Search Results - (( using function clustering algorithm ) OR ( parameter simulation based algorithm ))

Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor by Adilah, Abdul Ghapor

“…As for the multiple outliers, a clustering algorithm is considered and a dendogram to visualise the clustering algorithm is used. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari by Satari, Siti Zanariah

“…Here, we introduce a measure of similarity based on the circular distance and obtain a cluster tree using the single linkage clustering algorithm. …”
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Fuzzy clustering method and evaluation based on multi criteria decision making technique by Sameer, Fadhaa Othman

“…This proposed algorithm is developed based on heuristic method named modified binary particle swarm optimization (MBPSO) with kernel fuzzy clustering method as a fitness function. …”
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Cauchy density-based algorithm for VANETs clustering in 3D road environments by Jubair, Mohammed Ahmed, Ahmad, Mohd Riduan, Abdul Aziz, Izzatdin, Al-Obaidi, Ahmed Salih, Al-Tickriti, Abdullah Talaat, Hassan, Mustafa Hamid, Mostafa, Salama A., Mahdin, Hairulnizam

“…This paper attempts to solve the problem of VANETs in 3D road environments using a centralized clustering technique to develop a Cauchy density model. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

“…The fitness function used is the correlation function in the SKF algorithm to optimize the cipher image produced using the Lorenz system. …”
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Enhancement load balancing in failover web server by using HTTP-response status techniques with pfsense opensource / Muhammad Hafiz Ramli by Ramli, Muhammad Hafiz

“…Then, this paper will propose the architecture design and techniques will use during the implementation stage. Moreover, there will be a simulation with the research parameter. …”
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Predicting the popularity of tweets using the theory of point processes. by Tan, Wai Hong

“…We first propose a marked point process model, termed the Marked Self-Exciting Process with Time-Dependent Excitation Function, or the MaSEPTiDE for short. The intensity process of the model is interpretable as a cluster Poisson process, which implies that the model can be simulated using the cascading algorithm similar to that used for the efficient simulation of Hawkes processes, and the prediction can be done properly by exploiting the probabilistic properties of the model. …”
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Green anaconda optimization based energy aware clustering protocol for 6G wireless communication systems by Motwakel, Abdelwahed, Hassan Abdalla Hashim, Aisha, Mengash, Hanan Abdullah, Alruwais, Nuha, Yafoz, Ayman, Alsini, Raed, Edris, Alaa

“…In addition, an objective function is designed to accomplish Quality of Service (QoS) in WSN using three input parameters, namely energy, distance, and delay. …”
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Channel Modeling and Direction-of-Arrival Estimation in Mobile Multiple-Antenna Communication Systems by Ravari, Arastoo Rostami

“…Low-complexity spectral-based estimators are used for the estimation of direction and spatial spread of the distributed source by employing the proposed channel model for simulation. …”
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Determining the preprocessing clustering algorithm in radial basis function neural network by S.L. Ang, H.C. Ong, H.C. Law

“…Three types of method used in this study to find the centres include random selections, K-means clustering algorithm and also K-median clustering algorithm. …”
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Biological-based semi-supervised clustering algorithm to improve gene function prediction by Kasim, Shahreen, Deris, Safaai, M. Othman, Razib, Hashim, Rathiah

“…However, commonclustering algorithms do not provide a comprehensive approach that look into the three categories of annotations; biologicalprocess, molecular function, and cellular component, and were not tested with different functional annotation database formats.Furthermore, the traditional clustering algorithms use random initialization which causes inconsistent cluster generation and areunable to determine the number of clusters involved. …”
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Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques by Googhari, Shahram Karimi

“…The model was trained based on the Leven-berg Marquardt algorithm with sigmoid activation functions. …”
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

“…The K-Means algorithm is the commonest and fast technique in partitional cluster algorithms, although with unnormalized datasets it can achieve local optimal. …”
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Self-Organized Wireless Sensor Network (SOWSN) for dense jungle applications by Hakim, Galang Persada Nurani, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Alghaihab, Abdullah, Yusoff, Siti Hajar, Adesta, Erry Yulian Triblas

“…The first feature is the introduction of Multi Criteria Decision Making (MCDM) algorithm with simple Additive Weight (SAW) function for clustering the SOWSN nodes. …”
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A Proposed Method For Selecting The Clustering Algorithm In Radial Basis Function [QA223. A581 2008 f rb]. by Ang, Sau Loong

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A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds by Salim, Naomie, Shah, J. Z.

“…In this work a fuzzy hierarchical algorithm is developed which provides a mechanism not only to benefit from the fuzzy clustering process but also to get advantage of the multiple membership function of the fuzzy clustering. …”
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A review: accuracy optimization in clustering ensembles using genetic algorithms by Ghaemi, Reza, Sulaiman, Md. Nasir, Ibrahim, Hamidah, Mustapha, Norwati

“…This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research.…”
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Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

“…Moreover, experiments demonstrate that final clustering solution generated by the proposed incremental genetic-based clustering ensemble algorithm using the pattern ensemble learning method possess comparative or better clustering accuracy than clustering solutions generated by the incremental genetic-based clustering ensemble algorithms using other recombination operators. …”
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Clustering for binary data sets by using genetic algorithm-incremental K-means by Saharan, S., Baragona, R., Nor, M. E., Salleh, R. M., Asrah, N. M.

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Indoor positioning using weighted magnetic field signal distance similarity measure and fuzzy based algorithms by Bundak, Caceja Elyca

“…Therefore, for the second objective, another algorithm named the fuzzy algorithm is designed which combines the clustering algorithm, matching algorithm, triangle area algorithm and average Euclidean algorithm used to estimate location. …”
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