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. …”

Nondestructive evaluation of hybrid concrete properties using image processing and machine learning by Vagelis Plevris (14158863)

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A Machine Learning Approach on Chest X-Rays for Pediatric Pneumonia Detection by Barakat, Natali Imad

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

“…These reconstructed images are then fed as input features to the Machine Learning Systems to ultimately estimate the age of the apples. …”

Image and audio caps: automated captioning of background sounds and images using deep learning by M. Poongodi (14158869)

Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study by Tahir Abbas Khan (21632639)

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Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review by Fahad Muflih Alshagathrh (18427950)

Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization by Tawsifur Rahman (14150523)

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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. …”

Advancing paleontology: a survey on deep learning methodologies in fossil image analysis by Mohammed Yaqoob (344668)

DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins by Samir Brahim, Belhaouari

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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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Detection of central serous retinopathy using deep learning through retinal images by Syed Ale Hassan (17785655)

Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection by Sulaiman Khan (12585349)

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Self-Supervised Learning Powered by Synthetic Data From Diffusion Models: Application to X-Ray Images by Abdullah Hosseini (22466602)

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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)

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Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images by Muhammad Salman Khan (17632266)

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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DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only by Mohammad Tariqul Islam (7854059)

“…Comparing the performance of DiaNet against the existing clinical data-based machine learning models, we conclude that the retinal images contain sufficient information to distinguish the Qatari diabetes cohort from the control group. …”

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review by Normaisharah Mamat (19517623)

“…Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. …”