Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review by Umile Giuseppe Longo, Alberto Lalli, Guido Nicodemi, Matteo Giuseppe Pisani, Alessandro De Sire, Pieter D'Hooghe, Ara Nazarian, Jacob F. Oeding, Balint Zsidai, Kristian Samuelsson

“…Included were studies published between September 2018 and February 2024, which evaluated machine learning (ML) model effectiveness in improving orthopaedic imaging. …”
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Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics by Marko M. Kostić, Željana Grbović, Rana Waqar, Bojana Ivošević, Marko Panić, Antonio Scarfone, Aristotelis C. Tagarakis

“…These findings suggest areas for potential improvements in machine technology to improve sowing operations.…”
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Defining analytical skills for human resources analytics: A call for standardization by Konrad Kulikowski

“…PURPOSE: Human resources (HR) analytics systems, powered by big data, AI algorithms, and information technology, are increasingly adopted by organizations to enhance HR’s impact on business performance. …”
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Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations by Ibrahim Antoun, Ibrahim Antoun, Ahmed Abdelrazik, Mahmoud Eldesouky, Xin Li, Georgia R. Layton, Georgia R. Layton, Georgia R. Layton, Mustafa Zakkar, Mustafa Zakkar, Mustafa Zakkar, Riyaz Somani, Riyaz Somani, G. André Ng, G. André Ng, G. André Ng

“…Furthermore, AI-powered remote monitoring integrates wearable-derived insights with real-time decision support, improving patient engagement and adherence. Despite these advances, significant challenges persist, including algorithm transparency, bias, data integration, and regulatory hurdles. …”
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Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction: The Multi‐Ethnic Study of Atherosclerosis by Brittany Saldivar Murphy, Yunbi Nam, Robyn L. McClelland, Isaac Acquah, Miguel Cainzos‐Achirica, Khurram Nasir, Wendy S. Post, Melinda C. Aldrich, Andrew P. DeFilippis

“…Incorporation of SDoH into the MESA Risk Score did not improve predictive ability of the model. Our findings do not support the incorporation of SDoH into current risk prediction algorithms.…”
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Optimizing Stroke Risk Prediction: A Primary Dataset‐Driven Ensemble Classifier With Explainable Artificial Intelligence by Md. Maruf Hossain, Md. Mahfuz Ahmed, Md. Rakibul Hasan Rakib, Mohammad Osama Zia, Rakib Hasan, Md. Rakibul Islam, Md. Shohidul Islam, Md Shahariar Alam, Md. Khairul Islam

“…Future work will focus on incorporating deep learning techniques and medical imaging to further improve classification accuracy and model performance.…”
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Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei, Riad Alharbey

“…ResNet improves feature extraction, VGGNet optimises feature refining, and Swin-Transformer captures contextual dependencies, making the model sensitive to complicated attack patterns across varied network circumstances. …”
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Association of microtubule-based processes gene expression with immune microenvironment and its predictive value for drug response in oestrogen receptor-positive breast cancer by Zhenfeng Huang, Minghui Zhang, Nana Zhang, Mengyao Zeng, Yao Qian, Meng Zhu, Xiangyan Meng, Ming Shan, Guoqiang Zhang, Feng Liu

“…Additionally, functional analyses were conducted to investigate the underlying mechanisms involved. Prognostic risk models were developed via random forest, support vector machines and the least absolute shrinkage and selection operator algorithm. …”
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Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol by Martin Eklund, Brett Delahunt, Lars Egevad, Hemamali Samaratunga, Mattias Rantalainen, Marcello Gambacorta, Einar Gudlaugsson, Bodil Ginnerup Pedersen, Xiaoyi Ji, Karina Dalsgaard Sørensen, Kelvin Szolnoky, Nita Mulliqi, Anders Blilie, Henrik Olsson, Matteo Titus, Geraldine Martinez Gonzalez, Sol Erika Boman, Masi Valkonen, Svein Reidar Kjosavik, José Asenjo, Paolo Libretti, Marcin Braun, Radzislaw Kordek, Roman Łowicki, Kristina Hotakainen, Päivi Väre, Benedicte Parm Ulhøi, Pekka Ruusuvuori, Toyonori Tsuzuki, Emilius Adrianus Maria Janssen, Kimmo Kartasalo

“…Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. …”
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Optimizing role assignment for scaling innovations through AI in agricultural frameworks: An effective approach by Sonia Bisht, Ranjana, Swapnila Roy

“…This model is tested in various agricultural scenarios to evaluate its impact on operational efficiency and innovation scalability. …”
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Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning by Chenbo Yang, Chenbo Yang, Meichen Feng, Juan Bai, Hui Sun, Rutian Bi, Lifang Song, Chao Wang, Yu Zhao, Wude Yang, Lujie Xiao, Meijun Zhang, Xiaoyan Song

“…In conclusion, based on the winter wheat ChD data set and the corresponding canopy hyperspectral data set, combined with 3 FOD calculation methods, 1 band screening method, and 8 modeling algorithms, this study constructed hyperspectral monitoring models for winter wheat ChD. …”
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Can Different Cultivars of <i>Panicum maximum</i> Be Identified Using a VIS/NIR Sensor and Machine Learning? by Gelson dos Santos Difante, Gabriela Oliveira de Aquino Monteiro, Juliana Caroline Santos Santana, Néstor Eduardo Villamizar Frontado, Jéssica Gomes Rodrigues, Aryadne Rhoana Dias Chaves, Dthenifer Cordeiro Santana, Izabela Cristina de Oliveira, Luis Carlos Vinhas Ítavo, Fabio Henrique Rojo Baio, Gabriela Souza Oliveira, Carlos Antonio da Silva Junior, Vanessa Zirondi Longhini, Alexandre Menezes Dias, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro

“…The objectives of this study were to identify ML models able to differentiate <i>P. maximum</i> cultivars and determine which is the best spectral input for these algorithms and whether reducing the sample size improves the response of the algorithms. …”
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Designing CITOBOT: A portable device for cervical cancer screening using human-centered design, smart prototyping, and artificial intelligence by Marcela Arrivillaga, Paula C. Bermúdez, Juan Pablo García-Cifuentes, Hernán Darío Vargas-Cardona, Daniela Neira, Maria del Mar Torres, Mérida Rodríguez-López, Daniela Morales, Bleider Arizala

“…Despite technological advancements that have improved the quality of cervical images captured during visual inspections, several challenges persist. …”
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Application of Machine Learning Methods for Employee Turnover Prediction Based on Open Data by A. N. Kazinets

“…An analysis of existing approaches to predicting staff turnover is conducted, the need to use modern machine learning algorithms is substantiated. Based on an open data set, a model is developed that allows for a high-precision determination of the probability of employee dismissal. …”
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Application of Hybrid Attention Mechanisms in Lithological Classification with Multisource Data: A Case Study from the Altay Orogenic Belt by Dong Li, Jinlin Wang, Kefa Zhou, Jiantao Bi, Qing Zhang, Wei Wang, Guangjun Qu, Chao Li, Heshun Qiu, Tao Liao, Chong Zhao, Yingpeng Lu

“…However, traditional deep learning algorithms fail to distinguish the importance of different features effectively during fusion, leading to insufficient focus in the model. …”
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Review and Prospects of Artificial Intelligence Technology in Virtual Power Plants by Xinxing Liu, Ciwei Gao

“…Finally, the challenges of the VPP in coping with uncertainty, improving algorithmic interpretability and ensuring data security are pointed out, which provides theoretical support for subsequent research in the field of VPPs.…”
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Dynamic Dwell Time Adjustment in Wire Arc-Directed Energy Deposition: A Thermal Feedback Control Approach by Md Munim Rayhan, Abderrachid Hamrani, Fuad Hasan, Tyler Dolmetsch, Arvind Agarwal, Dwayne McDaniel

“…Future work should focus on integrating multi-axis thermal sensors, extending the framework to multi-material deposition scenarios and implementing machine learning algorithms for predictive thermal modeling.…”
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Simulating the root-to-shoot ratio of natural grassland biomass in China by the AutoGluon framework by Rui Guo, Xiaodong Huang, Yangjing Xiu, Minglu Che, Jinlong Gao, Shuai Fu, Qisheng Feng, Tiangang Liang

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Multi-Modal Sensing for Propulsion Estimation in People Post-Stroke Across Speeds by Krithika Swaminathan, Dabin K. Choe, Daekyum Kim, Flore Barde, Teresa C. Baker, Nicholas C. Wendel, Andrew Chin, Gregoire Bergamo, Christopher J. Siviy, Christina Lee, Louis N. Awad, Terry D. Ellis, Conor J. Walsh

“…We show that in the lab, IMU + Insole models improve performance relative to IMU only and Insole only models, with an average root-mean-squared-error (RMSE) of 0.80 %bodyweight (%BW) across the stance phase. …”
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The relationship between low perceived numeracy and cancer knowledge, beliefs, and affect. by Katherine Ross, Justin Stoler, Nick Carcioppolo

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