FedRecSys: A Federated Learning-Based Recommender System for Concept Drift Adaptation by Anagha Chaudhari, A. A. Hitham Seddig, Roshani Raut, Aliza Sarlan

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Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques by Joan D. Gonzalez-Franco, Alejandro Galaviz-Mosqueda, Salvador Villarreal-Reyes, Jose E. Lozano-Rizk, Raul Rivera-Rodriguez, Jose E. Gonzalez-Trejo, Alexei-Fedorovish Licea-Navarro, Jorge Lozoya-Arandia, Edgar A. Ibarra-Flores

“…Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques—specifically the k-means clustering algorithm—alongside dimensionality reduction methods like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to identify patient groups with varying levels of heart attack risk. …”
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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

“…Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. …”
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A large-scale prospective nested case-control study: developing a comprehensive risk prediction model for early detection of pancreatic cancer in the community-based ESPRIT-AI coho... by Chaoliang Zhong, Penghao Li, Jia Zhao, Xue Han, Beilei Wang, Gang Jin

“…This study aimed to develop a robust risk prediction model for early detection, utilizing a large prospective cohort to ensure generalizability. …”
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Objective Evaluation of Gait Asymmetries in Traditional Racehorses During Pre-Race Inspection: Application of a Markerless AI System in Straight-Line and Lungeing Conditions by Federica Meistro, Maria Virginia Ralletti, Riccardo Rinnovati, Alessandro Spadari

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Deep Learning and Edge Computing in Agriculture: A Comprehensive Review of Recent Trends and Innovations by Apri Junaidi, Siti Zaiton Mohd Hashim, Mohd Shahizan Bin Othman, Mohd Murtadha Bin Mohamad, Hitham Alhussian, Said Jadid Abdulkadir, Maged Nasser, Yunusa Adamu Bena

“…Rice is a vital staple for over half of the global population, yet its production is significantly threatened by leaf diseases caused by fungal and bacterial pathogens. Early and accurate detection of such diseases is critical to minimizing crop loss, particularly under conditions of labor shortages and climate variability. …”
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Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT by Meysam Dadgar, Amaryllis Verstraete, Jens Maebe, Yves D’Asseler, Stefaan Vandenberghe

“…The results also indicate a significant improvement in larger spheres when considering both background variability and contrast recovery coefficient. The high precision deep learning approach proved advantageous for short scans and exhibited potential in improving detectability of small lesions. …”
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Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications by Ashfakul Karim Kausik, Adib Bin Rashid, Ramisha Fariha Baki, Md Mifthahul Jannat Maktum

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EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI by Md. Ehsanul Haque, Mahe Zabin, Jia Uddin

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Radiomics, Pathomics and Connectomics by Debora GIL RESINA GIL RESINA

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Methodology for Feature Selection of Time Domain Vibration Signals for Assessing the Failure Severity Levels in Gearboxes by Antonio Pérez-Torres, René-Vinicio Sánchez, Susana Barceló-Cerdá

“…Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. …”
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Digital biomarkers: Redefining clinical outcomes and the concept of meaningful change by Maria Florencia Iulita, Emmanuel Streel, John Harrison

“…They offer a promising approach for detecting real‐time, objective clinical differences and improving patient outcomes by enabling continuous monitoring, individualized assessments, and leveraging artificial intelligence learning for complex analytical predictions. …”
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Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte

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Prediction cardiovascular deterioration in a paediatric intensive care unit (PicEWS): a machine learning modelling study of routinely collected health-care dataResearch in context by Dan Fredman Stein, Michael J. Carter, John Booth, Mark J. Peters, Samiran Ray, Neil J. Sebire, Payam Barnaghi, Mario Cortina-Borja

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From Indoor to Daylight Electroluminescence Imaging for PV Module Diagnostics: A Comprehensive Review of Techniques, Challenges, and AI-Driven Advancements by Rodrigo del Prado Santamaría, Mahmoud Dhimish, Gisele Alves dos Reis Benatto, Thøger Kari, Peter B. Poulsen, Sergiu V. Spataru

“…It examines key challenges, including ambient light interference and environmental variability, and highlights innovations such as infrared-sensitive indium gallium arsenide (InGaAs) cameras, optical filtering, and periodic current modulation to enhance defect detection. …”
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Establishment of an AI-supported scoring system for neuroglial cells by Annika Bitsch, Manfred Henrich, Svenja Susanne Erika Körber, Kathrin Büttner, Christiane Herden, Christiane Herden

“…The feasibility of a computer-aided scoring system based on artificial intelligence to detect and classify morphological changes in neuroglial cells was assessed in this study. …”
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Diagnostic accuracy of case-identification algorithms for heart failure in the general population using routinely collected health data: a systematic review by Anita Andreano, Vito Lepore, Pietro Magnoni, Alberto Milanese, Caterina Fanizza, Deborah Testa, Alessandro Musa, Adele Zanfino, Paola Rebora, Lucia Bisceglia, Antonio Giampiero Russo, the PROPHET-I study group

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Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A decision-support approach for pathological examination “A pilo... by Ayten Kayı Cangır, Süleyman Gökalp Güneş, Kaan Orhan, Hilal Özakıncı, Yusuf Kahya, Duru Karasoy, Serpil Dizbay Sak

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Comparing AI and Manual Segmentation of Prostate MRI: Towards AI-Driven 3D-Model-Guided Prostatectomy by Thierry N. Boellaard, Roy van Erck, Sophia H. van der Graaf, Lisanne de Boer, Henk G. van der Poel, Laura S. Mertens, Pim J. van Leeuwen, Behdad Dashtbozorg

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Rapid and Efficient Screening of <i>Helicobacter pylori</i> in Gastric Samples Stained with Warthin–Starry Using Deep Learning by José Aneiros-Fernández, Pedro Montero Pavón, Natalia García Gómez, Rosa María Palo Prian, Ismael Sánchez García, Ana Isabel Romero Ortiz, Rodrigo López Castro, César Casado-Sánchez, Víctor Sánchez Turrión, Antonio Luna, Manuel Álvaro Berbís

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