Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

“…Methods: This study employs the Light Gradient Boosting Machine (LightGBM), an advanced ML algorithm, in combination with Explainable Artificial Intelligence (XAI) methodology to grade gliomas more effectively. …”
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Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms by Paramasivam Alagumariappan, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj, K. Kamalanand, C. Emmanuel, Sarah Allabun, Manal Othman, Masresha Getahun, Ben Othman Soufiene

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XAI-XGBoost: an innovative explainable intrusion detection approach for securing internet of medical things systems by Yousif Hosain, Muhammet Çakmak

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Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles by Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, Yahia Said, Ali M. Al-Sharafi

“…This study presents the explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection for autonomous vehicles (XAIFTL-AWCDAV) method. …”
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Anchor-Based Explainable and Causal Artificial Intelligence for Enhancing Financial Predictions of Future Earnings by Siriporn Sawangarreerak, Putthiporn Thanathamathee, Pankaewta Lakkanawanit, Nor Shaipah Abdul Wahab

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Advancing CVD Risk Prediction with Transformer Architectures and Statistical Risk Factor Filtering by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

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An Explainable LSTM-Based Intrusion Detection System Optimized by Firefly Algorithm for IoT Networks by Taiwo Blessing Ogunseyi, Gogulakrishan Thiyagarajan

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A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…This study presents a novel, hybrid ensemble learning-based intrusion detection framework that integrates deep learning and traditional ML algorithms with explainable artificial intelligence for real-time cybersecurity applications. …”
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Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach. by Josh L Espinoza, Chris L Dupont, Aubrie O'Rourke, Sinem Beyhan, Pavel Morales, Amy Spoering, Kirsten J Meyer, Agnes P Chan, Yongwook Choi, William C Nierman, Kim Lewis, Karen E Nelson

“…Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. …”
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Prediction of all-cause mortality in Parkinson’s disease with explainable artificial intelligence using administrative healthcare data by You Hyun Park, Yong Wook Kim, Dae Ryong Kang, Sang Chul Lee, Seo Yeon Yoon

“…Abstract Many studies have reported increased mortality risk in patients with Parkinson’s disease (PD), but few have investigated the risk factors for PD mortality, including medical and socioeconomic factors. We applied an explainable artificial intelligence (XAI) model to predict long-term all-cause mortality in patients with PD using administrative healthcare data collected at PD diagnosis. …”
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Explainable Artificial Intelligence for predicting the compressive strength of soil and ground granulated blast furnace slag mixtures by Ahmed Mohammed Awad Mohammed, Omayma Husain, Muyideen Abdulkareem, Nor Zurairahetty Mohd Yunus, Nadiah Jamaludin, Elamin Mutaz, Hashim Elshafie, Mosab Hamdan

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Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence by Manohar Pavanya, Krishnaraj Chadaga, Vennila J, Akhila Vasudeva, Bhamini Krishna Rao, Srikanth Prabhu, Shashikala K Bhat

“…To address this issue, our study aims to develop a more practical ML model by incorporating explainable artificial intelligence (XAI). We prospectively collected real-world clinical data of 19 maternal and fetal clinical features from 237 singleton pregnancies. …”
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Development and Clinical Interpretation of an Explainable AI Model for Predicting Patient Pathways in the Emergency Department: A Retrospective Study by Émilien Arnaud, Pedro Antonio Moreno-Sanchez, Mahmoud Elbattah, Christine Ammirati, Mark van Gils, Gilles Dequen, Daniel Aiham Ghazali

“…Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. …”
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Optimized Clinical Feature Analysis for Improved Cardiovascular Disease Risk Screening by Sofiya Vyshnya, Rachel Epperson, Felipe Giuste, Wenqi Shi, Andrew Hornback, May D. Wang

“…Furthermore, the insights obtained from explainable artificial intelligence techniques enable medical practitioners to offer personalized interventions by prioritizing patient-specific high-risk factors. …”
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Explainable AI for Enhancing Efficiency of DL-Based Channel Estimation by Abdul Karim Gizzini, Yahia Medjahdi, Ali J. Ghandour, Laurent Clavier

“…The support of artificial intelligence (AI) based decision-making is a key element in future 6G networks. …”
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Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images by K. Lakshmi, Sibi Amaran, G. Subbulakshmi, S. Padmini, Gyanenedra Prasad Joshi, Woong Cho

“…With this motivation, this article presents a new explainable artificial intelligence with semantic segmentation and Bayesian machine learning for brain tumors (XAISS-BMLBT) technique. …”
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Modelling radiological features fusion and explainable AI in pneumonia detection: A graph- based deep learning and transformer approach by Pratham Kaushik, Eshika Jain, Vinay Kukreja, Shanmugasundaram Hariharan, Murugaperumal Krishnamoorthy, Vandana Ahuja, Abhishek Bhattacherjee, Rajesh Kumar Kaushal, Shih-Yu Chen

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Optimization of Table Tennis Swing Action Supported by the Temporal Convolutional Network Algorithm in Deep Learning by Shaoxuan Sun, Hongyu Zheng, Zhixin Lin

“…To enhance the navigation accuracy and interpretability of Unmanned Aerial Vehicles (UAVs) in sports analysis, this study proposes an improved model based on the Temporal Convolutional Network (TCN) algorithm, integrated with Explainable Artificial Intelligence. The model aims to analyze real-time data collected by Internet of Things sensors mounted on UAVs to capture athletes’ swing actions during table tennis matches, thereby providing transparent decision support. …”
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Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…This study aims to employ explainable artificial intelligence (XAI) to identify lipidomic biomarkers for liver cancer and to develop a robust predictive model for early diagnosis. …”
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NeuroFuzzXAI: A Hybrid Artificial Intelligence Framework for Epileptic Seizure Detection by Flavia COSTI, Emanuel COVACI, Ovidiu ROMAN, Darian ONCHIS

“…This study proposes a hybrid framework that combines explainable artificial intelligence (XAI) with fuzzy logic to improve both accuracy and interpretability in seizure classification. …”
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