Explaining the Link Between Alcohol and Homicides: Insights from the Analysis of Legal Cases in Lithuania by Laura Miščikienė, Justina Trišauskė, Mindaugas Štelemėkas, Kristina Astromskė

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Root and canopy traits and adaptability genes explain drought tolerance responses in winter wheat. by A S Nehe, M J Foulkes, I Ozturk, A Rasheed, L York, S C Kefauver, F Ozdemir, A Morgounov

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Explaining Trends and Factors Affecting Export Diversification in ASEAN and SAARC Regions: An Empirical Analysis by SHABANA NOUREEN, ZAFAR MAHMOOD

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A Controlled Variation Approach for Example-Based Explainable AI in Colorectal Polyp Classification by Miguel Filipe Fontes, Alexandre Henrique Neto, João Dallyson Almeida, António Trigueiros Cunha

“…This study presents an example-based explainable artificial intehlligence (XAI) approach using Pix2Pix to generate synthetic polyp images with controlled size variations and LIME to explain classifier predictions visually. …”
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Explainable AI and monocular vision for enhanced UAV navigation in smart cities: prospects and challenges by Shumaila Javaid, Shumaila Javaid, Shumaila Javaid, Muhammad Asghar Khan, Hamza Fahim, Bin He, Bin He, Bin He, Nasir Saeed

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Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis by Qiqi Yan, Guiling Liu, Ruifeng Wang, Dandan Li, Xiaoli Chen, Jingjing Cong, Deguang Wang

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Explainable hybrid transformer for multi-classification of lung disease using chest X-rays by Xiaoyang Fu, Rongbin Lin, Wei Du, Adriano Tavares, Yanchun Liang

“…This paper proposes an explainable transformer with a hybrid network structure (LungMaxViT) combining CNN initial stage block with SE block to improve feature recognition for predicting Chest X-ray images for multiple lung disease classification. …”
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Enhancing Medicare Fraud Detection With a CNN-Transformer-XGBoost Framework and Explainable AI by Mohammad Balayet Hossain Sakil, Md Amit Hasan, Md Shahin Alam Mozumder, Md Rokibul Hasan, Shafiul Ajam Opee, M. F. Mridha, Zeyar Aung

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Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications by Sayda Umma Hamida, Mohammad Jabed Morshed Chowdhury, Narayan Ranjan Chakraborty, Kamanashis Biswas, Shahrab Khan Sami

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Explaining the conceptual model of organizational ethics based on Nahjul-Balagha in the country\'s health system by Behzad Abdollahi, Rostam Qara Daghi, Kambiz Hamidi, Behroz Bayat, Seyed Hamid Hoseyni

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Explaining reasons for cheating in exams among undergraduate nursing students: a qualitative study by Seyed Kazem Mousavi, Mohsen Kamali

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Explainable graph neural network based on metabolic brain imaging for differential diagnosis of parkinsonism by Ronghua Ling, Ronghua Ling, Xingxing Cen, Shaoyou Wu, Min Wang, Ying Zhang, Juanjuan Jiang, Jiaying Lu, Yingqian Liu, Chuantao Zuo, Jiehui Jiang, Yinghui Yang, Zhuangzhi Yan, Zhuangzhi Yan

“…This study introduces an explainable graph neural network (GNN) framework integrating a Regional Radiomics Similarity Network (R2SN) and Transformer-based attention mechanisms to address this diagnostic dilemma.MethodsOur study prospectively enrolled 1,495 participants, including 220 healthy controls and 1,275 patients diagnosed with idiopathic Parkinson’s disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP), all undergoing standardized 18F-fluorodeoxyglucose positron emission tomography imaging. …”
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Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI by Insu Jeon, Minjoong Kim, Dayeong So, Eun Young Kim, Yunyoung Nam, Seungsoo Kim, Sehoon Shim, Joungmin Kim, Jihoon Moon

“…<b>Background:</b> As the demand for early and accurate diagnosis of autism spectrum disorder (ASD) increases, the integration of machine learning (ML) and explainable artificial intelligence (XAI) is emerging as a critical advancement that promises to revolutionize intervention strategies by improving both accuracy and transparency. …”
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Explaining key stakeholders’ preferences for potential policies governing psychiatric electroceutical intervention use by Aaron M. McCright, Eric D. Achtyes, Robyn Bluhm, Laura Y. Cabrera

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Genetic Profile and Symptom Pattern Explain Variability of Deep Brain Stimulation Effect in Dystonia by Mahboubeh Ahmadipour, Luigi M. Romito, Antonio E. Elia, Nico Golfré Andreasi, Roberto Eleopra, Alberto Mazzoni, Fabiana Colucci

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MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors by Abena Achiaa Atwereboannah, Wei-Ping Wu, Mugahed A. Al-antari, Sophyani B. Yussif, Chukwuebuka J. Ejiyi, Edwin K. Tenagyei, Grace-Mercure B. Kissanga, Gyarteng S. A. Emmanuel, Yeong Hyeon Gu, Emmanuel Ahene

“…All data and code are available at https://github.com/GracedAbena/MEN-Leveraging-Explainable-Multimodal-Encoding-Network .…”
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Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability by Yan Jiang, Hong-wei Wang, Fang-ying Tian, Yue Guo, Xiu-mei Wang

“…LASSO regression was used to screen the variables, and the XGBoost model was established (136 cases in the training set and 60 cases in the test set). Shapley additive explain (SHAP) method was used to explain the importance of variables. …”
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Explainable Machine Learning: A SHAP value-based approach to locational frequency stability by Alinane B. Kilembe, Robert I. Hamilton, Panagiotis N. Papadopoulos

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Explaining and Analyzing the Realist Review Method: An Approach to Evaluating and Improving the Performance of Complex Interventions by Shahnaz Akbari Emami, Hamed Roshancheshm, Sara Fathi

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Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI by Aymin Javed, Nadeem Javaid, Muhammad Hasnain, Umair Sarfraz, Imran Ahmed, Muhammad Shafiq, Jin-Ghoo Choi

“…To determine the contribution of features for miscarriage prediction two eXplainable Artificial Intelligence techniques are applied to EDI-Blend and DRIM-Net: Interpretable Model-agnostic Explanations and SHapley Additive exPlanations. …”
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