Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

Deep neuro-fuzzy systems (DNFSs) have been successfully applied to real-world problems
using the efficient learning process of deep neural networks (DNNs) and reasoning aptitude …

Diabetes is one of the most common and hazardous diseases, which can affect almost every
organ in the body. Diagnosis of diabetes requires determining all vital parameters related to …

The development of wireless sensors and wearable devices has led health care services to
the new paramount. The extensive use of sensors, nodes, and devices in health care …

Medical data is being generated from a wide variety of sources today, including smart
phones, wearable sensors, patient records, clinical reports, researchers, healthcare …

Postpartum haemorrhage (PPH) is the loss of blood above 500 ml during vaginal or
caesarean deliveries. It is difficult to find a PPH in an earlier stage, so pregnant women are …

A typical time series classification issue that has recently received a lot of attention is eye
state identification. To classify the states of the eyes, electroencephalography (EEG), a …

Abstract Healthcare organizations and Health Monitoring Systems generate large volumes
of complex data, which offer the opportunity for innovative investigations in medical decision …

Chronic Kidney disease is a significant public health concern across the world, with rising
incidence and prevalence, as well as negative effects such as renal failure, cardiovascular …

Abstract The novel Deep Neuro-Fuzzy System (DNFS) has attracted significant attention
from researchers due to the model's adaptability and rule-based structure. The model has …