Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers by Ehtesham Hashmi, Sule Yildirim Yayilgan, Muhammad Mudassar Yamin, Mohib Ullah

“…Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. …”
Get full text

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning by Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Michael Kalloniatis, Erik Meijering

“…To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability. …”
Get full text

An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network by R. Karthik, Armaano Ajay, Anshika Jhalani, Kruthik Ballari, Suganthi K

“…The proposed work also incorporates Grad-CAM-based Explainable Artificial Intelligence (XAI) to visualize and interpret the decision making of the network. …”
Get full text

An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients by Krishnaraj Chadaga, Varada Khanna, Srikanth Prabhu, Niranjana Sampathila, Rajagopala Chadaga, Shashikiran Umakanth, Devadas Bhat, K. S. Swathi, Radhika Kamath

“…The Hybrid Bat Algorithm technique performed the best among the above algorithms, with an accuracy of 94% for the customized APPSTACK model. Five explainable artificial intelligence techniques have been tested to interpret the results made by the classifiers. …”
Get full text

Explanatory LSTM-AE-Based Anomaly Detection for Time Series Data in Marine Transportation by Zhan Wang, Mwamba Kasongo Dahouda, Hyoseong Hwang, Inwhee Joe

“…To enhance the interpretability of the results, explainable artificial intelligence (XAI) techniques are incorporated, specifically shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), to identify which features have the most impact on detected anomalies. …”
Get full text

Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells by Nida Aslam, Irfan Ullah Khan, Aisha Alansari, Marah Alrammah, Atheer Alghwairy, Rahaf Alqahtani, Razan Alqahtani, Maryam Almushikes, Mohammed AL Hashim

“…Besides, the study employed Explainable Artificial Intelligence (XAI) to enable surveillance engineers to interpret black box models to understand the causes of abnormalities. …”
Get full text

A deep learning analysis for dual healthcare system users and risk of opioid use disorder by Ying Yin, Elizabeth Workman, Phillip Ma, Yan Cheng, Yijun Shao, Joseph L. Goulet, Friedhelm Sandbrink, Cynthia Brandt, Christopher Spevak, Jacob T. Kean, William Becker, Alexander Libin, Nawar Shara, Helen M. Sheriff, Jorie Butler, Rajeev M. Agrawal, Joel Kupersmith, Qing Zeng-Trietler

“…We conducted a retrospective study of 856,299 patient instances from the Washington DC and Baltimore VA Medical Centers (2012–2019), using a deep neural network (DNN) and explainable Artificial Intelligence to examine the impact of dual-system use on OUD and how demographic and clinical factors interact with it. …”
Get full text

Explainable AI-Enhanced Human Activity Recognition for Human–Robot Collaboration in Agriculture by Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis C. Tagarakis, Dimitrios Kateris, Patrizia Busato, Dionysis Bochtis

“…To fill this gap, this study integrates explainable artificial intelligence, specifically SHapley Additive exPlanations (SHAP), thus enhancing the interpretability of the model. …”
Get full text

ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia by Abhiram Thiriveedhi, Swetha Ghanta, Sujit Biswas, Ashok K. Pradhan

“…This article presents a new model, ALL-Net, for the detection of acute lymphoblastic leukemia (ALL) using a custom convolutional neural network (CNN) architecture and explainable Artificial Intelligence (XAI). A dataset consisting of 3,256 peripheral blood smear (PBS) images belonging to four classes—benign (hematogones), and the other three Early B, Pre-B, and Pro-B, which are subtypes of ALL, are utilized for training and evaluation. …”
Get full text

Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation by Zahra Bashir, Manxi Lin, Aasa Feragen, Kamil Mikolaj, Caroline Taksøe-Vester, Anders Nymark Christensen, Morten B. S. Svendsen, Mette Hvilshøj Fabricius, Lisbeth Andreasen, Mads Nielsen, Martin Grønnebæk Tolsgaard

“…Abstract We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. …”
Get full text

A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data by M. Atzeni, G. Cappon, J. K. Quint, F. Kelly, B. Barratt, M. Vettoretti

“…The framework employs (i) k-means clustering to uncover potentially distinct patient sub-types, (ii) supervised ML techniques (Logistic Regression, Random Forest, and eXtreme Gradient Boosting) to train and test predictive models for each patient sub-type and (iii) an explainable artificial intelligence technique (SHAP) to interpret the final models. …”
Get full text

AI-Driven Plant Health Assessment: A Comparative Analysis of Inception V3, ResNet-50 and ViT with SHAP for Accurate Disease Identification in Taro by Valeria Maeda-Gutiérrez, Juan José Oropeza-Valdez, Luis C. Reveles-Gómez, Cristian Padron-Manrique, Osbaldo Resendis-Antonio, Luis Octavio Solís-Sánchez, Hector A. Guerrero-Osuna, Carlos Alberto Olvera Olvera

“…This study addresses the challenges of Taro disease identification by employing two key strategies: integrating explainable artificial intelligence techniques to interpret deep learning models and conducting a comparative analysis of advanced architectures Inception V3, ResNet-50, and Vision Transformers for classifying common Taro diseases, including leaf blight and mosaic virus, as well as identifying healthy leaves. …”
Get full text

End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence by Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi

“…Ultimately, this representation is used within a directed graph convolutional architecture and investigated with explainable artificial intelligence (AI) tools, offering a more detailed understanding of how stroke alters communication within the brain. …”
Get full text

Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals by Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer

“…Moreover, a cortical connectome diagram related to psychotic criminal detection was created using a DLob-based explainable artificial intelligence (XAI) method. <b>Conclusions:</b> In this regard, the proposed ZPat-based XFE model achieved both high classification performance and interpretability. …”
Get full text