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Found 14 papers, 1 papers with code
Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches
The application of deep learning models in medical diagnosis has showcased considerable efficacy in recent years.
Multi-scale Intervention Planning based on Generative Design
The scarcity of green spaces, in urban environments, consists a critical challenge.
A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures.
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques
Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes.
Transferability limitations for Covid 3D Localization Using SARS-CoV-2 segmentation models in 4D CT images
In this paper, we investigate the transferability limitations when using deep learning models, for semantic segmentation of pneumonia-infected areas in CT images.
Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components.
TraCon: A novel dataset for real-time traffic cones detection using deep learning
In this work, the YOLOv5 algorithm is employed, in order to find a solution for the efficient and fast detection of traffic cones.
Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models
Recent studies indicate that detecting radiographic patterns on CT scans can yield high sensitivity and specificity for Covid-19 localization.
Evaluating the Usefulness of Unsupervised monitoring in Cultural Heritage Monuments
In the context of this paper, we detect the level of decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes utilizing hyperspectral images.
Assessing the Lockdown Effects on Air Quality during COVID-19 Era
In this work we investigate the short-term variations in air quality emissions, attributed to the prevention measures, applied in different cities, to mitigate the COVID-19 spread.
Creating a Baseline for Robust Energy Savings Estimation in Households
In this paper we provide an analytical methodology to handle such scenarios and create year-long hourly-based consumption baseline.
Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation
Corrosion detection on metal constructions is a major challenge in civil engineering for quick, safe and effective inspection.
Semi-Supervised Fine-Tuning for Deep Learning Models in Remote Sensing Applications
A combinatory approach of two well-known fields: deep learning and semi supervised learning is presented, to tackle the land cover identification problem.
Decision making via semi-supervised machine learning techniques
Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training.
