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1
Arabic text classification using hybrid feature selection method using chi-square binary artificial bee colony algorithm
Published 2021“…After that, the wrapper method, Artificial Bee Colony algorithm, is used as the second level where Naive Base is used as a fitness function. …”
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2
Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…Even a normal people using clustering to grouping their data. …”
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3
SANAsms: Secure short messaging system for secure GSM mobile communication
Published 2008“…The system is developed using Java 2 Micro Edition (J2ME) which is written in Java. …”
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4
Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
Published 2018“…This research selected three commonly used swarm-based metaheuristic algorithms – Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) – to perform component-wise analysis. …”
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5
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Electing the best set of features will help to improve the classifier predictions in terms of the normal and abnormal pattern. The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. …”
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6
Challenges of hidden data in the unused area two within executable files
Published 2009“…The designed algorithms were intended to help in proposed system aim to hide and retract information (data file) with in unused area 2 of any execution file(exe.file). …”
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7
Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions
Published 2017“…Two sets of experimental data involving 20 diabetic patients and 20 healthy subjects were collected from CITO laboratory Semarang Central Java, Indonesia. …”
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8
Design And Implementation Of Human Crowd Density Estimation System With Energy Harvesting In Wireless Sensor Network Platform
Published 2017“…These factors are then integrated into the proposed H-CDE algorithm. The H-CDE algorithm and its crowd classification yielded an average of 71.2 % accuracy in identifying the level of crowd density, which is the best compared to other algorithms found in the literature. …”
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