Application of Artificial Intelligence techniques for the detection of Alzheimer's disease using structural MRI images
Alzheimer's disease (AD) is an irreversible, progressive brain disorder that slowly destroys
memory and thinking skills. It is one of the leading types of dementia for persons aged above …
memory and thinking skills. It is one of the leading types of dementia for persons aged above …
Machine learning-based early prediction of sepsis using electronic health records: a systematic review
Background: Sepsis, a life-threatening infection-induced inflammatory condition, has
significant global health impacts. Timely detection is crucial for improving patient outcomes …
significant global health impacts. Timely detection is crucial for improving patient outcomes …
A remaining useful life prognosis of turbofan engine using temporal and spatial feature fusion
C Peng, Y Chen, Q Chen, Z Tang, L Li, W Gui - Sensors, 2021 - mdpi.com
The prognosis of the remaining useful life (RUL) of turbofan engine provides an important
basis for predictive maintenance and remanufacturing, and plays a major role in reducing …
basis for predictive maintenance and remanufacturing, and plays a major role in reducing …
An automated skin melanoma detection system with melanoma-index based on entropy features
Skin melanoma is a potentially life-threatening cancer. Once it has metastasized, it may
cause severe disability and death. Therefore, early diagnosis is important to improve the …
cause severe disability and death. Therefore, early diagnosis is important to improve the …
A stock series prediction model based on variational mode decomposition and dual-channel attention network
Due to the comprehensive impact of external factors (politics, economy, market, etc.) and
internal factors (organizational structure, management ability, innovation capability, etc.) …
internal factors (organizational structure, management ability, innovation capability, etc.) …
A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification
Imbalanced time-series classification (ITSC) is ubiquitous in many real-world applications. In
this study, a novel cost-sensitive deep learning framework, namely ACS-ATCN, is proposed …
this study, a novel cost-sensitive deep learning framework, namely ACS-ATCN, is proposed …
Artificial intelligence for clinical decision support in sepsis
M Wu, X Du, R Gu, J Wei - Frontiers in Medicine, 2021 - frontiersin.org
Sepsis is one of the main causes of death in critically ill patients. Despite the continuous
development of medical technology in recent years, its morbidity and mortality are still high …
development of medical technology in recent years, its morbidity and mortality are still high …
[Retracted] A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients
In today's scenario, sepsis is impacting millions of patients in the intensive care unit due to
the fact that the mortality rate is increased exponentially and has become a major challenge …
the fact that the mortality rate is increased exponentially and has become a major challenge …
Convolutional neural networks and temporal CNNs for COVID-19 forecasting in France
This paper focus on multiple CNN-based (Convolutional Neural Network) models for COVID-
19 forecast developed by our research team during the first French lockdown. In an effort to …
19 forecast developed by our research team during the first French lockdown. In an effort to …
Attention mechanism-based deep learning for heat load prediction in blast furnace ironmaking process
Heat load prediction is essential to discover blast furnace (BF) anomalies in time and take
measures in advance to reduce erosion in the ironmaking process. However, owing to the …
measures in advance to reduce erosion in the ironmaking process. However, owing to the …