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

“…In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the centre of the image. 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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Design, Construction, and Evaluation of an Automatic Feeder Control System for Sugarcane Billet Planters by B. Abbasian, M. E. Khorasani Ferdavani, H. Zaki Dizaji

“…The load cell control method emerged as the most effective solution, successfully eliminating the need for operators while ensuring high standards of planting quality and efficiency. …”
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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease by Mohammed A. Chowdhury, Rodrigue Rizk, Conroy Chiu, Jing J. Zhang, Jamie L. Scholl, Taylor J. Bosch, Arun Singh, Lee A. Baugh, Jeffrey S. McGough, KC Santosh, William C.W. Chen

“…We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. …”
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Ethical AI in Social Sciences Research: Are We Gatekeepers or Revolutionaries? by Remus Runcan, Vasile Hațegan, Ovidiu Toderici, Gabriel Croitoru, Mihaela Gavrila-Ardelean, Lavinia Denisia Cuc, Dana Rad, Alina Costin, Tiberiu Dughi

“…The findings highlight dominant ethical concerns, including governance, bias, transparency, and fairness, emphasizing the need for interdisciplinary collaborations and responsible AI frameworks. While AI offers efficiency and scalability in research, unresolved issues related to algorithmic bias, governance, and public trust persist. …”
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Robust vector-weighted and matrix-weighted multi-view hard c-means clustering by Zhe Liu, Sarah Aljohani, Sijia Zhu, Tapan Senapati, Gözde Ulutagay, Salma Haque, Nabil Mlaiki

“…With the rapid advancement of information technology, multi-view data has become ubiquitous, prompting extensive attention towards multi-view clustering algorithms. Despite significant strides, several challenges persist: (1) the prevalence of noise and outliers in real-world multi-view data often compromises the efficacy of clustering; (2) most existing multi-view clustering algorithms predominantly assess the overall contribution of each view, while neglecting the intra-view contributions. …”
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Navigating the landscape: Unveiling the reflection of AI in the sharing economy through bibliometric-based approach by Veljko Uskoković, Milica Maričić, Aleksandra Dacić-Pilčević, Veljko Jeremić

“…Additionally, AI plays a crucial role in promoting energy-efficient behaviour in smart accommodations, while algorithmic oversight is increasingly integrated into digital labour platforms to balance automation with human dignity. …”
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Advancing clinical biochemistry: addressing gaps and driving future innovations by Haiou Cao, Enwa Felix Oghenemaro, Amaliya Latypova, Amaliya Latypova, Munthar Kadhim Abosaoda, Munthar Kadhim Abosaoda, Munthar Kadhim Abosaoda, Gaffar Sarwar Zaman, Anita Devi

“…However, concerns regarding algorithmic bias, data privacy, lack of transparency in decision-making (“black box” models), and over-reliance on automated systems pose significant challenges that must be addressed for responsible AI integration. …”
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Optimizing Inotropic Infusion With Cluster Specific AI Decision Models and Digital Twins by Vidya S Nair, G. D. Heshan Niranga, Aryalakshmi C.S, Dipu T. Sathyapalan, Thushara Madathil, Rahul Krishnan Pathinarupothi

“…Conventional feedback controllers, including fuzzy logic controllers (FLC), struggle to adapt to complex BP variations due to fixed algorithms and intracohort variability in drug responses. …”
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A Hybrid Approach to Model Hospitals and Evaluate Wards’ Performances by Arsineh Boodaghian Asl, Luca Marzano, Jayanth Raghothama, Adam S. Darwich, Nina Falk, Patrik Bodeby, Sebastiaan Meijer

“…Through network analysis, such as structural hole and flow algorithms, the approach identifies common persistent bottlenecks from the flow and structural perspectives, while percolation and perturbation analyses measure the performance improvement of wards based on variations in patient flow, and the simulations enable the investigation of scenarios. …”
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Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting by Jaemyung Shin, Ryan Kang, Kinam Hyun, Zhangkang Li, Hitendra Kumar, Kangsoo Kim, Simon S. Park, Keekyoung Kim

“…In this study, a high‐throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. …”
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Analysis of Distributed Dynamic Loads Induced by the Own Mass of Manipulator Links and Their Visualization on Interactive 3D Computer Models by Muratulla Utenov, Tarek Sobh, Yerbol Temirbekov, Saltanat Zhilkibayeva, Sarosh Patel, Dauren Baltabay, Zhadyra Zhumasheva

“…The implementation of these algorithms and program codes has enabled the creation of three-dimensional computer models of manipulators with clear visual representations of links, their cross-sections, kinematic pairs, grippers, and loads, differing in structure and degrees of freedom while ensuring a comprehensive view from all spatial perspectives. …”
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Recent advances in machine learning for defects detection and prediction in laser cladding process by X.C. Ji, R.S. Chen, C.X. Lu, J. Zhou, M.Q. Zhang, T. Zhang, H.L. Yu, Y.L. Yin, P.J. Shi, W. Zhang

“…Furthermore, it encapsulates prevalent machine learning models and algorithms employed for defect detection. The findings highlight the efficacy of machine learning algorithms in detecting defects within laser cladding coatings, while concurrently establishing correlations between feature signals, coating defects, and cladding processes. …”
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Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis by Siqi Li, Di Miao, Qiming Wu, Chuan Hong, Danny D’Agostino, Xin Li, Yilin Ning, Yuqing Shang, Ziwen Wang, Molei Liu, Huazhu Fu, Marcus Eng Hock Ong, Hamed Haddadi, Nan Liu

“…Background: Federated learning (FL) holds promise for safeguarding data privacy in healthcare collaborations. While the term “FL” was originally coined by the engineering community, the statistical field has also developed privacy-preserving algorithms, though these are less recognized. …”
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Augmented intelligence with voice assistance and automated machine learning in Industry 5.0 by Alexandros Bousdekis, Mina Foosherian, Mattheos Fikardos, Stefan Wellsandt, Katerina Lepenioti, Enrica Bosani, Gregoris Mentzas, Klaus-Dieter Thoben

“…However, existing research works on voice assistants relies on knowledge management and simulation methods instead of data-driven algorithms. In addition, practical application and evaluation in real-life scenarios are scarce and limited in scope. …”
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Predictive model for customer satisfaction analytics in E-commerce sector using machine learning and deep learning by Hoanh-Su Le, Thao-Vy Huynh Do, Minh Hoang Nguyen, Hoang-Anh Tran, Thanh-Thuy Thi Pham, Nhung Thi Nguyen, Van-Ho Nguyen

“…Results show BERT and Bi-GRU yield over 70% sentiment accuracy, while XGBoost achieves 80%+ satisfaction prediction accuracy. …”
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ARchitect: advancing architectural visualization and interaction through handheld augmented reality by Sabahat Israr, Sabahat Israr, Mudassar Ali Khan, Muhammad Shahid Anwar, Kamran Ahmad Awan, Saoucene Mahfoudh, Turki Althaqafi, Wadee Alhalabi, Wadee Alhalabi

“…The proposed approach enables users to dynamically overlay and manipulate 3D architectural elements, such as roofs, windows, and doors, within their physical environment using AR raycasting and device sensors. Algorithms supporting translation, rotation, and scaling allow precise adjustments to model placement while integrating metadata to enhance design comprehension. …”
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Building health systems capable of leveraging AI: applying Paul Farmer’s 5S framework for equitable global health by Liam G. McCoy, Azra Bihorac, Leo Anthony Celi, Matthew Elmore, Divya Kewalramani, Teddy Kwaga, Nicole Martinez-Martin, Renata Prôa, Joel Schamroth, Jonathan D. Shaffer, Alaa Youssef, Amelia Fiske

“…A broad base of critical scholarship has highlighted important issues with healthcare AI, including algorithmic bias and inequitable and inaccurate model outputs. …”
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A Year of Critical Care: The Changing Face of the ICU During COVID-19 by Atiya Dhala, Deepa Gotur, Steven Huan-Ling Hsu, Aditya Uppalapati, Marco Hernandez, Jefferson Alegria, Faisal Masud

“…Houston Methodist’s incident command center team navigated this health crisis by ramping up its bed capacity, streamlining treatment algorithms, and optimizing ICU staffing while ensuring adequate supplies of personal protective equipment (PPE), ventilators, and other ICU essentials. …”
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High-precision prediction of non-resonant high-order harmonics energetic particle modes via stacking ensemble strategies by Sheng Liu, Zhenzhen Ren, Weihua Wang, Kai Zhong, Jinhong Yang, Hongwei Ning

“…The evaluation results indicate that the performance of the proposed model surpasses most supervised learning algorithms. Specifically, in comparison with the SVR and Bagging algorithms, the growth rate predictions of stacking model reduces Root mean squared error (RMSE) by 45% and 33%, mean absolute error (MAE) by 47% and 32%, and increases the R -squared coefficient ( R ^2 ) by 5% and 3%, respectively. …”
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Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study by Urszula Bialonczyk, Malgorzata Debowska, Lu Dai, Abdul Rashid Qureshi, Leon Bobrowski, Magnus Soderberg, Bengt Lindholm, Peter Stenvinkel, Tomasz Lukaszuk, Jan Poleszczuk

“…Age and copeptin were selected by all algorithms, while diabetes, male sex, choline, and osteoprotegerin were chosen by four methods. …”
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