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Disaster related social media content processing for sustainable cities
(
Elsevier
, 2021 , Article)
The current study offers a hybrid convolutional neural networks (CNN) model that filters relevant posts and categorises them into several humanitarian classifications using both character and word embedding of textual ...
An active learning method for diabetic retinopathy classification with uncertainty quantification
(
Springer Science and Business Media Deutschland GmbH
, 2022 , Article)
In recent years, deep learning (DL) techniques have provided state-of-the-art performance in medical imaging. However, good quality (annotated) medical data is in general hard to find due to the usually high cost of medical ...
Advance Warning Methodologies for COVID-19 Using Chest X-Ray Images
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 ...
Deep Learning-Based Conjunctival Melanoma Detection Using Ocular Surface Images
(
springer link
, 2023 , Article)
The human eye could be affected with conjunctival melanoma, which indicates a fatal malignant growth of the eye. Being a very rare disease, there exists a lack of related data in the literature. Also, very few studies ...
RamanNet: a generalized neural network architecture for Raman spectrum analysis
(
Springer Science and Business Media Deutschland GmbH
, 2023 , Article)
Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kinds of materials. This sort of molecule fingerprinting has thus led to the widespread application of ...
BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data
(
Springer Science and Business Media Deutschland GmbH
, 2023 , Article)
Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge ...