Search Results - (( program data selection algorithm ) OR ( java data classification algorithm ))
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Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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Undergraduates Project Papers -
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Classification System for Heart Disease Using Bayesian Classifier
Published 2007“…This system was developing base on to three main part which is data processing, testing and implementation of the algorithm. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…Major problems in classification task are large amount of training data, large number of features and different behavior of data streams that reduce accuracy and increase computational cost in classifier training phase. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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A web-based implementation of k-means algorithms
Published 2022“…Firstly, k-luster could incorporate additional clustering algorithms, or even classification algorithms in the future. …”
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Final Year Project / Dissertation / Thesis -
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Case Slicing Technique for Feature Selection
Published 2004“…Since the 1960s, many algorithms for data classification have been proposed. …”
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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. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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Automated path testing using the negative selection algorithm
Published 2017“…Negative selection algorithm (NSA) has been used in this paper to generate test data for path testing automatically. …”
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Indexed Article -
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Development of decentralized data fusion algorithm with optimized kalman filter.
Published 2016“…This thesis proposes a data fusion model that facilitates selection of algorithm and recommends selected algorithm to be optimized. …”
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Intelligent agent for e-commerce using genetic algorithm / Kok Sun Sun
Published 2000“…Whereas the process of examining through the web pages, retrieving and searching the relevant data in a liTML page, and selecting the best satisfying data are based on the features and operations of the Genetic Algorithms.…”
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Development Of Machine Learning User Interface For Pump Diagnostics
Published 2022“…Machine Learning is one of the ways as a preventive method by applying the data collected from the clogging experiment in the vibration lab to build up a machine learning model for classification of flow blockage levels in the centrifugal pump. …”
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Monograph -
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Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning
Published 2024Conference Paper -
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System identification using Extended Kalman Filter
Published 2017“…Besides, Extended Kalman Filter (EKF) algorithm was selected in this project as an algorithm for offline estimation data purposes. …”
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Student Project -
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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions
Published 2017“…Future work should be focusing on data collection of the E-Nose sensors and the improvement of the learning algorithm robustness towards environmental noise during data acquisition, such as evaporation and contamination of odor samples.…”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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