Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey
W Ding, M Abdel-Basset, H Hawash, AM Ali - Information Sciences, 2022 - Elsevier
The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …
strategy of decision-making in different life domains. Regardless of this achievement, AI …
Comprehensive survey of recent drug discovery using deep learning
Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for develo** novel drugs. With the …
significantly reduces the time and cost required for develo** novel drugs. With the …
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …
statistics are used, and the area under the receiver operating characteristic curve (ROC …
Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation
Optimal performance is desired for decision-making in any field with binary classifiers and
diagnostic tests, however common performance measures lack depth in information. The …
diagnostic tests, however common performance measures lack depth in information. The …
A survey on medical explainable AI (XAI): recent progress, explainability approach, human interaction and scoring system
The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of
utmost importance. Meanwhile, incorporating explanations in the medical domain with …
utmost importance. Meanwhile, incorporating explanations in the medical domain with …
Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods
White blood cells are cells in the blood and lymph tissue produced by the bone marrow in
the human body. White blood cells are an important part of the immune system. The most …
the human body. White blood cells are an important part of the immune system. The most …
Single-cell gene regulatory network prediction by explainable AI
The molecular heterogeneity of cancer cells contributes to the often partial response to
targeted therapies and relapse of disease due to the escape of resistant cell populations …
targeted therapies and relapse of disease due to the escape of resistant cell populations …
BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation
Medical image segmentation enables doctors to observe lesion regions better and make
accurate diagnostic decisions. Single-branch models such as U-Net have achieved great …
accurate diagnostic decisions. Single-branch models such as U-Net have achieved great …
Deep pre-trained networks as a feature extractor with XGBoost to detect tuberculosis from chest X-ray
M Rahman, Y Cao, X Sun, B Li, Y Hao - Computers & Electrical Engineering, 2021 - Elsevier
Pulmonary Tuberculosis is a plague caused by Mycobacterium tuberculosis or Tubercle
bacillus, which kills 1.8 million people worldwide. Tuberculosis is among the top 10 deadly …
bacillus, which kills 1.8 million people worldwide. Tuberculosis is among the top 10 deadly …
A fuzzy convolutional attention-based GRU network for human activity recognition
Human activity recognition has become a pillar of today intelligent Human–Computer
Interfaces as it typically provides more comfortable and ubiquitous interaction. This paper …
Interfaces as it typically provides more comfortable and ubiquitous interaction. This paper …