Abstract: Convolutional neural networks (CNNs) have been extensively studied for hyperspectral image classification (HSIC). However, CNNs are critically attributed to a large number of labeled ...
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern ...
1 Amazon Web Services, Seattle, USA. 2 Rajiv Gandhi University of Knowledge Technologies, Nuzvid, India. Optical Coherence Tomography (OCT) is a non-invasive imaging modality widely employed for ...
This project investigates the application of Deep Neural Networks (DNNs) for automated fault classification and fault location in power transmission lines. Using data generated from a simulated 4-bus ...
Abstract: The goal of this study is to create and test improved enhanced deep learning approaches for anomaly detection and classification in CT images, specifically lung cancer images. The Improved ...
fDepartment of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany gDepartment of Medicine III, RWTH Aachen University Hospital, Aachen, Germany hDepartment of Pathology, ...
This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual ...
This demo shows how to interpret the classification by CNN using LIME (Local Interpretable Model-agnostic Explanations) [1]. This demo was created based on [1], but the implementation might be a ...
Diet plays an important role in people’s daily life with its strong correlation to health and chronic diseases. Meanwhile, deep based food computing emerges to provide lots of works which including ...