Search Results - (( parameter predictions clustering algorithm ) OR ( java implication based algorithm ))

Development of an intelligent prediction tool for rice yield based on machine learning techniques by Md. Sap, Mohd. Noor, Awan, A. M.

“…Whereas kernel-based clustering algorithm is developed for finding clusters in climate data. …”
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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

“…After that, it has been suggested to improve the efficiencies in the Deep Neural Network (DNN) model by combining the DNN with an unsupervised machine learning algorithm, the K-Means clustering algorithm. This study constructs the flow of DNN based method with the K-Means algorithm. …”
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An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis by Tie, K. H., A., Senawi, Chuan, Z. L.

“…However, different clustering algorithms have different parameters that need to be specified. …”
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Hybrid clustering-GWO-NARX neural network technique in predicting stock price by Das, Debashish, Sadiq, Ali Safa, Mirjalili, Seyedali, Noraziah, Ahmad

“…It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. …”
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Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach by Marpaung, Faridawaty, Ramadhani, Fanny, Dinata, Dewan

“…Poverty prediction was conducted using a random forest (RF) algorithm and poverty mapping was conducted using the K-Means algorithm. …”
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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

“…In conclusion, hybrid DNN with the K-Means Clustering Algorithm is proven to resolve parameter estimations of the chaotic system by developing an accurate prediction model.…”
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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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A modified π rough k-means algorithm for web page recommendation system by Zidane, Khaled Ali Othman

“…The ultimate goal is to improve the recommendation quality which leads to increase the prediction accuracy. Hence, this study carried out several objectives to augment the support of modified clustering algorithm. …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

“…In conventional hard clustering approach, the number of clusters was determined by hierarchical clustering and two-step cluster analysis; then the sites were allocated to the appropriate cluster by k-means clustering method. …”
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Adaptive neuro-fuzzy model with fuzzy clustering for nonlinear prediction and control by Al-Himyari, Bayadir Abbas, Yasin, Azman, Gitano, Horizon

“…Nonlinear systems have more complex manner and profoundness than linear systems.Thus, their analyses are much more difficult.This paper presents the use of neuro-fuzzy networks as means of implementing algorithms suitable for nonlinear black-box prediction and control.In engineering applications, two attractive tools have emerged recently.These two attractive tools are: the artificial neural networks and the fuzzy logic system. …”
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Integrating type-2 fuzzy logic system with fuzzy C-means clustering for weather prediction by Soozaei, Ahmad Shahi

“…The proposed method is based on combination of statistic equation with Fuzzy C-Mean (FCM) clustering and Type-2 fuzzy logic system (Type-2 FLS) with gradient descent algorithm. …”
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach by Mustakim, Nurul Ain

“…The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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Probe Drilling Based Prediction Of Rock Mass Strength, Natm-4, Pahang-Selangor Raw Water Transfer Tunnel, Hulu Langat, Selangor, Malaysia by Hassan, Nurfarhana

“…The information recorded were interpreted using k-means clustering algorithm to predict the ground condition. …”
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A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

“…The Takagi-Sugeno-Kang (TSK) type fuzzy inference system was chosen and constructed by an automatic generation of clusters as well as membership functions and minimal rules through the use of hybrid fuzzy clustering and the modified apriori algorithms respectively. …”
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IMPLEMENTATION OF A TIME SERIES PREDICTION ALGORITHM FOR COVID-19 CONFIRMED CASES IN MALAYSIA : A PRELIMANARY STUDY AFTER THE RECOVERY MOVEMENT CONTROL ORDER by Tee, Bee Lin

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Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi by Habeebah Adamu , Kakudi

“…The "Cohort study on clustering of lifestyle risk factors and understanding its association with stress on health and well-being among school teachers in Malaysia" (CLUSTer) dataset was used to compare the performance of the proposed Genetically Optimised Bayesian ARTMAP (GOBAM) model and three other classic Adaptive Resonance Theory Mapping (ARTMAP) models –Genetic Algorithm Fuzzy ARTMAP (GAFAM), Fuzzy ARTMAP (FAM), and Bayesian ARTMAP (BAM). …”
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Predicting the popularity of tweets using the theory of point processes. by Tan, Wai Hong

“…The mode of the posterior distribution is used as the estimator of the finite-dimensional parameter, and suitable functionals of the predictive distribution for the number of retweets implied by the estimated model are used to predict the tweet popularity. …”
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Development of Anthro-Fitness Model for evaluating firefighter recruits’ performance readiness using machine learning by Borhanudin, Mohd Yusof @ Mohamed, Rabiu Muazu, Musa, Mohamad Nizam, Nazarudin, Anwar P. P., Abdul Majeed, Raj, Naresh Bhaskar, Mohd Azraai, Mohd Razman

“…A k-means clustering algorithm was utilized to group the performance levels of the firefighters whilst a quadratic discriminant analysis model was employed to predict the grouping of firefighters based on these parameters. …”
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Development of anthro-fitness model for evaluating firefighter recruits' performance readiness using machine learning by Borhanudin, Mohd Yusof @ Mohamed, Rabiu Muazu, Musa, Mohamad Nizam, Nazarudin, Anwar P. P., Abdul Majeed, Raj, Naresh Bhaskar, Mohd Azraai, Mohd Razman

“…A k-means clustering algorithm was utilized to group the performance levels of the firefighters whilst a quadratic discriminant analysis model was employed to predict the grouping of firefighters based on these parameters. …”
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A novel hybrid modelling structure fabricated by using Takagi-Sugeno fuzzy to forecast HVAC systems energy demand in real-time for Basra city by Homod R.Z., Togun H., Abd H.J., Sahari K.S.M.