[HTML][HTML] Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)
Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …
the present era of big data. Machine learning and probability theory methods have been …
Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus
Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing
capability of the human body. This results in high blood sugar level which might lead to …
capability of the human body. This results in high blood sugar level which might lead to …
Blockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing
Fog computing is a modern computing model which offers geographically dispersed end-
users with the latency-aware and highly scalable services. It is comparatively safer than …
users with the latency-aware and highly scalable services. It is comparatively safer than …
A comprehensive survey on the detection, classification, and challenges of neurological disorders
Simple Summary This study represents a resourceful review article that can deliver
resources on neurological diseases and their implemented classification algorithms to …
resources on neurological diseases and their implemented classification algorithms to …
Heart disease prediction based on pre-trained deep neural networks combined with principal component analysis
Heart Disease (HD) is often regarded as one of the deadliest human diseases. Therefore,
early prediction of HD risks is crucial for prevention and treatment. Unfortunately, current …
early prediction of HD risks is crucial for prevention and treatment. Unfortunately, current …
An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images
Early detection of Tuberculosis or TB can help in mitigating the chances of affecting the other
body parts like the kidney, spine and brain, thereby reducing the death rate due to this …
body parts like the kidney, spine and brain, thereby reducing the death rate due to this …
Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey
Q Jiang, X Zhou, R Wang, W Ding, Y Chu, S Tang… - Applied Soft …, 2022 - Elsevier
Infectious diseases usually have the characteristics of rapid spread with a large impact
range. Once they break out, they will cause a large area of infection, which creates …
range. Once they break out, they will cause a large area of infection, which creates …
A cnn approach for corn leaves disease detection to support digital agricultural system
Correct, fast and early detection of corn leave diseases and their prevention and control at
the earlier stages is profitable. To improve the detection accuracy of corn leaf diseases, a …
the earlier stages is profitable. To improve the detection accuracy of corn leaf diseases, a …
[HTML][HTML] A Jaya algorithm based wrapper method for optimal feature selection in supervised classification
In recent years, Jaya optimization algorithm has been successfully applied in several
optimization problems. This paper presents a novel feature selection (FS) approach based …
optimization problems. This paper presents a novel feature selection (FS) approach based …