Exploring Semi-Supervised Learning Algorithms for Camera Trap Images by Sajun, Ali Reza

“…A Master of Science thesis in Computer Engineering by Ali Reza Sajun entitled, “Exploring Semi-Supervised Learning Algorithms for Camera Trap Images”, submitted in August 2022. …”
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Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

“…The proposed method in this paper is unique in that it converts the mechanical behaviour of shell structures into images that are then used to train a machine learning algorithm. …”

Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification by Rajendra Babu Chikkala (22330876)

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Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images by Ahila A (18394806)

“…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. …”

A machine learning model for early detection of diabetic foot using thermogram images by Amith Khandakar (14151981)

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DoFP-ML: A Machine Learning Approach to Food Quality Monitoring Using a DoFP Polarization Image Sensor by Maen Takruri (10873368)

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On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

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A Fusion-Based Approach for Skin Cancer Detection Combining Clinical Images, Dermoscopic Images, and Metadata by Siddiqui, Ansah Juned

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Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach by Arshad Ali Khan (23152516)

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A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net by Sania Gul (18272227)

Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models by Theng, Lau Wei

“…There are many techniques that can be used for fruit classification using computer vision technology. Deep learning is the most promising algorithm compared to another Machine Learning (ML) algorithm. …”
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Competitive learning/reflected residual vector quantization for coding angiogram images by Mourn, W.A.H.

“…It was found that it has a higher probability to diverge when used with nonGaussian and nonLaplacian image sources such as angiogram images. By employing competitive learning neural network in the codebook design process, we tried to obtain a stable and convergent algorithm. …”
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A combined algorithm based on finite elements and a modified methodof characteristics for the analysis of the corona in wire-ductelectrostatic precipitators by Al-Hamouz, Z.M.

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Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions by Khalil Khan (9333883)

“…Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applications in several human-face image analysis tasks. Although many algorithms have been developed to address the problem, face segmentation is still a challenge not being completely solved, particularly for images taken in wild, unconstrained conditions. …”

A reduced model for phase-change problems with radiation using simplified PN approximations by Belhamadia, Youssef

“…The integro-differential equation for the full radiative transfer is replaced by a set of differential equations which are independent of the angle variable and easy to solve using conventional computational methods. To solve the coupled equations, we implement a second-order implicit scheme for the time integration and a mixed finite element method for the space discretization. …”
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Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices by Khawam, Yahya Bader

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Image-CNN Based Process Control of Profile by Zeinab, Zeinab Jihad

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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth, Kunhoth

“…This work proposes various machine learning methods, including transfer learning via fine-tuning, transfer learning via feature extraction, ensembles of deep convolutional neural network (CNN) models, and fusion of CNN features, to develop a preliminary dysgraphia diagnosis system based on handwritten images. …”
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Brain Source Localization in the Presence of Leadfield Perturbations by Momin, Rabiya Nakhat

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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth Kunhoth (14158908)

“…This work proposes various machine learning methods, including transfer learning via fine-tuning, transfer learning via feature extraction, ensembles of deep convolutional neural network (CNN) models, and fusion of CNN features, to develop a preliminary dysgraphia diagnosis system based on handwritten images. …”