Assessment of Bone Aging—A Comparison of Different Methods for Evaluating Bone Tissue by Paweł Kamiński, Aleksander Gali, Rafał Obuchowicz, Michał Strzelecki, Adam Piórkowski, Marcin Kociołek, Elżbieta Pociask, Joanna Kwiecień, Karolina Nurzyńska

“…Correlation analysis indicated that using certain features in specific regions of interest is promising for accurate age estimation. Machine learning models demonstrated that when using uncorrelated features, the optimal mean absolute error (MAE) for age estimation is 5.20, whereas when employing convolutional networks on the texture feature maps yields the best result of 9.56. …”
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Efficiency Enhancement and Estimation of Photovoltaic Energy Generation Using Dual-Axis Tracking Systems by Aditya Aggarwal, Himanshu Himanshu, Manav Sidana, Girish Gupta, Ishtdeep Singh Sodhi, Anamika Sharma

“…This work has utilised the applications of various machine learning models, such as decision trees, AdaBoost, and K-nearest neighbour, for predicting energy generation. …”
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Artificial Intelligence in Bronchopulmonary Dysplasia: A Review of the Literature by Tony Jha, Sana Suhail, Janet Northcote, Alvaro G. Moreira

“…AI has helped advance BPD management strategies by optimizing treatment pathways and prognostic predictions through computational modeling. …”
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A review of recent artificial intelligence for traditional medicine by Chengbin Hou, Yanzhuo Gao, Xinyu Lin, Jinchao Wu, Ning Li, Hairong Lv, William Cheng-Chung Chu

“…By leveraging advanced algorithms and models, AI can improve decision-making efficiency, optimize diagnosis accuracy, enhance patient experience, and reduce costs. …”
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How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review by Edo Belva Firmansyah, Marcos R. Machado, João Luiz Rebelo Moreira

“…The study also explores the evolution of CLV research, particularly in the application of Machine Learning (ML) for risk-adjusted CLV. Our findings offer a comprehensive overview, laying the groundwork for future research and helping businesses refine risk management strategies, identify high-risk customers, and enhance customer value through more dynamic, data-driven models.…”
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Advancements in digital drilling technology for deep engineering: a review by Keqi Fan

“…The establishment of penetration, cutting, and rock-breaking models was discussed based on the mechanical interaction during rock drilling. …”
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Application and Effectiveness of Modern Technologies in Sports Training - a Literature Review by Tomasz Włoch, Marcin Kwiatkowski, Tomasz Koziński, Marta Donderska, Paulina Bochniak, Jakub Kołacz, Patrycja Oleś, Mateusz Bryła, Zofia Bryła, Monika Szpaczyńska-Kwiatkowska

“…This review provides an overview of the utilization and effectiveness of these technologies in contemporary sport with main focus on their role in the tracking real-time performance, tailored training, and the development of cognitive skills. AI and machine learning models provide insightful recommendations on optimizing training loads and injury forecasting, whereas VR and AR create experiential environments for developing decision-making and mental resilience. …”
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Advancing Alzheimer's Therapy: Computational strategies and treatment innovations by Jibon Kumar Paul, Abbeha Malik, Mahir Azmal, Tooba Gulzar, Muhammad Talal Rahim Afghan, Omar Faruk Talukder, Samar Shahzadi, Ajit Ghosh

“…Our findings underscore the critical role of computational models in elucidating disease mechanisms, identifying therapeutic targets, and expediting drug discovery. …”
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Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation by Yizhe Zhang, Yinan Guo, Xiusong You, Lunfeng Guo, Bing Miao, Hao Li

“…Driver intention recognition is essential for optimizing driving decisions by dynamically adjusting speed and trajectory to enhance system performance. …”
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Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement by Elżbieta Wójcik-Gront, Bartłomiej Zieniuk, Magdalena Pawełkowicz

“…AI systems integrate machine learning (ML) and deep learning (DL) with big data to identify complex patterns and relationships by analyzing vast genomic, phenotypic, and environmental datasets. …”
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Noninvasive imaging biomarker reveals invisible microscopic variation in acute ischaemic stroke (≤ 24 h): a multicentre retrospective study by Kui Sun, Rongchao Shi, Xinxin Yu, Ying Wang, Wei Zhang, Xiaoxia Yang, Mei Zhang, Jian Wang, Shu Jiang, Haiou Li, Bing Kang, Tong Li, Shuying Zhao, Yu Ai, Jianfeng Qiu, Haiyan Wang, Ximing Wang

“…Through a series of procedures: head alignment, co-registration of NCCT and DWI, the volume of interest delineation and feature extraction. Multiple ML models (random forest, RF; support vector machine, SVM; logistic regression, LR; multilayer perceptron, MLP) were used to discriminate microscopic AIS and non-AIS. …”
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A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation by Rahul Haripriya, Nilay Khare, Manish Pandey, Sreemoyee Biswas

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In situ and dynamic screening of extracellular vesicles as predictive biomarkers in immune-checkpoint inhibitor therapies by Yihe Wang, Yue Sun, Mengqi Liu, Chao Wang, Miao Huang, Jiaoyan Qiu, Ningkai Yang, Yu Zhang, Hong Liu, Lin Han

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Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review by Salman Khalid, Soo-Ho Jo, Syed Yaseen Shah, Joon Ha Jung, Heung Soo Kim

“…These findings could contribute to optimizing industrial processes and advancing health management strategies for critical components.…”
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Comparison of nanoimaging and nanoflow based detection of extracellular vesicles at a single particle resolution by Shihan Xu, Zhengrong Zhang, Bridgette C. Melvin, Nibedita Basu Ray, Seiko Ikezu, Tsuneya Ikezu

“…Additionally, we applied machine learning models to correct for false positive particles identified in imaging‐based results by fitting size distribution data.…”
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Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning by Alfredo Petrella, Marco Miozzo, Paolo Dini

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A cloud-based framework for the quantification of the spatially-explicit uncertainty of remotely sensed benthic habitats by Spyridon Christofilakos, Alina Blume, Avi Putri Pertiwi, Chengfa Benjamin Lee, Dimosthenis Traganos, Peter Reinartz

“…The goal of this study is to estimate the per pixel uncertainty of a Random Forest classification of four benthic habitats and exploit it to retrain the model through training data selection by bootstrapping and producing an ensemble model. …”
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Innovations in animal health: artificial intelligence-enhanced hematocrit analysis for rapid anemia detection in small ruminants by Aftab Siddique, Sudhanshu S. Panda, Sophia Khan, Seymone T. Dargan, Savana Lewis, India Carter, Jan A. Van Wyk, Ajit K. Mahapatra, Eric R. Morgan, Thomas H. Terrill

“…This work demonstrates that AI-driven biosensors can be used for on-site rapid anemia detection. Optimized machine learning models maximize detection accuracy, proving the sensor’s validity and rapidity in assessing anemia levels. …”
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Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence by David B. Olawade, Kusal Weerasinghe, Mathugamage Don Dasun Eranga Mathugamage, Aderonke Odetayo, Nicholas Aderinto, Jennifer Teke, Stergios Boussios

“…In conclusion, future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses. …”
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Advancing Medical Research Through Artificial Intelligence: Progressive and Transformative Strategies: A Literature Review by Ahmad R. Al‐Qudimat, Zainab E. Fares, Mai Elaarag, Maha Osman, Raed M. Al‐Zoubi, Omar M. Aboumarzouk

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