Search Results - (( data identification using algorithm ) OR ( variable extractions using algorithm ))
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1
Development of an effective clustering algorithm for older fallers
Published 2022“…Using feature extraction with the t-SNE and the K-means clustering algorithm, subjects were clustered into low, intermediate A, intermediate B and high fall risk groups which corresponded with fall occurrence of 13%, 19%, 21% and 31% respectively. …”
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2
Identification of Electrooculography Signals Frequency Energy Distribution Using Wavelet Algorithm
Published 2011“…Approach: We describe the identification of Electro-Oculograph (EOG) signals of eye movement potentials by using wavelet algorithm which gives a lot of information than FFT. …”
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3
CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…The correlation analysis is used for the identification and selection of the most influential input variable vector (IVV). …”
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4
Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…An essential component of assessing lithium-ion battery (LIB) performance, reliability, and administration in the application of battery health monitoring and management is determining the battery's Remaining Useful Life (RUL). However, existing RUL prediction approaches have difficulties with variability and nonlinearity that occur during battery degradation, data extraction, feature extraction, hyperparameters optimization, and prediction model uncertainty. …”
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Analysis of Traffic Accident Patterns Using Association Rule Mining
Published 2024“…This study analyzed the levels of minor, moderate, and severe traffic accidents in the Palembang Police area from 2015 to 2020 using association rule mining and the apriori algorithm. …”
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Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi
Published 2022“…Besides, Artificial Neural Network (ANN) and Random Forest (RF) algorithm was used to predicted the AGB using different combination of variables. …”
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Oil palm female inflorescences anthesis stages identification using selected emissivities through thermal imaging and Machine Learning
Published 2022“…This research studies different Machine Learning (ML) classification and ensemble techniques for the assessment of the four pollination stages consist of pre-anthesis I, pre-anthesis II, pre-anthesis III, and anthesis using thermal imaging. Different ML algorithms such as Random Forest (RF), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) as well as an ensemble method are used on data extracted from thermal images collected during infield oil palms pollination stages monitoring. …”
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Multi-sensor fusion and deep learning framework for automatic human activity detection and health monitoring using motion sensor data / Henry Friday Nweke
Published 2019“…This is further worsen by the use of single sensors modality and machine learning algorithms. …”
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9
Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease
Published 2021“…The sampling-based method, also known as the data level method, is used to deal with this problem. …”
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10
Date store management in sliding window based on-line identification algorithms
Published 2001“…Sliding window identification algorithms use information from a store of data to optimise on-line learning. …”
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11
Hybrid DE-PEM algorithm for identification of UAV helicopter
Published 2014“…Design/methodology/approach – In this study, flight data were collected and analyzed; MATLAB-based system identification algorithm was developed using DE and PEM; parameterized state-space model parameters were estimated using the developed algorithm and model dynamic analysis. …”
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System identification using Extended Kalman Filter
Published 2017“…Besides, Extended Kalman Filter (EKF) algorithm was selected in this project as an algorithm for offline estimation data purposes. …”
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13
Classification for large number of variables with two imbalanced groups
Published 2020“…This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. …”
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Data mining based damage identification using imperialist competitive algorithm and artificial neural network
Published 2018“…In this study, to predict the damage severity of sin-gle-point damage scenarios of I-beam structures a data mining based damage identification framework and a hybrid algorithm combining Artificial Neural Network (ANN) and Imperial Competitive Algorithm (ICA), called ICA-ANN method, is proposed. …”
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Missing tags detection algorithm for radio frequency identification (RFID) data stream
Published 2019“…AC complement algorithm was used to compare the different in each set of data. …”
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17
Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification
Published 2011“…A deterministic mutation-based algorithm is introduced to overcome this problem. Identification studies using NARX (Nonlinear AutoRegressive with eXogenous input) models employing simulated systems and real plant data are used to demonstrate that the algorithm is able to detect significant variables and terms faster and to select a simpler model structure than other well-known EC methods.…”
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False path identification algorithm framework for nonseparable controller-data path circuits
Published 2016“…This paper proposes an algorithm frame-work to deal with these false paths through identification for DFT test. …”
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Algorithm for rapid identification of flavonoids classes
Published 2017“…The study concluded that FTIR coupled with PCA analysis can be used as a molecular fingerprint for rapid identification of flavonoids.…”
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Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…Several simulated systems and real-world timedependent data are used in the investigation. Comparisons based on widely used optimization performance indicators along with outcomes from other research are used. …”
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