Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

“…Abstract Background Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation, which suffer from low specificity and high interobserver variability. This study aimed to develop and validate an artificial intelligence (AI)-driven diagnostic model to improve diagnostic accuracy and reduce variability. …”
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Repeatability and reproducibility of brain age estimates in multiple sclerosis for three publicly available models by Lonneke Bos, David R. van Nederpelt, J.H. Cole, E.M.M. Strijbis, B. Moraal, J.P.A. Kuijer, B.M.J. Uitdehaag, F. Barkhof, A.M. Wink, H. Vrenken, B. Jasperse

“…Accelerated brain aging is a marker of disease-related neurodegeneration in multiple sclerosis (MS). Artificial intelligence models, trained on healthy individuals, can estimate age from brain MRI scans, but the effects of technical variations between MR scanners and conditions on these estimates are currently insufficiently investigated. …”
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Functional Verification of Extended Kalman Filter on FPGA for Railway Wheelset Parameters Estimation by Khakoo Mal, Tayab Din Memon, Imtiaz Hussain Kalwar, Allah Rakhio Junejo, Majid Hussain Memon, Tarique Rafique Memon, Sufyan Ali Memon, Bhawani Shankar Chowdhry

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Techniques and Methods for Fatty Acid Analysis in Lipidomics: Exploring <i>Pinus cembroides</i> Kernels as a Sustainable Food Resource by Luis Ricardo León-Herrera, Luis Miguel Contreras-Medina, Ana Angélica Feregrino-Pérez, Christopher Cedillo, Genaro Martín Soto-Zarazúa, Miguel Angel Ramos-López, Samuel Tejeda, Eduardo Amador-Enríquez, Enrique Montoya-Morado

“…Likewise, some considerations are addressed for the treatment of data obtained in the detection of fatty acids from bioformatics and the evaluation of the data through statistical methods and artificial intelligence and deep learning. …”
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Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study by Eun-Tae Jeon, Heemoon Park, Jung-Kyu Lee, Eun Young Heo, Chang Hoon Lee, Deog Kyeom Kim, Dong Hyun Kim, Hyun Woo Lee

“…In contrast, the Clin model used only clinical variables. The primary outcomes were moderate-to-severe and severe AE-COPD events within a year of spirometry. …”
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Objective determination of initiation stress and damage stress in rock failure using acoustic emission and digital image correlation by Peng Liang, Yanbo Zhang, Xulong Yao, Guangyuan Yu, Qiang Han, Junling Liu

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Early diagnosis and survival outcomes in silicosis: a retrospective cohort study of 11,809 patients in Guangdong Province, China (1956–2020) by Chunyue Fan, Yuhao Wang, Yuhao Wang, Jingjing Chen, Qiaoli Wei, Shijie Hu, Lihua Xia, Jiawen Huang, Weihui Liang, Lin Wu, Xudong Li

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Source apportionment of PM10 based on offline chemical speciation data at 24 European sites by Xiansheng Liu, Xun Zhang, Bowen Jin, Tao Wang, Siyuan Qian, Jin Zou, Vy Ngoc Thuy Dinh, Jean-Luc Jaffrezo, Gaëlle Uzu, Pamela Dominutti, Sophie Darfeuil, Olivier Favez, Sébastien Conil, Nicolas Marchand, Sonia Castillo, Jesús D. de la Rosa, Stuart Grange, Christoph Hueglin, Konstantinos Eleftheriadis, Evangelia Diapouli, Manousos-Ioannis Manousakas, Maria Gini, Silvia Nava, Giulia Calzolai, Célia Alves, Marta Monge, Cristina Reche, Roy M. Harrison, Philip K. Hopke, Andrés Alastuey, Xavier Querol

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Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis by Lingke Liu, Minfei Hu, Yun Zhou, Fei Zheng, Xiaohui Ma, Li Yang, Yiping Xu, Liping Teng, Bin Hu, Meiping Lu, Xuefeng Xu

“…The radiomics score combining with clinical variables was used to establish a prediction model for JDM-ILD. …”
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Process-driven strength enhancement and progressive damage analysis of three-dimensional woven composites traction rods by Hao Huang, Zhongde Shan, Yanming Xing, Zitong Guo, Chunguang Yang, Jianhua Liu, Zheng Sun, Xiaohui Ao, Dong Wang, Chenchen Tan, Weihao Wang, Juncheng Luo

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Diagnosis of Device Exception Based on Causality of Device Indicators by Zhaohui Wang, Yan Wei, Longhua Shang, Shiwei Zhang, Shixiong Bao, Zhengren Li

“…Existing intelligent exception diagnosis methods face challenges in unstable feature extraction and difficulty detecting anomalies in high-dimensional spaces. …”
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…The development of artificial intelligence provides a new approach for early disease identification. …”
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P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices by Yuta Sasatake, Kojiro Matsushita

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Emotion-Recognition System for Smart Environments Using Acoustic Information (ERSSE) by Gabriela Santiago, Jose Aguilar, Rodrigo García

“…The system is based on a sound pattern for emotion recognition and the autonomic cycle of intelligent sound analysis (ISA), defined by three tasks: variable extraction, sound data analysis, and emotion recommendation. …”
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DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge by Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew, Alexandre Hutton, Roland Wiest, Johannes Kaesmacher, Uta Hanning, Arsany Hakim, Richard Zubal, Waldo Valenzuela, David Robben, Diana M. Sima, Vincenzo Anania, Arne Brys, James A. Meakin, Anne Mickan, Gabriel Broocks, Christian Heitkamp, Shengbo Gao, Kongming Liang, Ziji Zhang, Md Mahfuzur Rahman Siddiquee, Andriy Myronenko, Pooya Ashtari, Sabine Van Huffel, Hyunsu Jeong, Chiho Yoon, Chulhong Kim, Jiayu Huo, Sebastien Ourselin, Rachel Sparks, Albert Clèrigues, Arnau Oliver, Xavier Lladó, Liam Chalcroft, Ioannis Pappas, Jeroen Bertels, Ewout Heylen, Juliette Moreau, Nima Hatami, Carole Frindel, Abdul Qayyum, Moona Mazher, Domenec Puig, Shao-Chieh Lin, Chun-Jung Juan, Tianxi Hu, Lyndon Boone, Maged Goubran, Yi-Jui Liu, Susanne Wegener, Florian Kofler, Ivan Ezhov, Suprosanna Shit, Moritz R. Hernandez Petzsche, Michael Müller, Bjoern Menze, Jan S. Kirschke, Benedikt Wiestler

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Multi-Stage Data Processing for Enhancing Korean Cattle (Hanwoo) Weight Estimations by Automated Weighing Systems by Dong-Hyeon Kim, Jae-Woo Song, Hyunjin Cho, Mingyung Lee, Dae-Hyun Lee, Seongwon Seo, Wang-Hee Lee

“…However, owing to the high measurement variability caused by environmental factors, the accuracy of AWSs has been questioned. …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The approach assists in predicting key values of the dependent variable associated with defect occurrence, enabling foundries to enhance product quality, reduce waste, and augment overall production process efficiency. …”
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Sensing Technologies for Outdoor/Indoor Farming by Luwei Wang, Mengyao Xiao, Xinge Guo, Yanqin Yang, Zixuan Zhang, Chengkuo Lee

“…These technologies can detect factors such as plant water content, volatile organic compounds (VOCs), and hormones released by plants, as well as environmental conditions like humidity, temperature, wind speed, and light intensity. …”
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Predicting police and military violence: evidence from Colombia and Mexico using machine learning models by Juan David Gelvez

“…Such misconduct is not random, making its prevention both crucial and challenging due to the difficulty of measuring and detecting these phenomena beforehand. Recent advances in artificial intelligence offer new tools for this task. …”
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Changes of yeast, enzyme activity and metabolites of fermented grains of sauce-flavor Baijiu during stacking by HU Chunhong, LI Xintao, LU Jun, LIN Liangcai, ZHANG Cuiying, LI Changwen, YE Zhengliang, XIAO Dongguang

“…There were 95 common aroma components detected in the intelligent and traditional workshop of stacking fermented grains, including acetic acid, ethyl lactate, benzyl alcohol, etc. …”
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