Search Results - (( motion estimation metric algorithm ) OR ( early identification based algorithm ))*
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Image restoration techniques for removal of blurred images / Ameer Naeemi Zyarah
Published 2017“…In this study, four different techniques are used to remove the motion blur. They are Direct Inverse filter, Wiener filter, Constrained Least Squares filter, and Lucy Richardson algorithm, to restore degraded image (motion blurred image). …”
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The impact of executive function and aerobic exercise recognition in obese children under deep learning
Published 2025“…Initially, a motion recognition model based on STN and Lucas–Kanade optical flow algorithm optimization was constructed. …”
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Restoration of blurred images using geometric and chebichef moments / Ahlad Kumar
Published 2016“…Several methods have been developed in both spatial and frequency domains to deblur Gaussian and motion blurred images by using iterative methods to estimate the blur parameters. …”
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Blind motion image deblurring using canny edge detector with generative adversarial networks / Idriss Moussa Idriss
Published 2021“…Most of the existing algorithms rely on MAP (Maximuma Priori) and VB (Variational Bayesian)which are based on deterministic and stochastic methodologies, respectively, to estimate the blur function. …”
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A vision-based deep learning approach for non-contact vibration measurement using (2+1)D CNN and optical flow
Published 2025“…An optical flow-based preprocessing algorithm synchronized motion features in recorded video inputs with measured vibration labels, improving measurement accuracy. …”
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Methods for identification of the opportunistic gut mycobiome from colorectal adenocarcinoma biopsy tissues
Published 2024“…•Application of machine learning algorithms to the identification of potential mycobiome biomarkers for non-invasive colorectal cancer screening. …”
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Development of MyCGPA for early predicting students’ academic performance
Published 2025“…One of the primary concerns in higher education is the early identification of underperforming students. …”
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Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
Published 2018“…It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. …”
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Deep learning-based colorectal cancer classification using augmented and normalised gut microbiome data / Mwenge Mulenga
Published 2022“…First, to investigate the methods used to address limitations associated with microbiome-based datasets in colorectal cancer identification using deep neural network algorithms. …”
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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
Published 2020“…In addition, the ranked order of the variables based on their importance differed across the ML algorithms. …”
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Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques
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Predictive Modelling of Stroke Occurrence among Patients using Machine Learning
Published 2023“…Early identification of high-risk patients enables timely intervention and implementation of preventive measures, potentially reducing the burden of stroke-related complications. …”
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Development of IoT-based heatstroke early symptoms monitoring system for students in Malaysia
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Comparative study of informative acoustic features for VTOL UAV faulty prediction using machine learning
Published 2025“…The sound emitted by Vertical Take Off and Landing (VTOL) UAVs offers valuable insights into their flight performance, serving as a crucial element for the efficient monitoring of flying conditions and early detection of potential faults. This paper will focus on developing fault detection and identification using audio data of different propeller conditions. …”
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Outlier Detection Technique in Data Mining: A Research Perspective
Published 2005“…In this paper we will explain the first part of our research, which is focused on outlier identification and provide a description of why an identified outlier exceptional, based on Distance-Based outlier detection and Density-Based outlier detection.…”
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