Search Results - (( generating application testing algorithm ) OR ( java application mining algorithm ))

Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…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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Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms by Lee, Quan

“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

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An improved algorithm in test case generation from UML activity diagram using activity path by Hashim, Nor Laily, Salman, Yasir D.

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An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion by Haque, Ariful

“…The elaborated result of the study will help test engineers to choose the algorithm they need to generate test cases efficiently and optimally.…”
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Test suite generation based on hybrid flower pollination algorithm and hill climbing by Nasser, Abdullah B., Nor Wardah, Mohd Nasir, Kamal Zuhairi, Zamli, Waheeda Ali, H. M. Ghanem, Fakhrud, Din

“…The application of meta-heuristic algorithms in t-way tests generation, as an example of SBST, has as of late gotten to be predominant. …”
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Q-learning whale optimization algorithm for test suite generation with constraints support by Hassan, Ali Abdullah, Salwani, Abdullah, Kamal Z., Zamli, Rozilawati, Razali

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Evaluating Bees Algorithm for Sequence-based T-way Testing Test Data Generation by M. H., Mohamed Zabil, Kamal Z., Zamli, K. C., Lim

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Combinatorial test suites generation strategy utilizing the whale optimization algorithm by Ali Abdullah, Hassan, Salwani, Abdullah, Kamal Zuhairi, Zamli, Rozilawati, Razali

“…In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. …”
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Development of automated neighborhood pattern sensitive faults syndrome generator for static random access memory by Rusli, Julie Roslita

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Hybrid flower pollination algorithm strategies for t-way test suite generation by Abdullah, Nasser, Kamal Z., Zamli, Alsewari, Abdulrahman A., Ahmed, Bestoun S.

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March-based diagnosis algorithm for static random-access memory stuck-at faults and transition faults by Mat Isa, Masnita

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Development of a Tuneable Test Problem Generator for Assembly Sequence Planning and Assembly Line Balancing by M. F. F., Ab Rashid, Hutabarat, Windo, Tiwari, Ashutosh

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A Pairwise Test Suite Generator based on Melody Search Algorithm by Toh, S. Yuen, Al-Omoush, Ala’a A., Foo, W. Wen, Goh, G. Hau, Alsewari, Abdulrahman A.

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Latin hypercube sampling Jaya algorithm based strategy for T-way test suite generation by Nasser, Abdullah B., Abdul-Qawy, Antar S. H., Abdullah, Nibras, Hujainah, Fadhl, Kamal Z., Zamli, Ghanem, Waheed A. H. M.

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Faculty timetabling using genetic algorithm by Liong, Boon Yaun

“…Faculty Timetabling using Genetic Algorithm (FTGA) is an application that generate optimum timetable for faculty.The target user of this application is faculty staff who responsible in generate timetable.The problem statement of the project is many clashing exist in the timetable.Faculty staff needs to solve the clashing manually.This will waste time and it is a problem for staff to solve the clashing.By implement GA,clashing will be reduced.The objective of the project is to develop aprototype in scheduling application for generates an optimum timetable for a faculty.Genetic algorithm will be implemented.The scope of FTGA is Faculty of Computer Systems & Software Engineering (FCSSE).The methodology use in this project is prototype model.The testing result show 95 out of 100 test cases achieved the maximum fitness value which means there is no clashing in the timetable.The maximum generation is set to 15 generation.Population for each generation is 3 populations.Percentage of FTGA solve the problem is 95%.…”
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