future work, in order to enhance the proposed bio-inspired classification model, a comparative study should be conducted using other established artificial immune system classification algorithms i.e. clonal selection and artificial immune network.…”
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Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
Published 2019“…Furthermore, most classification algorithms, using either fuzzy or non-fuzzy approaches, produce results in the form of crisp or categorical classification outcomes. …”
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Daisy species classification based on image using Convolutional Neural Network algorithm / Haris Hidayatullah Khaimuza
Published 2024“…Second objective is to develop the prototype of daisy species classification based on image using CNN algorithm. The last objective is to evaluate the accuracy of CNN model in the daisy species classification based on image. …”
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Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…In order to solve it, Bat Algorithm method is implement in to optimization method in fuzzy modeling for classification. …”
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Undergraduates Project Papers -
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Performance comparison of CNN and LSTM algorithms for arrhythmia classification
Published 2020“…Among the existing deep learning model, convolutional neural network (CNN) and long short-term memory (LSTM) algorithms are extensively used for arrhythmia classification. …”
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…For a fair performance evaluation, the selection of the best peak model requires experimental exploration by using a common and unbiased classification approach. …”
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Symmetric Key Size for Different Level of Information Classification
Published 2006“…By using this model, we then propose key sizes for different levels of information classification.…”
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Development of classification algorithms of human gait
Published 2022“…In addtion, after compared with other state-of-the-arts of gait classification algorithms, our proposed classification algorithm produced comparable results with other state-of-arts using a smaller dataset with fewer training features.…”
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Final Year Project / Dissertation / Thesis -
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Intent-IQ: customer’s reviews intent recognition using random forest algorithm
Published 2025“…Two machine learning model is chosen to build the classification models which are Random Forest (RF) algorithm and Multinomial Naïve Bayes (MNB) algorithm. …”
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