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Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour
Published 2022“…With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. …”
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Proceeding Paper -
2
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Article -
3
Comparative assessment of rule-based and Bayes' theorem as inference engines in diagnosing symptoms for Islamic medication expert system
Published 2014“…An expert system for diagnosing sickness and suggesting treatment based on Islamic Medication (IM) was constructed using Rule Based (RB) and Bayes' theorem (BT) algorithms independently as its inference engine. …”
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Conference or Workshop Item -
4
Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
Published 2021“…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. …”
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Final Year Project -
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Classification of Diabetes Mellitus using Ensemble Algorithms
Published 2021“…Apart from that, ensemble methods such as bagging, boosting, hybrid classifier using combinations of Random Forest with other base classifiers and ensemble algorithm which is the Random Forest has also been studied. …”
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Conference or Workshop Item -
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Enhancing Classification Algorithms with Metaheuristic Technique
Published 2024“…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
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Article -
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Car recognition using Bayes Classifier / Nur Atiqah Mat Samadi
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Thesis -
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Utilize Medical Text Data to Estimate Disease Types by Using Naïve Bayes and ANN Classifier
Published 2021“…By using these two algorithms, the results of the classification of the kind of disease. …”
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Article -
9
A direct ensemble classifier for imbalanced multiclass learning
Published 2012“…A combiner method called OR-tree is used to combine the decisions obtained from the ensemble classifiers.The DECIML framework has been tested with several benchmark dataset and shows promising results.…”
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Conference or Workshop Item -
10
An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…An investigative study was undertaken to assess the efficiency of EB and EW by evaluating their classification performance using Naive Bayes and K-nearest neighbor algorithms on four continuous datasets sourced from the UCI datasets. …”
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Article -
11
An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…An investigative study was undertaken to assess the efficiency of EB and EW by evaluating their classification performance using Naive Bayes and K-nearest neighbor algorithms on four continuous datasets sourced from the UCI datasets. …”
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Article -
12
Multi-Objectives Memetic Discrete Differential Evolution Algorithm for Solving the Container Pre-Marshalling Problem
Published 2019“…This demonstrates that using the multi-objectives approach with a combination of the Discrete Differential Evolution mutation and the Memetic Algorithm evolutionary is a suitable approach for solving multi-objectives CPMP.…”
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Emotion recognition and analysis of netizens based on micro-blog during covid-19 epidemic
Published 2022“…This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. …”
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Article -
15
Ant system-based feature set partitioning algorithm for classifier ensemble construction
Published 2016“…In this study, Ant system-based feature set partitioning algorithm for classifier ensemble construction is proposed.The Ant System Algorithm is used to form an optimal feature set partition of the original training set which represents the number of classifiers.Experiments were carried out to construct several homogeneous classifier ensembles using nearest mean classifier, naive Bayes classifier, k-nearest neighbor and linear discriminant analysis as base classifier and majority voting technique as combiner. …”
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Article -
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An improved multiple classifier combination scheme for pattern classification
Published 2015“…In this study, an improved multiple classifier combination scheme is proposed. The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. …”
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Thesis -
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Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim
Published 2018“…By adopting object based approached instead of pixel based will avoid the 'salt and pepper' effect that will decrease the accuracy of land-cover classification. Using Worldview-2 multispectral satellite image as a primary data, together with ancillary data which include normalized Digital Surface Model (nDSM) derived from Light Detection and Ranging (LIDAR) data and indices layer, the image segmentation process utilizing multiresolution segmentation algorithm was conducted. …”
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18
Exploring the impact of social media on political discourse: a case study of the Makassar mayoral election
Published 2024“…A hybrid method is employed to combine the benefits of k-means, SVM, and naïve Bayes. …”
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Journal -
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Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Thesis -
20
Enhanced Image Classification for Defect Detection on Solar Photovoltaic Modules
Published 2023“…Methodologically, the entire study used a quantitative experiment technique. This research uses the Gaussian Naïve Bayes Algorithm using a ratio of training data and testing data of 70:30 resulting in an accuracy value of 46%. …”
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