The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

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Identification of Maize Kernel Varieties Using LF-NMR Combined with Image Data: An Explainable Approach Based on Machine Learning by Chunguang Bi, Xinhua Bi, Jinjing Liu, He Chen, Mohan Wang, Helong Yu, Shaozhong Song

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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…Hence, this study proposes a Metaheuristic-Hybrid Metaheuritstic eXplainable Artificial Intelligence (MHMXAI) driven Feature Selection (FS) approach and Deep Learning (DL) models for CKD stage prediction. …”
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A Novel Hybrid Deep Learning Model Enhanced with Explainable AI for Brain Tumor Multi-Classification from MRI Images by Esra Gundogan

“…To assist medical professionals in this difficult and error-prone process and improve both the accuracy and interpretability of the model, this study proposes a new hybrid deep learning model enhanced with explainable artificial intelligence for brain tumor multi-classification from MRI images. …”
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Game Interactive Learning: A New Paradigm towards Intelligent Decision-Making by Junliang Xing, Zhe Wu, Zhaoke Yu, Renye Yan, Zhipeng Ji, Pin Tao, Yuanchun Shi

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Proto-Caps: interpretable medical image classification using prototype learning and privileged information by Luisa Gallée, Catharina Silvia Lisson, Timo Ropinski, Meinrad Beer, Michael Götz

“…Explainable artificial intelligence (xAI) is becoming increasingly important as the need for understanding the model’s reasoning grows when applying them in high-risk areas. …”
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Dementia ascertainment in India and development of nation‐specific cutoffs: A machine learning and diagnostic analysis by Danny Maupin, Hongxin Gao, Emma Nichols, Alden Gross, Erik Meijer, Haomiao Jin

“…A machine learning (ML) model was trained on these classifications, with explainable artificial intelligence to assess feature importance and inform cutoffs that were assessed across demographic groups. …”
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Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation by Nisha Purohit, Chandi Prasad Bhatt

“…Future research directions include exploring transfer learning, improving dataset diversity, and developing explainable artificial intelligence techniques to enhance clinical adoption. …”
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Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMI... by Zhengqiu Yu, Lexin Fang, Yueping Ding

“…SHAP analyses provided detailed insights into how these features influenced model predictions. Conclusions The explainable ML models based on various artificial intelligence methods demonstrated promising clinical applicability in predicting 28-day mortality risk among immunocompromised ICU patients. …”
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Optimized disease prediction in healthcare systems using HDBN and CAEN framework by G. Prabaharan, S.M. Udhaya Sankar, V. Anusuya, K. Jaya Deepthi, Rayappan Lotus, R. Sugumar

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Enhancing action recognition in educational settings using AI-driven information systems for public health monitoring by Changchun Lu, Han Ruijuan

“…IntroductionThe integration of Artificial Intelligence (AI) into educational environments is revolutionizing action recognition, offering a transformative opportunity to enhance public health monitoring. …”
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Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability by Abid Badshah, Basem Yousef Alkazemi, Fakhrud Din, Kamal Z. Zamli, Muhammad Haris

“…Machine learning, a subset of Artificial Intelligence (AI), enables prediction, classification, and automation in agriculture. …”
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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…Soil Index (SI), Unit Weight (γ), Curing Days (CD), Nano-Silica Content (NS%), Cohesion (c), Internal Friction Angle (Ø), Slope Height (H), Slope Angle (β), Pore Water Pressure Ratio (ru) were the features used for Explainable Artificial Intelligence (XAI) and SHAP (Shapley Additive Explanations). …”
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Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization by Vasavi Chithanuru, Mangayarkarasi Ramaiah

“…The ensemble model is fine-tuned using Bayesian optimization to enhance predictive accuracy, while explainable artificial intelligence (XAI) tools—SHAP, LIME, and ELI5—provide interpretable feature insights, improving transparency in model predictions. …”
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Identification of pharmacophore synergism for optimization of estrogen receptor beta binders for hormone dependent forms of breast cancer by Rania A. Hussien, Fatmah Ali S. Alasmary, Vijay H. Masand, Abdul Samad, Rahul D. Jawarkar, Gaurav S. Masand, Sami A. Al-Hussain, Magdi E.A. Zaki

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Prediction of UHPC mechanical properties using optimized hybrid machine learning model with robust sensitivity and uncertainty analysis by ZhiGuang Zhou, Jagaran Chakma, Md Ahatasamul Hoque, Vaskar Chakma, Asif Ahmed

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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…Moreover, the core contributions of IHML lie in its ability to tackle the optimal base-learner and feature sets determination mechanism with the help of Explainable Artificial Intelligence (XAI) and heuristic elbow methods. …”
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Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms by Guanmin Huang, Ying Zhang, Shenghao Gu, Weiliang Wen, Xianju Lu, Xinyu Guo

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A real-time AI tool for hybrid learning recommendation in education: Preliminary results by Chaman Verma

“…This study created an innovative AI tool utilizing the Support Vector Machine (SVM) algorithm on primary samples of Hungarian informatics students to assess their suitability for adopting hybrid learning in their studies. This paper also explained the strength of the model with Shapley Additive exPlanations (SHAP) values of the Explainable Artificial Intelligence (XAI) method. …”
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