Intelligence artificielle et fact-checking en Afrique : entre logiques de dépendance et limites de l’automatisation by Sokhna-Fatou SECK-SARR

“…The approach combines content analysis and distanced observation of two fact-checking platforms, chosen on the basis of their local roots and the experimentation of smart tools: Africa Check and Check4Decision. …”
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Intelligent library shelf management system for open-access environments: A CNN-based approach with enhanced image recognition and disorder detection by Xueqi Zhang

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Artificial intelligence-optimized shield parameters for soft ground tunneling in urban environment: A case study of Bangkok MRT Blue Line by Sahatsawat Wainiphithapong, Chana Phutthananon, Sompote Youwai, Pitthaya Jamsawang, Phattarawan Malaisree, Ochok Duangsano, Pornkasem Jongpradist

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Machine Learning in Hospitality: Interpretable Forecasting of Booking Cancellations by Ismael Gomez-Talal, Mana Azizsoltani, Pilar Talon-Ballestero, Ashok Singh

“…This paper addresses this gap by proposing a new approach to predicting hotel booking cancellations rooted in stacked generalization and Explainable Artificial Intelligence (XAI). …”
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Regulatory Insights From 27 Years of Artificial Intelligence/Machine Learning–Enabled Medical Device Recalls in the United States: Implications for Future Governance by Wei-Pin Chen, Wei-Guang Teng, C Benson Kuo, Yu-Jui Yen, Jian-Yu Lian, Matthew Sing, Peng-Ting Chen

“… Abstract BackgroundArtificial intelligence/machine learning (AI/ML) has revolutionized the health care industry, particularly in the development and use of medical devices. …”
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Smart Nutrient Solution Temperature Control System for Oversummering Lettuce Cultivation Based on Adaptive Dung Beetle Optimizer-Fuzzy PID by Yuliang Cai, Zelan Zhao, Wenzhong Guo, Hailing Xu, Yunfei Teng, Xiaobei Han, Qian Zhao, Lichun Wang

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Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing by Suchuan Xing, Yihan Wang, Wenhe Liu

“…Traditional monitoring sensor tools struggle with heterogeneous metrics, temporal correlations, and precise root cause analysis in these environments. This paper proposes a dual-channel deep learning framework that integrates Temporal Convolutional Networks with Variational Autoencoders to address these challenges. …”
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Accuracy evaluation of dental CBCT and scanned model registration method based on pulp horn mapping surface: an in vitro proof-of-concept by Dianhao Wu, Jingang Jiang, Jinke Wang, Shan Zhou, Kun Qian

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A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes by Husnain Ali, Rizwan Safdar, Yuanqiang Zhou, Yuan Yao, Le Yao, Zheng Zhang, Weilong Ding, Furong Gao

“…Traditional monitoring techniques for automatic anomaly detection, identifying the potential variables, and root cause analysis for fault information are not intelligent enough to tackle the intricate problems of real-time practices in the industrial and chemical sectors. …”
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Smartphone‐Embedded Artificial Intelligence‐Based Regression for Colorimetric Quantification of Multiple Analytes with a Microfluidic Paper‐Based Analytical Device in Synthetic Tea... by Meliha Baştürk, Elif Yüzer, Mustafa Şen, Volkan Kılıç

“…Artificial intelligence (AI) and smartphones have attracted significant interest in microfluidic paper‐based colorimetric sensing due to their convenience and robustness. …”
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Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network by Xin Xiong, Feng Gao, Keping Zhou, Yuxu Gao, Chun Yang

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Large-scale groundwater pollution risk assessment research based on artificial intelligence technology: A case study of Shenyang City in Northeast China by Lingjun Meng, Yuru Yan, Haihua Jing, Muhammad Yousuf Jat Baloch, Shouying Du, Shanghai Du

“…Compared with the Artificial Neural Network (ANN) and Random Forest (RF) models, the performance evaluation parameters mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE) are closer to 0, and the coefficient of determination (R2) is closer to 1. …”
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Dissecting the link between PD-1/PD-L1-based immunotherapy and cancer pain: mechanisms, research implications, and artificial intelligence perspectives by Marco Cascella, Brigida Miranda, Carmen Gagliardi, Lucia Santaniello, Milena Mottola, Alida Mancusi, Laura Ferrara, Federica Monaco, Francesca Gargano, Francesco Perri, Alessandro Ottaiano, Maurizio Capuozzo, Ornella Piazza, Stefano Pepe, Anna Crispo, Agostino Guida, Giovanni Salzano, Giustino Varrassi, Luigi Liguori, Francesco Sabbatino, The TRIAL Group

“…Finally, this article discusses the role of artificial intelligence (AI) in advancing research and clinical practice in this context. …”
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Ensemble‐Based, Large‐Eddy Reconstruction of Wind Turbine Inflow in a Near‐Stationary Atmospheric Boundary Layer Through Generative Artificial Intelligence by Alex Rybchuk, Luis A. Martínez‐Tossas, Stefano Letizia, Nicholas Hamilton, Andy Scholbrock, Emina Maric, Daniel R. Houck, Thomas G. Herges, Nathaniel B. deVelder, Paula Doubrawa

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Deep learning-based single-shot computational spectrometer using multilayer thin films by David S. Bhatti, Jioh Lee, Cheolsun Kim, Youngin Choi, Hoon Hahn Yoon, Heung-No Lee

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An Emerging Trend of At-Home Uroflowmetry—Designing a New Vibration-Based Uroflowmeter with Artificial Intelligence Pattern Recognition of Uroflow Curves and Comparing with Other T... by Vincent F. S. Tsai, Yao-Chou Tsai, Stephen S. D. Yang, Ming-Wei Li, Yuan-Hung Pong, Yu-Ting Tsai

“…Therefore, there is a growing need for a user-friendly, artificial intelligence (AI)-powered at-home uroflow monitoring solution. …”
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Can we use lower extremity joint moments predicted by the artificial intelligence model during walking in patients with cerebral palsy in the clinical gait analysis? by Firooz Salami, Mustafa Erkam Ozates, Yunus Ziya Arslan, Sebastian Immanuel Wolf

“…This categorization was based on the normalized root mean square error (nRMSE) between lab-measured and predicted joint moments. …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

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Speaking exams with less anxiety in Intelligent Computer-Assisted Language Assessment (ICALA): mirroring EFL learners’ foreign language anxiety, shyness, autonomy, and enjoyment by Botir Elov, Irodakhon Abdullayeva, Laylo Raupova, Azam Kholikov, Marguba Mirkasimova

“…Abstract A significant number of students experience anxiety when asked to speak in English. This unease, often rooted in factors such as shyness, lack of confidence, uncertainty, and a lack of motivation, can hinder their active participation during English oral exams. …”
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A Food Intake Estimation System Using an Artificial Intelligence–Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study... by Masato Tagi, Yasuhiro Hamada, Xiao Shan, Kazumi Ozaki, Masanori Kubota, Sosuke Amano, Hiroshi Sakaue, Yoshiko Suzuki, Takeshi Konishi, Jun Hirose

“…In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R2) were used as metrics to determine the accuracy of the evaluation process. …”
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