An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining by Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli

“…Unlike empirical equations that lack generalizability or black box ML models with limited transparency, the proposed approach embeds domain specific physical laws while leveraging data driven learning to improve both predictive accuracy and interpretability. …”
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Unsupervised Rural Flood Mapping from Bi-Temporal Sentinel-1 Images Using an Improved Wavelet-Fusion Flood-Change Index (IWFCI) and an Uncertainty-Sensitive Markov Random Field (US... by Amin Mohsenifar, Ali Mohammadzadeh, Sadegh Jamali

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“Diwan”: Constructing the Largest Annotated Corpus for Arabic Poetry by Badriyya B. Al-Onazi, Wadee A. Nashir, Asma A. Al-Shargabi

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Using the LTO Network Level 1 Blockchain to Automate Inter-Organizational Business Processes by Khrypko Serhii L., Shcherbakov Serhii S.

“…However, organizations face difficulties when trying to obtain such benefits for inter-organizational processes, partly due to a lack of trust. This paper considers a blockchain approach to address this problem. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…There is a growing interest in focusing on Artificial Intelligence (AI) use for anaemia prediction, however, traditional AI models (black boxes) lack transparency, which causes doctors not to pick them up for practical usage. …”
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Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model by Abeer Fayez Al Bataineh, Khalid M. O. Nahar, Hayel Khafajeh, Ghassan Samara, Raed Alazaidah, Ahmad Nasayreh, Ayah Bashkami, Hasan Gharaibeh, Waed Dawaghreh

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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…However, deep learning algorithms are difficult to explain, like a “black box” that lacks insight. …”
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Artificial Intelligence in Healthcare: Medical Students' Perspectives on Balancing Innovation, Ethics, and Patient-Centered Care by Eleanor Roy, Sara Malafa, Lina M. Adwer, Houda Tabache, Tanishqa Sheth, Vasudha Mishra, Moaz Elsayed Abouelmagd, Andrea Cushieri, Sajjad Ahmed Khan, Mihnea-Alexandru Gaman, Juan C. Puyana, Francisco Javier Bonilla-Escobar

“…We argue that while AI can support autonomy by providing personalized insights, opaque “black box” models and lack of informed consent can undermine shared decision-making and trust. …”
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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…Furthermore, the lack of standardized evaluation benchmarks makes it difficult to compare the performance of different DL models in hydrogeological contexts. …”
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Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study by Qing Hua, Fengchun Yang, Yadan Zhou, Fenglian Shi, Xiaoyan You, Jing Guo, Li Li

“…The SHAP method captures highly relevant risk factors for model interpretation, alleviating concerns about the “black box” problem of ML techniques.…”
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Pseudorandom Density Matrices by Nikhil Bansal, Wai-Keong Mok, Kishor Bharti, Dax Enshan Koh, Tobias Haug

“…We also establish lower bounds on the purity needed for efficient testing and black-box distillation. Finally, we introduce memoryless PRSs, a noise-robust notion of PRS, which are indistinguishable to Haar random states for efficient algorithms without quantum memory, as well as noise-robust quantum money. …”
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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model by Bihua Yao, Xingyu Yu, Liannv Qiu, Er-min Gu, Siyu Mao, Lei Jiang, Jijun Tong, Jianguo Wu

“…The integration of SHAP ensures the model’s clinical interpretability, mitigating concerns surrounding the “black-box” nature of many machine learning approaches. …”
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Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment by Rahul Kumar, Kiran Marla, Kyle Sporn, Phani Paladugu, Akshay Khanna, Chirag Gowda, Alex Ngo, Ethan Waisberg, Ram Jagadeesan, Alireza Tavakkoli

“…AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

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Research on High Arch Dam Deformation Monitoring Model with Deep Capturing Related Features in Factor-time Dimensions by XUE Jianghan, ZHANG Pengtao, TIAN Jichen, LU Xiang, CHEN Jiankang, Guo Yinju

“…However, at the present stage, the dam prediction model based on machine learning mostly adopts the means of data preprocessing, using optimization algorithm, and using the model's characteristics to stack multiple models, lacking in in-depth consideration of the physical mechanism of dam deformation. …”
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Artificial intelligence in drug development: reshaping the therapeutic landscape by Sarfaraz K. Niazi, Zamara Mariam

“…However, AI models are generally considered “black boxes,” making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. …”
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Skin region images extracted from 3D total body photographs for lesion detection by Anup Saha, Joseph Adeola, Nuria Ferrera, Adam Mothershaw, Gisele Rezze, Séraphin Gaborit, Brian D’Alessandro, Robert Voskanyan, Gyula Szabó, Balázs Pataki, Hayat Rajani, Sana Nazari, Hassan Hayat, Laura Serra-García, Clare Primiero, Serena Bonin, Iris Zalaudek, H. Peter Soyer, Josep Malvehy, Rafael Garcia

“…While these lesion-centric datasets have been fundamental for developing diagnostic algorithms, they lack the context of the surrounding skin, which is critical for improving lesion detection. …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…Furthermore, the integration of crack detection results with coordinate transformation algorithms enabled the precise geospatial localization of cracks. …”
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Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures by Yudong Shi, Yinggan Zhang, Jiansen Wen, Zhou Cui, Jianhui Chen, Xiaochun Huang, Cuilian Wen, Baisheng Sa, Zhimei Sun

“…However, many conventional ML algorithms operate as “black‐boxes”, lacking transparency in revealing explicit relationships between material features and target properties. …”
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