Search Results - (( parameter detection method algorithm ) OR ( software visualisation using algorithm ))
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Effectiveness of algorithm visualisation in studying complex algorithms: a case study using TRAKLA Ravie / Chandren Muniyandi, Ali Maroosi
Published 2015“…TRAKLA2 is a visualisation tool used to enhance the process of learning algorithm construction and optimization. …”
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Bioinformatics analysis and spatiotemporal distribution of the f lic gene and its protein isolated from Escherichia coli-infected patients in Eastern Algeria
Published 2024“…The alignment results were then visualised using the MView programme. Finally, a phylogenetic tree was constructed using the maximum likelihood algorithm in MEGA 11 software. …”
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Bioinformatics analysis and spatiotemporal distribution of the fliC gene and its protein isolated from Escherichia coli-infected patients in eastern Algeria
Published 2024“…The alignment results were then visualised using the MView programme. Finally, a phylogenetic tree was constructed using the maximum likelihood algorithm in MEGA 11 software. …”
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3D terrain visualisation for GIS: A comparison of different techniques
Published 2011“…Geographic Information System (GIS) applications are now moving towards 3D as it has a better representation of the real world.Most GIS applications now can be visualised not only in 2D but also in 3D.Currently most of the applications are capable of operating in an online environment such as Google Earth, Microsoft Virtual Earth and World Wind.Recently, many people use these online applications for their daily work and also for decision making.Terrain visualisation is an important aspect for these three applications, in visualizing the world in 3D.The quality of terrain visualisation depends on the techniques used by the developer.The research for generating good quality terrain visualisation for depicting the real world is ongoing and faces big challenges. …”
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Visualisation System of COVID-19 Data in Malaysia
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Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…The simulation results indicate that the proposed method is suitable to detect a single outlier. As for the multiple outliers, a clustering algorithm is considered and a dendogram to visualise the clustering algorithm is used. …”
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Application of weighted average on modal parameters for damage detection algorithms: Case study on steel beam
Published 2011“…This study verifies the use of the proposed weighting method by Fayyadh and Abdul Razak (2011a) for damage detection algorithms applied on cracked steel beam. …”
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Weighting method for modal parameter based damage detection algorithms
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…This creates the jamming detection and classification parameters. The second stage is detecting jammers by integrating both lower layers by developing Integrated Combined Layer Algorithm (ICLA). …”
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Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
Published 2014“…This study focuses on using GSA method, a new computational intelligence algorithm. …”
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PREDICTION OF HFMD DISEASE OUTBREAK FROM TWITTER
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Final Year Project Report / IMRAD -
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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Improved intrusion detection algorithm based on TLBO and GA algorithms
Published 2021“…The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. …”
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Outlier detection in circular regression model using minimum spanning tree method
Published 2019“…The existence of outliers in a circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using the minimum spanning tree method. …”
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Comparison of UAV flying height parameter for crack detection applications / Wan Nurdayini Batrisyia Wan Ghazali
Published 2024“…The key concern of this thesis is to determine the influence of flying height parameters on crack detection using UAVs. Looking at the current scenario of development and the age of the structures in urban areas, there is a high significance of efficient and accurate building inspection methods. …”
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Fuzzy modelling using butterfly optimization algorithm for phishing detection
Published 2020“…To generate the fuzzy parameter automatically, an optimization method is required and Butterfly Optimization Algorithm (BOA) is one of the good methods to be applied. …”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm
Published 2023“…It provides an increased convergence and globally optimized solutions. The algorithm has been tested using actual customer consumption data from SESB. 10 fold cross validation method is used to confirm the consistency of the detection accuracy. …”
Conference Paper -
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Novel chewing cycle approach for peak detection algorithm of chew count estimation
Published 2025“…The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation
Published 2025“…The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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