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Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm
Published 2022“…The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. …”
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Adapted arithmetic optimization algorithm for multi-level thresholding image segmentation: a case study of chest x-ray images
Published 2023“…The best thresholding values are found using various techniques, including Otsu and Kapur-based techniques. These techniques work well for bi-level thresholding, but when used to find the appropriate thresholds for multi-level thresholding, there will be issues with long calculation times, high computational costs, and the need for accuracy improvements. …”
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An image processing and genetic algorithm-based approach for the detection of melanoma in patients
Published 2018“…In this paper, we present a twophase technique to classify images of lesions into benign or malignant. The first phase consists of an image processing-based method that extracts the Asymmetry, Border Irregularity, Color Variation and Diameter of a given mole. …”
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Application of Red Deer Algorithm in Optimizing Complex functions
Published 2021“…The Red Deer algorithm (RDA), a recently developed population-based meta-heuristic algorithm, is examined in this paper with the optimization task of complex functions. …”
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Nonlinear analysis of shell structures using image processing and machine learning
Published 2023“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images
Published 2024“…<h3>Purpose</h3><p dir="ltr">To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions.…”
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A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net
Published 2023“…Although, there are dedicated audio processing DNNs, yet, many recent models of AE have utilized U-Net: a DNN based on Convolutional Neural Network (CNN), fundamentally developed for image segmentation. …”
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Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
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AI-Aided Robotic Wide-Range Water Quality Monitoring System
Published 2024Subjects: Get full text
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Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review
Published 2022“…It also summarizes the problems solved in image processing by DE and its variants.…”
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Animal migration optimization algorithm: novel optimizer, analysis, and applications
Published 2024“…Optimization algorithms are applied to find the optimal solutions in many domains and fields such as image processing, machine learning, and others. …”
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Unsupervised histogram based color image segmentation
Published 2003“…Histograms are then computed, smoothed and downsampled. A peak picking algorithm finds the predominant peaks in the histograms. …”
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Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images
Published 2022“…The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. …”
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”