[HTML][HTML] Xair: A systematic metareview of explainable ai (xai) aligned to the software development process
Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in
regard to its practical implementation in various application domains. To combat the lack of …
regard to its practical implementation in various application domains. To combat the lack of …
[HTML][HTML] An empirical survey on explainable ai technologies: Recent trends, use-cases, and categories from technical and application perspectives
M Nagahisarchoghaei, N Nur, L Cummins, N Nur… - Electronics, 2023 - mdpi.com
In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …
Wildfire danger prediction and understanding with deep learning
Climate change exacerbates the occurence of extreme droughts and heatwaves, increasing
the frequency and intensity of large wildfires across the globe. Forecasting wildfire danger …
the frequency and intensity of large wildfires across the globe. Forecasting wildfire danger …
[HTML][HTML] Investigating explainability methods in recurrent neural network architectures for financial time series data
W Freeborough, T van Zyl - Applied Sciences, 2022 - mdpi.com
Statistical methods were traditionally primarily used for time series forecasting. However,
new hybrid methods demonstrate competitive accuracy, leading to increased machine …
new hybrid methods demonstrate competitive accuracy, leading to increased machine …
[HTML][HTML] Transformer for gene expression modeling (T-GEM): an interpretable deep learning model for gene expression-based phenotype predictions
Simple Summary Cancer is the second leading cause of death worldwide. Predicting
phenotype and understanding makers that define the phenotype are important tasks. We …
phenotype and understanding makers that define the phenotype are important tasks. We …
[HTML][HTML] Towards machine learning-aided lung cancer clinical routines: Approaches and open challenges
Advancements in the development of computer-aided decision (CAD) systems for clinical
routines provide unquestionable benefits in connecting human medical expertise with …
routines provide unquestionable benefits in connecting human medical expertise with …
[HTML][HTML] Age estimation from sleep studies using deep learning predicts life expectancy
Sleep disturbances increase with age and are predictors of mortality. Here, we present deep
neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging …
neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging …
Effect of tokenization on transformers for biological sequences
E Dotan, G Jaschek, T Pupko, Y Belinkov - Bioinformatics, 2024 - academic.oup.com
Motivation Deep-learning models are transforming biological research, including many
bioinformatics and comparative genomics algorithms, such as sequence alignments …
bioinformatics and comparative genomics algorithms, such as sequence alignments …
[HTML][HTML] MultiGML: Multimodal graph machine learning for prediction of adverse drug events
Adverse drug events constitute a major challenge for the success of clinical trials. Several
computational strategies have been suggested to estimate the risk of adverse drug events in …
computational strategies have been suggested to estimate the risk of adverse drug events in …
Predicting the survival of patients with cancer from their initial oncology consultation document using natural language processing
Importance Predicting short-and long-term survival of patients with cancer may improve their
care. Prior predictive models either use data with limited availability or predict the outcome …
care. Prior predictive models either use data with limited availability or predict the outcome …