Results 1 to 10 of about 157,570 (268)

Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making

Mathematics, 2023
Employee attrition and high turnover have become critical challenges faced by various sectors in today’s competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict ...
Gabriel Marín Díaz, José Javier Galán Hernández, José Luis Galdón Salvador   +2 more
doaj   +1 more source

Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions

Entropy, 2021
The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions.
Domjan Barić, Petar Fumić, Davor Horvatić, Tomislav Lipic   +3 more
doaj   +1 more source

Prediction or interpretability?

Emerging Themes in Epidemiology, 2019
The journal published a review of the literature on recursive partition in epidemiological research comparing two decision tree methods: classification and regression trees (CARTs) and conditional inference trees (CITs).
Stefano Nembrini
doaj   +1 more source

Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases

Patterns, 2021
Summary: Free-text clinical notes in electronic health records are more difficult for data mining while the structured diagnostic codes can be missing or erroneous. To improve the quality of diagnostic codes, this work extracts diagnostic codes from free-
Xianghao Zhan, Marie Humbert-Droz, Pritam Mukherjee, Olivier Gevaert   +3 more
doaj   +1 more source

A New Interpretable Unsupervised Anomaly Detection Method Based on Residual Explanation

IEEE Access, 2022
Despite the superior performance in modeling complex patterns to address challenging problems, the black-box nature of Deep Learning (DL) methods impose limitations to their application in real-world critical domains.
David F. N. Oliveira, Lucio F. Vismari, Alexandre M. Nascimento, Jorge R. de Almeida, Paulo S. Cugnasca, Joao B. Camargo, Leandro Almeida, Rafael Gripp, Marcelo Neves   +8 more
doaj   +1 more source

Double Prior Network for Multidegradation Remote Sensing Image Super-Resolution

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Image super-resolution (SR) is widely used in remote sensing because it can effectively increase image details. Neural networks have shown remarkable performance in recent years, benefitting from their end-to-end training.
Mengyang Shi, Yesheng Gao, Lin Chen, Xingzhao Liu   +3 more
doaj   +1 more source

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Frontiers in Artificial Intelligence, 2022
The influence of Artificial Intelligence is growing, as is the need to make it as explainable as possible. Explainability is one of the main obstacles that AI faces today on the way to more practical implementation.
Jurgita Černevičienė, Audrius Kabašinskas   +1 more
doaj   +1 more source

Emulating quantum dynamics with neural networks via knowledge distillation

Frontiers in Materials, 2023
We introduce an efficient training framework for constructing machine learning-based emulators and demonstrate its capability by training an artificial neural network to predict the time evolution of quantum wave packets propagating through a potential ...
Yu Yao, Chao Cao, Stephan Haas, Mahak Agarwal, Divyam Khanna, Marcin Abram, Marcin Abram   +6 more
doaj   +1 more source

Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability

Frontiers in Oncology, 2022
BackgroundMicrosatellite instability (MSI) is associated with several tumor types and has become increasingly vital in guiding patient treatment decisions; however, reasonably distinguishing MSI from its counterpart is challenging in clinical practice ...
Jin Zhu, Wangwei Wu, Yuting Zhang, Shiyun Lin, Yukang Jiang, Ruixian Liu, Heping Zhang, Xueqin Wang   +7 more
doaj   +1 more source

Modeling Local Demand for Mobile Spectrum: An Interpretable Machine Learning Approach

IEEE Open Journal of the Communications Society
With the expansion of 5G networks and the ongoing development of future 6G networks, the demand for mobile spectrum is expected to continue to grow, particularly at a local level.
Janaki Parekh, Elizabeth Yackoboski, Amir Ghasemi, Halim Yanikomeroglu   +3 more
doaj   +1 more source