Search Results - (( using rough tree algorithm ) OR ( java application testing algorithm ))

A safe overtaking control scheme for autonomous vehicles using rapid-exploration random tree by Yincong Ma, Kit Guan Lim, Min Keng Tan, Helen Sin Ee Chuo, Lorita Angeline, Kenneth Tze Kin Teo

“…The improved algorithm is used to obtain the rough obstacle avoidance of the vehicle. …”
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Knowledge discovery in distance relay event report: a comparative data-mining strategy of rough set theory with decision tree by Othman, Mohammad Lutfi, Aris, Ishak, Abdullah, Senan Mahmood, Ali, Md. Liakot, Othman, Mohammad Ridzal

“…This paper addresses these issues by intelligently divulging the knowledge hidden in the relay recorded event report using a data-mining strategy based on rough set theory and a rule-quality measure under supervised learning to discover the relay decision algorithm and association rule. …”
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RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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Discovering decision algorithm of distance protective relay based on rough set theory and rule quality measure by Othman, Mohamad Lutfi

“…The discovered decision algorithm and association rule from the Rough-Set based data mining had been compared with and successfully validated by those discovered using the benchmarking Decision-Tree based data mining strategy. …”
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Comparing the knowledge quality in rough classifier and decision tree classifier by Mohamad Mohsin, Mohamad Farhan, Abd Wahab, Mohd Helmy

“…This paper presents a comparative study of two rule based classifier; rough set (Rc) and decision tree (DTc).Both techniques apply different approach to perform classification but produce same structure of output with comparable result. …”
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Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

“…The classification accuracy obtained from the CST method is compared to other selected classification methods such as Value Difference Metric (VDM), Pre-Category Feature Importance (PCF), Cross-Category Feature Importance (CCF), Instance-Based Algorithm (IB4), Decision Tree Algorithms such as Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5), Rough Set methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) and Neural Network methods such as the Multilayer method.…”
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New Learning Models for Generating Classification Rules Based on Rough Set Approach by Al Shalabi, Luai Abdel Lateef

“…Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzzy logic, genetic algorithm, rough set theory, and others. …”
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Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application by Yana Aditia, Gerhana, Nur, Lukman, Arief Fatchul, Huda, Cecep Nurul, Alam, Undang, Syaripudin, Devi, Novitasari

“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Java based expert system for selection of natural fibre composite materials by Ahmed Ali, Basheer A., Salit, Mohd Sapuan, Zainudin, Edi Syams, Othman, Mohamed

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Implementation of (AES) Advanced Encryption Standard algorithm in communication application by Moh, Heng Huong

“…The concept of ABS algorithm was firstly studied, including the definition, historical background, and a brief comparison was made between the ABS algorithm with other types of algorithm. …”
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Classification System for Heart Disease Using Bayesian Classifier by Magendram, Anusha

“…This system was developing base on to three main part which is data processing, testing and implementation of the algorithm. In this system a Bayesian algorithm was used in order to implement the system. …”
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Input-output based relation combinatorial testing using whale optimization algorithm for generating near optimum number of test suite by Suali, Anjila J., Nuraminah Ramli, Rozmie Razif Othman, Hasneeza Liza Zakaria, Iszaidy Ismail, Nor Shahida Mohd Jamail, Rimuljo Hendradi, Nurol Husna Che Rose

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A comparative study between rough and decision tree classifiers by Mohamad Mohsin, Mohamad Farhan

“…Theoretically, a good set of knowledge should provide good accuracy when dealing with new cases.Besides accuracy, a good rule set must also has a minimum number of rules and each rule should be short as possible.It is often that a rule set contains smaller quantity of rules but they usually have more conditions.An ideal model should be able to produces fewer, shorter rule and classify new data with good accuracy.Consequently, the quality and compact knowledge will contribute manager with a good decision model.Because of that, the search for appropriate data mining approach which can provide quality knowledge is important.Rough classifier (RC) and decision tree classifier (DTC) are categorized as RBC.The purpose of this study is to investigate the capability of RC and DTC in generating quality knowledge which leads to the good accuracy.To achieve that, both classifiers are compared based on four measurements that are accuracy of the classification, the number of rule, the length of rule, and the coverage of rule.Five dataset from UCI Machine Learning namely United States Congressional Voting Records, Credit Approval, Wisconsin Diagnostic Breast Cancer, Pima Indians Diabetes Database, and Vehicle Silhouettes are chosen as data experiment.All datasets were mined using RC toolkit namely ROSETTA while C4.5 algorithm in WEKA application was chosen as DTC rule generator.The experimental results indicated that both classifiers produced good classification result and had generated quality rule in different types of model – higher accuracy, fewer rule, shorter rule, and higher coverage.In term of accuracy, RC obtained higher accuracy in average while DTC significantly generated lower number of rule than RC.In term of rule length, RC produced compact and shorter rule than DTC and the length is not significantly different.Meanwhile, RC has better coverage than DTC.Final conclusion can be decided as follows “If the user interested at a variety of rule pattern with a good accuracy and the number of rule is not important, RC is the best solution whereas if the user looks for fewer nr, DTC might be the best choice”…”
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A Model for Evaluation of Cryptography Algorithm on UUM Portal by Norliana, Abdul Majid

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Propositional satisfiability method in rough classification modeling for data mining by Abu Bakar, Azuraliza

“…The classification accuracy, the number of rules and the maximum length of rules obtained from the SIPIDRIP method was compared with other rough set method such as Genetic Algorithm (GA), Johnson, Holte l R, Dynamic and Exhaustive method. …”
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Encrypted mobile messaging application using AES block cipher cryptography algorithm / Nur Rasyiqah Zulkifli by Zulkifli, Nur Rasyiqah

“…The programming language that is used to write the program is Java Language that will run on android phones. As the result, this project was tested on the functionality of the mobile messaging application. …”
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Design Of Robot Motion Planning Algorithm For Wall Following Robot by Ali Hassan, Muhamad Khairul

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Near-infrared spectroscopy modeling of combustion characteristics in chip and ground biomass from fast-growing trees and agricultural residue by Shrestha, Bijendra, Posom, Jetsada, Pornchaloempong, Pimpen, Sirisomboon, Panmanas, Shrestha, Bim Prasad, Ariffin, Hidayah

“…The remaining models (Di in chip and ground, Df, and Si in chip, and Ci in chip and ground biomass) are primarily applicable only for rough screening purposes. However, including more representative samples and exploring a more suitable machine learning algorithm are essential for updating the model to achieve a better nondestructive assessment of biomass combustion behavior.…”
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Security Analysis Between Static And Dynamic S-boxes In Block Ciphers by Nur Hafiza Zakaria, Azuan Ahmad, Azni Hazlizan Ab Halim, Farida Hazwani Mohd Ridzuan

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