Search Results - (( developing data selection algorithm ) OR ( java validation using algorithm ))
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1
The implications for ahybrid detection technique against malicious sqlattacks on web applications
Published 2025“…The methodology is based on JavaScript and PHP languages for developing a new technique called DetectCombined capable of filtering queries using parameterized queries to protect against SQL injection which is a safe method. …”
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2
Priority and dynamic quantum time algorithms for central processing unit scheduling
Published 2018“…Central Processing Unit is scheduled using different types of scheduling algorithms. One of the most widely algorithm used in scheduling with sharing and batch operating systems is Round Robin. …”
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Thesis -
3
A Model for Evaluation of Cryptography Algorithm on UUM Portal
Published 2004“…The simulation model was developed using Active Server Page, JavaScript and SQL 7.0 as database. …”
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4
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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5
Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…In cluster generating process, the developed BBSI algorithm was used to select the best band combination for generating cluster by using Iterative self- Organizing Data Analysis (ISODATA) technique. …”
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6
AGENT MEETING SCHEDULER
Published 2011“…Qualitative research method is being used to gather necessary data on agent algorithm and this data will be used to select the suitable algorithm. …”
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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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8
SURE-Autometrics algorithm for model selection in multiple equations
Published 2016“…The SURE-Autometrics is also validated using two sets of real data by comparing the forecast error measures with five model selection algorithms and three non-algorithm procedures. …”
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9
Prevention And Detection Mechanism For Security In Passive Rfid System
Published 2013“…In addition, the randomness of the session key generated from the MLCG is verified using NIST test suite. Besides that, the security of the proposed protocol is validated using the formal analysis tool, AVISPA. …”
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10
Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan
Published 2017“…This study has shown how the Negative Selection Algorithm can diagnose the skin cancer based on the input image and extracted data that has been provided. …”
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11
Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat
Published 2012“…Clonal selection algorithm was used in this project because there are no researcher are focus on that technique for classify twitter’s data. …”
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12
Enhancement of bearing defect diagnosis via genetic algorithm optimized feature selection
Published 2015“…This is due to the features selected in this classifier is over-fitted to the training data and not generalized for variations in testing data. …”
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Sentiment classification for malay newspaper using clonal selection algorithm / Nur Fitri Nabila Mohamad Nasir
Published 2013“…In this study, sentiment classifier using clonal algorithm selection was developed to categorize sentiment in Malay newspaper (Berita Harian). …”
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14
A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm
Published 2024“…Ten knowledge and domain experts provided positive feedback on the framework's verification, affirming the framework's robustness. Validation was conducted through three experiments involving four Java programs. …”
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15
The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
Published 2020“…Therefore, we conclude that the proposed BFOA-G is the best algorithm in the study, and PG approach is recommended for further development of BFOA as an IS algorithm.…”
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16
Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…However, these semi-supervised multi-task selection feature algorithms are unable to naturally handle the multi-view data since they are designed to deal with single-view data. …”
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17
Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…On the Construction phase, the development of the prototype has been started. All the algorithm for the engine has been developed by using Java script language. …”
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18
Sentiment analysis using negative selection algorithm for Twitter’s messages / Nazirah Che Alhadi
Published 2012“…In order to develop this model classification and prototype, 480 Twitter’s messages were used as training data and 120 Twitter’s messages for testing data to determine the accuracy of the classification model. …”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Published 2020“…Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. …”
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Algorithmic approaches in model selection of the air passengers flows data
Published 2015“…Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts.…”
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