Search Results - (( process classification model algorithm ) OR ( java application tools algorithm ))
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1
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. …”
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Thesis -
2
Provider independent cryptographic tools
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Monograph -
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Knowledge base processing method based on text classification algorithm
Published 2023“…The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. …”
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Conference or Workshop Item -
5
RSA Encryption & Decryption using JAVA
Published 2006“…The key is the trigger mechanism to the algorithm. Until the advent of the Internet, encryption was rarely used by the public, but was largely a military tool. …”
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Final Year Project -
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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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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. …”
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Article -
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Feature Selection with Harmony Search for Classification: A Review
Published 2021“…If the datasets contains irrelevant features, it will not only affect the training of the classification process but also the accuracy of the model. …”
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Proceeding -
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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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Article -
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Development of classification model between clean water and polluted water based on capacitance properties using Levenberg Marquardt (LM) algorithm of artificial neural network / M...
Published 2020“…These measurement inputs were then going through the process of classification in ANN to generate the optimized models by using LM algorithm. …”
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Student Project -
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Waste management using machine learning and deep learning algorithms
Published 2020“…So, we are proposing an automated waste classification problem utilizing Machine Learning and Deep Learning algorithms. …”
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Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif
Published 2014“…The objective of this study is to suggest the potential classification model for talent forecasting throughout some experiments using SVM learning algorithm. …”
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Research Reports -
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Classification of Citrus (Rutaceae) by Using Image Processing
Published 2019“…This research will be conducted by using digital image processing approach based on the morphological features of leaf with the combination of gray level co-occurrence matrix (GLCM), Prewitt and Canny algorithm and training classification models by using support vector machine (SVM). …”
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Undergraduate Final Project Report -
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Modified word representation vector based scalar weight for contextual text classification
Published 2024“…For this experiment, the modified word vectors serve as input to train a Machine Learning (ML) model for the text classification process, aiming for the developed ML model to have a significantly smaller parameter count. …”
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Thesis -
15
Academic leadership bio-inspired classification model using negative selection algorithm
Published 2015“…Managing employee’s competency is considered as the top challenge for human resource professional especially in the process to determine the right person for the right job that is based on their competency.As an alternative approach, this article attempts to propose academic leadership bio-inspired classification model using negative selection algorithm to handle this issue.This study consists of three phases; data preparation, model development and model analysis. …”
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Conference or Workshop Item -
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Direct approach for mining association rules from structured XML data
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Ideal combination feature selection model for classification problem based on bio-inspired approach
Published 2020“…Feature selection or attribute reduction is a crucial process to achieve optimal data reduction for classification task. …”
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Book Section -
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Classification models for higher learning scholarship award decisions
Published 2018“…Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. …”
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Article -
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Text spam messages classification using Artificial Immune System (AIS) algorithms
Published 2024thesis::master thesis -
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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