Sensor-Based Fuzzy Inference of COVID-19 Transmission Risk in Cruise Ships.

Cruise ships are densely populated ecosystems where infectious diseases can spread rapidly. Hence, early detection of infected individuals and risk assessment (RA) of the disease transmissibility are critical. Recent studies have investigated the long-term assessment of transmission risk on cruise ships; however, short-term approaches are limited by data unavailability.

E pluribus unum interpretable convolutional neural networks.

The adoption of convolutional neural network (CNN) models in high-stake domains is hindered by their inability to meet society's demand for transparency in decision-making. So far, a growing number of methodologies have emerged for developing CNN models that are interpretable by design. However, such models are not capable of providing interpretations in accordance with human perception, while maintaining competent performance.

Interpretable EEG-Based Emotion Recognition Using Fuzzy Cognitive Maps.

The brain is one of the most complex parts of the human body, consisting of billions of neurons and it is involved in almost all vital functions. To study the brain functionality, Electroencephalography (EEG) is used to record the electrical activity generated by the brain through electrodes placed on the scalp surface. In this paper, an auto-constructed Fuzzy Cognitive Map (FCM) model is used for interpretable emotion recognition, based on EEG signals.

Key research questions for implementation of artificial intelligence in capsule endoscopy.

Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents.

Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice.

Artificial Intelligence in Cardiology-A Narrative Review of Current Status.

Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms.