Results 41 to 50 of about 1,818,152 (349)

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset

Scientific Reports, 2022
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications.
Chuizheng Meng, Loc Trinh, Nan Xu, James Enouen, Yan Liu   +4 more
semanticscholar   +1 more source

Semantic Structure and Interpretability of Word Embeddings

, 2018
Dense word embeddings, which encode semantic meanings of words to low dimensional vector spaces have become very popular in natural language processing (NLP) research due to their state-of-the-art performances in many NLP tasks.
Cukur, Tolga, Koc, Aykut, Senel, Lutfi Kerem, Utlu, Ihsan, Yucesoy, Veysel   +4 more
core   +1 more source

Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability [PDF]

, 2020
Understanding the shopping motivations behind market baskets has high commercial value in the grocery retail industry. Analyzing shopping transactions demands techniques that can cope with the volume and dimensionality of grocery transactional data while
Manolopoulou, Ioanna, Musolesi, Mirco, O'sullivan, Jason, Prior, Rosie, Vega-Carrasco, Mariflor   +4 more
core   +1 more source

Interpretable Software Defect Prediction from Project Effort and Static Code Metrics

Computers
Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to ...
Susmita Haldar, Luiz Fernando Capretz
doaj   +1 more source

Tropical Cyclone Intensity Prediction Using Deep Convolutional Neural Network

Atmosphere, 2022
In this study, deep convolutional neural network (CNN) models of stimulated tropical cyclone intensity (TCI), minimum central pressure (MCP), and maximum 2 min mean wind speed at near center (MWS) were constructed based on ocean and atmospheric ...
Xiao-Yan Xu, Min Shao, Pu-Long Chen, Qin-Geng Wang   +3 more
doaj   +1 more source

Decision making, symmetry and structure: Justifying causal interventions

Journal of Causal Inference
We can use structural causal models (SCMs) to help us evaluate the consequences of actions given data. SCMs identify actions with structural interventions. A careful decision maker may wonder whether this identification is justified.
Johnston David O., Ong Cheng Soon, Williamson Robert C.   +2 more
doaj   +1 more source

Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps

Energies
Technological improvements are crucial for achieving decarbonisation targets and addressing the impacts of climate change in the built environment via mitigation and adaptation measures.
Massimiliano Manfren, Karla M. Gonzalez-Carreon, Patrick A. B. James   +2 more
doaj   +1 more source

Explainable Deep Learning Study for Leaf Disease Classification

Agronomy, 2022
Explainable artificial intelligence has been extensively studied recently. However, the research of interpretable methods in the agricultural field has not been systematically studied.
Kaihua Wei, Bojian Chen, Jingcheng Zhang, Shanhui Fan, Kaihua Wu, Guangyu Liu, Dongmei Chen   +6 more
doaj   +1 more source

A Quality Improvement Initiative to Standardize Pneumocystis jirovecii Pneumonia Prophylaxis in Pediatric Patients With Solid Tumors

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Pediatric patients with extracranial solid tumors (ST) receiving chemotherapy are at an increased risk for Pneumocystis jirovecii pneumonia (PJP). However, evidence guiding prophylaxis practices in this population is limited. A PJP‐related fatality at our institution highlighted inconsistent prescribing approaches and concerns about
Kriti Kumar, Denise M. Connolly, Ellen Kellington, Elyse Zelunka, Karla Kerrigan, Furqan Shaikh, Paul C. Nathan, Meredith S. Irwin, Sarah Cohen‐Gogo   +8 more
wiley   +1 more source

Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models

Machine Learning and Knowledge Extraction
This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy.
Mohammad Ennab, Hamid Mcheick
semanticscholar   +1 more source