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Pelvic classification based on deep learning algorithm on clinical CT scans in Malaysian population
Published 2023“…For age classification, each feature has been divided into two main groups which are age above 20 years old and age below 20 years old. The Phenice sex estimation method provides 98% of mean precision while 88.3% and 95% for mean sensitivity and mean specificity respectively. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…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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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…The suggested approaches are called new approach to min-max (NAMM) and decimal scaling (NADS). The Hybrid mean algorithms which are based on spherical clusters is also proposed to remedy the most significant limitation of the K-Means and K-Midranges algorithms. …”
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Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
Published 2024“…This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. …”
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The classification of wink-based eeg signals by means of transfer learning models
Published 2021“…Whilst it was observed that the optimized k-NN model based on the aforesaid pipeline could achieve a classification accuracy of 100% for the training, validation, and tes t data. …”
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Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…In addressing this problem, some robust procedures for high dimensional dataset via the RFCH algorithm are developed. A modified reweighted fast consistent and high breakdown (MRFCH) estimator in high dimensional data based on the diagonal elements of the scatter matrix instead of its entire elements in the computation of robust Mahalanobis distance within the RFCH algorithm is developed. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning
Published 2016“…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
Published 2011“…Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
Published 2011“…Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. …”
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Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines
Published 2020“…The BayesNet provides the best integrated MLADR fault classifier model better at a 5 % significance level than other deployed algorithms in the intelligent supervised learning model realization. …”
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Application of image processing and adaptive neuro-fuzzy system for estimation of the metallurgical parameters of a flotation process
Published 2016“…In this research study, the fuzzy c-mean clustering technique is utilized to classify the froth images (collected under different process conditions) based on the extracted visual characteristics. …”
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Intelligent detection of DTMF tones using a hybrid signal processing technique with support vector machines
Published 2023“…The Goertzel's Algorithm is used to estimate the seven fundamental DTMF carrier frequencies. …”
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Automated underwater vision system for detection and classification of marine life using CNN YOLO-based model / Mohamed Syazwan Asyraf Rosli
Published 2022“…The Adaptive Moment Estimation (Adam) optimizer and Learning Rate on Plateau are employed to optimize the model's training regime. …”
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Model selection approaches of water quality index data
Published 2016“…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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LASSO-type estimations for threshold autoregressive and heteroscedastic time series models.
Published 2020“…Furthermore, our CGD algorithms are also capable of estimating the pure GARCH model, unlike any similar algorithm for the same model in the literature. …”
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Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
Published 2023Conference Paper -
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A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model
Published 2023“…Algorithms; Diodes; Errors; Iterative methods; Least squares approximations; Mean square error; Optimization; Parameter estimation; Parameter extraction; Photovoltaic cells; Coefficient of determination; Differential Evolution; Electromagnetism-like algorithm; Hybrid evolutionary algorithm; Photovoltaic; Photovoltaic modules; Root mean square errors; Single-diode models; Evolutionary algorithms; algorithm; comparative study; electromagnetic method; estimation method; experimental design; numerical method; parameterization; performance assessment; photovoltaic system…”
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…The Ordinary Least Squares (OLS) method is often used to estimate the parameters of a linear model. Under certain assumptions, the OLS estimates are the best linear unbiased estimates. …”
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