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Found 15 papers, 1 papers with code
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey
This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery.
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
The results of this work have significant implications for patient treatment and for ongoing investigations into the early detection of Parkinson's disease.
A Brief Review of Explainable Artificial Intelligence in Healthcare
This paper presents a systematic review of XAI aspects and challenges in the healthcare domain.
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets
The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.
Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models for achieving satisfactory results.
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients
To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images.
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data.
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science.
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented.
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed.
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging.
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation.
