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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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Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system by Bundak, Caceja Elyca, Abd Rahman, Mohd Amiruddin, Abdul Karim, Muhammad Khalis, Osman, Nurul Huda

“…However, the problem of using the KNN algorithm is the fixed K value does not guarantee accurate estimation at every position. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

“…Moreover, the balance between the exploration and exploitation processes in the DPSO framework is considered using a combination of (i) a kernel density estimation technique associated with new bandwidth estimation method and (ii) estimated multi-dimensional gravitational learning coefficients. …”
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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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Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation by Alia, Osama Moh’d Radi

“…Second, a new dynamic HS-based fuzzy clustering algorithm (DCHS) is proposed to automatically estimate the appropriate number of clusters as well as a good fuzzy partitioning of the given dataset. …”
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Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohamad Fadhil, Abas, Bayuaji, Luhur

“…The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. …”
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Datasets Size: Effect on Clustering Results by Raheem, Ajiboye Adeleke, Ruzaini, Abdullah Arshah, Hongwu, Qin

“…In this paper, we proposed a research technique that implements descriptive algorithms on numeric datasets of varied sizes. We modeled each subset of our data using EM clustering algorithm; two different numbers of partitions (k) were estimated and used for each experiment. …”
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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 first research objective is to develop a new deep learning algorithm by a hybrid of DNN and K-Means Clustering algorithms for estimating the Lorenz chaotic system. …”
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Segmentation of MRI brain images using statistical approaches by Balafar, Mohammad Ali

“…The Gaussian Mixture Model (GMM) is a clustering algorithm that is commonly used for brain MRI segmentation. …”
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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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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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Semiparametric binary model for clustered survival data by Arlin, Rifina, Ibrahim, Noor Akma, Arasan, Jayanthi, Abu Bakar, Mohd Rizam

“…A backfitting algorithm is used in the derivation of the estimating equation for the parametric and nonparametric components of a semiparametric binary covariate model. …”
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Efficient tag grouping RFID anti-collision algorithm for internet of things applications based on improved k-means clustering by Umelo, Nnamdi Henry, Noordin, Nor Kamariah, A. Rasid, Mohd Fadlee, Tan, Kim Geok, Hashim, Fazirulhisyam

“…In the initialization stage, the reader uses improved K-means clustering running concurrently with a tag counter algorithm to cluster tags into K groups using tags RN16 while the counter returns an accurate tag number estimate. …”
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An investigation of structural breaks on spot and futures crude palm oil returns by Zainudin, Rozaimah, Shaharudin, Roselee Shah

“…Using the Inclan and Tioa Iterated Cumulative Sums of Squares (ICSS) algorithm procedures, we proceed to identify any structural changes in series variance. …”
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Expectation maximization clustering algorithm for user modeling in web usage mining system by Mustapha, Norwati, Jalali, Manijeh, Jalali, Mehrdad

“…The model is based on expectation-maximization (EM) algorithm and it is used for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. …”
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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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Kernel and multi-class classifiers for multi-floor wlan localisation by Abd Rahman, Mohd Amiruddin

“…The multi-class classification strategy is used to ensure quick estimation of the multi-class NN algorithms. …”
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Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering by Davari, Atefeh, Marhaban, Mohammad Hamiruce, Mohd Noor, Samsul Bahari, Karimadini, Mohammad, Karimoddini, Ali

“…This is achieved by a pre-estimation using fuzzy clustering that provides a prior knowledge and forms a rough model to be fine tuned using the least square method. …”
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Detection of multiple outliners in linear regression using nonparametric methods by Adnan, Robiah

“…New Algorithms for Computing the Least Trimmed Squares Regression Estimator. …”
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The multiple outliers detection for circular univariate data using different agglomerative clustering algorithms by Nur Syahirah, Zulkipli, Siti Zanariah, Satari, Wan Nur Syahidah, Wan Yusoff

“…Three circular similarity measures are used to obtain the dendrogram from three agglomerative clustering algorithms. …”
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