Federated learning (FL) allows hospitals and medical centers to train models without sharing patient data. Instead of sending data to a central server, each institution trains the model locally and ...

Brain tumors pose a significant health threat, and accurate detection and segmentation are crucial for treatment. This paper proposes a novel deep learning approach integrating U-Net for precise ...

Mask R-CNN is based on Faster R-CNN and achieves precise segmentation and localization of each instance object by adding pixel-level masks (He et al., 2017).

The widespread adoption of fiber-optic distributed acoustic sensing (DAS) technology in oil and gas production, the timely and precise identification of microseismic events within DAS datasets holds ...

We provide a novel method for local climate zone (LCZ) classification utilizing deep learning and big data to merge publicly available SAR and multispectral (MS) data, gathered by the Sentinel-1 and ...

This study proposed depth fused Mask R-CNN (DF-Mask R-CNN), which integrates depth information of the scene with the RGB image to enhance the detection, localization, and segmentation of strawberries ...

This study examines supercapacitors using machine learning. Each model is tested using Random Forest, Support Vector Machine, Regression, and ANN. This article examines data selection and collection ...

To obtain light ensemble model through clearly explained effective ensemble member selection and finding data representation in various valuable forms are major challenges in medical image ...

Properly predicting students'academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students ...

Sophisticated Machine Learning Techniques were used to conduct an exhaustive study of fingerprint images cutoffs. An assessment and comparisons will be done with the available approaches and a new ...