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A detailed description on unsupervised heterogeneous anomaly based intrusion detection framework
Published 2019“…Ultimately, the framework is able to detect a broad range of intrusions exist in the logs without using either the attack knowledge or the traffic behavioural models. …”
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Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…Most of the existing algorithms consider the noise filtering step precedes trajectory segmentation step which mean that there is no preprocessing method to partition trajectory into set of segments and remove noise points in real time with low computational cost. …”
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2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images
Published 2025“…This study introduces a novel method based on Two-tier Semantic Segmentation (2TSS) framework explicitly aimed at enhancing hotspot detection in thermal images of PV modules. …”
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4
Image segmentation method for boundary detection of breast thermography using random walkers
Published 2013“…Use of interactive segmentation can further enhance these results dramatically as all the standard images that were not segmented correctly by the automatically method were correctly segmented after the utilization of the interactive mode.…”
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5
Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The segmentation iris is transformed to rectangular shape using the Rubber Sheet method. …”
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6
Enhanced Deep Learning Framework for Fine-Grained Segmentation of Fashion and Apparel
Published 2022“…Consequently, the feature now includes both high-level and low-level image semantic feature information, boosting our overall segmentation frameworkâ��s performance. We use the Polyvore and DeepFashion2 databases for testing our algorithm, since these are the standard datasets used by the current methods for running the simulations. …”
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7
Fast shot boundary detection based on separable moments and support vector machine
Published 2021“…Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. …”
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8
Moving objects detection from UAV captured videos using trajectories of matched regional adjacency graphs
Published 2017“…Furthermore, both are computed with respect to the ground-truth data which are manually annotated for the video sequences. The proposed framework has also been compared with existing stateof- the-art detection algorithms. …”
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9
Detection and Severity Identification of control valve stiction in industrial loops using integrated partially retrained CNN-PCA frameworks
Published 2021“…Recent neural network based stiction detection methods published are only able to perform either stiction detection or quantification, which open up an area of research to propose a simplified algorithm to simultaneously detect and quantify stiction with high generalization capability. …”
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10
Local-based stereo matching algorithm using multi-cost pyramid fusion, hybrid random aggregation and hierarchical cluster-edge refinement
Published 2023“…Therefore, the proposed framework is proven to be competitive with other established methods and can be used as a complete algorithm.…”
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11
Deep learning-based item classification for retail automation
Published 2025“…Real-time processing was achieved through the integration of object detection algorithms like YOLO and image segmentation techniques. …”
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Development of brain tumor segmentation of magnetic resonance imaging (MRI) using u-net deep learning
Published 2023“…The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. …”
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Optimising acoustic features for source mobile device identification using spectral analysis techniques / Mehdi Jahanirad
Published 2016“…To achieve this aim, this study proposed a novel framework which extracts the mobile device intrinsic fingerprints from near-silent segments by using two spectral analysis approaches: (a) for linearized modeling, the proposed framework uses the cepstrum estimation technique and extracts entropy of Mel-frequency cepstral coefficients (MFCCs), (b) for non-linear modeling, the framework employs higher-order spectral analysis (HOSA) and utilizes the Zernike moments (ZMs) of the bicoherence magnitude and phase spectrum. …”
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14
Computed tomography and echocardiography image fusion technique for cardiac images
Published 2016“…It will present an accurate and robust segmentation technique, which its results are going to use as input for fusion system in the following. …”
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15
Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images
Published 2025“…The entire diagnostic pipeline is integrated into a MATLAB-based graphical user interface (GUI), facilitating real-time analysis and ease of use in clinical settings. Experimental results show that the proposed FCMAC method achieves a dice coefficient (DC) of 0.654, outperforming conventional segmentation techniques. …”
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A hybrid method for endocardial contour extraction of right ventricle in 4-slices from 3D echocardiography dataset
Published 2014“…Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. …”
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A study on component-based technology for development of complex bioinformatics software
Published 2004“…The second layer uses discriminative SVM algorithm with a state-of-the-art string kernel based on PSI-BLAST profiles that is used to leverage the unlabeled data. …”
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Development of river water level estimation from surveillance cameras for flood monitoring system using deep learning techniques
Published 2022“…The conventional segmentation methods used in this work are thresholding, region growing, and hybrid technique known as GeoRegion. …”
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20
Advances in remote sensing technology, machine learning and deep learning for marine oil spill detection, prediction and vulnerability assessment
Published 2020“…The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. …”
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