Search Results - (( developing mode decomposition algorithm ) OR ( java segmentation using algorithm ))
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Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data
Published 2021“…Therefore, there is the research scope to search for better methods to improve forecast accuracy obtainable from the traditional methods by applying advanced approaches. Empirical mode decomposition (EMD), a very essentially important part of Hilbert-Huang transforms (HHT) is a very adaptive decomposition algorithm to view data from granular and different time scales. …”
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Image clustering comparison of two color segmentation techniques
Published 2010“…Finally, the algorithm found, which would solve the image segmentation problem.…”
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Elastic net penalized Quantile Regression Model and Empirical Mode Decomposition for Improving the Accuracy of the Model Selection
Published 2023“…The EMD-EnetQR method is proposed to address this issue, which consists of applying the empirical mode decomposition (EMD) algorithm to time series data and then using the resulting components in penalized quantile regression models. …”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts
Published 2016“…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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Development of seven segment display recognition using TensorFlow on Raspberry Pi
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Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition
Published 2025“…Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. …”
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Hydraulic characterization of PVC-O pipes by means of transient tests
Published 2019“…This report is proposing and experimenting on the newly found mathematical algorithm for analyzing and processing a nonlinear and non-stationary signal for decomposing and imposing the signal into multiple decomposition of the signal. …”
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Forecasting of fine particulate matter based on LSTM and optimization algorithm
Published 2024Subjects:Article -
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Leveraging CQT-VMD and pre-trained AlexNet architecture for accurate pulmonary disease classification from lung sound signals
Published 2025“…This study presents a novel algorithm for classifying pulmonary diseases using lung sound signals by integrating Variational Mode Decomposition (VMD) and the Constant-Q Transform (CQT) within a pre-trained AlexNet convolutional neural network. …”
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Hybrid OCSSA-VMD and optimized deep learning networks for runoff forecasting
Published 2025“…The Osprey-Cauchy-Sparrow Search Algorithm (OCSSA) is employed to fine-tune the parameters of Variational Mode Decomposition (VMD), which is utilized to break down the original runoff data into multiple Intrinsic Mode Functions (IMFs). …”
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Focal and non-focal epilepsy localization: a review
Published 2018“…Generally, the techniques developed for detecting the epileptic seizures were based on tensors, entropy, empirical mode decomposition, wavelet transform and dynamic analysis. …”
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Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…The proposed method is tested on 10 multi-objective benchmark problems of CEC 2009 and compared with four metaheuristics: Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), Multi-Objective Differential Evolution (MODE) and MOPSO. …”
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Air quality forecasting and mapping in Malaysian urban areas: A hybrid deep learning approach
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Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA
Published 2017“…One such problematic artefact is the ballistocardiogram (BCG) artefact. Methods We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. …”
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Enhanced foreign exchange volatility forecasting using CEEMDAN with optuna-optimized ensemble deep learning model
Published 2024“…Furthermore, the hyperparameters for the DL models are optimized using the Optuna algorithm. Finally, a hybrid ensemble model for forecasting exchange rate volatility is developed by combining the predictions of three distinct DL models. …”
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Information fusion and data augmentation with deep features for a deep learning-based baby cry recognition / Zhang Ke
Published 2024“…The Whale optimization algorithm-Variational mode decomposition is used to optimally decompose the baby cry signals. …”
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