Cardiac Computed Tomography Measurements in Pulmonary Embolism Associated with Clinical Deterioration by Anthony J. Weekes, Angela M. Pikus, Parker L. Hambright, Kelly L. Goonan, Nathaniel O’Connell

“…Introduction: Most pulmonary embolism response teams (PERT) use a radiologist-determined right ventricle to left ventricle ratio (RV:LV) cut-off of 1.0 to risk-stratify pulmonary embolism (PE) patients. …”
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GPU Assisted Brute Force Cryptanalysis of GPRS, GSM, RFID, and TETRA by Cihangir Tezcan, Gregor Leander

“…In this work we provide optimized implementations of several widely used algorithms on GPUs, leading to interesting insights on the cost of brute force attacks on several real-word applications. …”
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Automated Body Condition Scoring in Dairy Cows Using 2D Imaging and Deep Learning by Reagan Lewis, Teun Kostermans, Jan Wilhelm Brovold, Talha Laique, Marko Ocepek

“…The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. …”
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Machine learning enables legal risk assessment in internet healthcare using HIPAA data by Shixian Liu, Hailing Liu, Siyu Fan, Leming Song, Zeyu Wang

“…DNN demonstrates strong capabilities in handling complex nonlinear relationships, and XGBoost further improves classification accuracy by optimizing decision tree models through gradient boosting. …”
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Research on Measurement of Coal–Water Slurry Solid–Liquid Two-Phase Flow Based on a Coriolis Flow Meter and a Neural Network by Jie Liu, Lingfei Kong, Jiahao Ma, Xuemei Zhang, Chengjie Wang, Dongze Wu

“…The first correction results showed that the corrected error of the predictive model was 3.98%, a significant improvement compared to the 5.11% error measured by the X company’s meter. (2) Building on this, a second correction model was established through algorithm optimization, successfully reducing the corrected error of the predictive model to 1.01%. …”
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Remote Sensing Target Tracking Method Based on Super-Resolution Reconstruction and Hybrid Networks by Hongqing Wan, Sha Xu, Yali Yang, Yongfang Li

“…And obtaining high-resolution images by optimizing algorithms will save a lot of costs. Aiming at the problem of large tracking errors in remote sensing target tracking by current tracking algorithms, this paper proposes a target tracking method combined with a super-resolution hybrid network. …”
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YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…Against the backdrop of the deep integration of national fitness and sports science, this study addresses the lack of standardized movement assessment in yoga training by proposing an intelligent analysis system that integrates an improved YOLOv11-ECA detector with the DeepSORT tracking algorithm. …”
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Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations by Yuchao Miao, Jiwei Li, Ruigang Ge, Chuanbin Xie, Yaoying Liu, Gaolong Zhang, Mingchang Miao, Shouping Xu

“…Abstract Background Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. …”
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Deep Reinforcement Learning Based Transferable EMS for Hybrid Electric Trains by Yogesh Wankhede, Sheetal Rana, Faruk Kazi

“…To enhance the performance of the EMS, proposes to use of a deep reinforcement learning (DRL) algorithm specifically the deep deterministic policy gradient (DDPG) combined with transfer learning (TL) which can improve the system's efficiency when driving cycles are changed. …”
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Initialization Methods for FPGA-Based EMT Simulations by Xin Ma, Xiao-Ping Zhang

“…The performance of these four methods are also compared, and Method 4 can initialize instantly with the simplest code. To improve hardware adaptability, optimized strategies are developed for address sequence, interface, update modes and dataflow. …”
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Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction by Ahya Radiatul Kamila, Johanes Fernandes Andry, Francka Sakti Lee, Felliks F. Tampinongkol

“…It is part of the preprocessing stage, aiming to reduce memory usage, speed up data preprocessing, and improve model performance. After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. …”
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On Flood Detection Using Dual-Polarimetric SAR Observation by Su-Young Kim, Yeji Lee, Sang-Eun Park

“…This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. …”
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Leveraging artificial intelligence to strengthen surgical systems in sub-Saharan Africa by Osedebamen Ralph-Okhiria, Ikhide Alonge

“…However, the review highlighted several crucial challenges and concerns, including data availability and quality, infrastructure gaps, ethical implications (such as data protection and algorithmic bias), costs and affordability, and the need for robust regulatory frameworks. …”
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Deep Q-Networks for Minimizing Total Tardiness on a Single Machine by Kuan Wei Huang, Bertrand M. T. Lin

“…Due to its computational intractability, exact approaches such as dynamic programming algorithms and branch-and-bound algorithms struggle to produce optimal solutions for large-scale instances in a reasonable time. …”
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Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis by Ida Mohammadi, Setayesh Farahani, Asal Karimi, Saina Jahanian, Shahryar Rajai Firouzabadi, Mohammadreza Alinejadfard, Alireza Fatemi, Bardia Hajikarimloo, Mohammadhosein Akhlaghpasand

“…IntroductionMachine learning (ML) models have been increasingly applied to predict post-heart transplantation (HT) mortality, aiming to improve decision-making and optimize outcomes. This systematic review and meta-analysis evaluates the performance of ML algorithms in predicting mortality and explores factors contributing to model accuracy.MethodA systematic search of PubMed, Scopus, Web of Science, and Embase identified relevant studies, with 17 studies included in the review and 12 in the meta-analysis. …”
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Static Voltage Stability Evaluation and Correction Strategy Based on Stage Solving Strategy and SIFT by Yan Wen, Xiaoming Liu, Yongzhi Zhou, Huiwen Zhang, Donglei Sun

“…Aiming at the static voltage instability problem of the power grid caused by large-scale access of renewable energy sources, the study adopts the scale invariant feature transform algorithm for boundary feature extraction, while optimizing the traditional generalized resilience index of the power grid. …”
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Approach to knowledge management and the development of a multi-agent knowledge representation and processing system by E. I. Zaytsev, E. V. Nurmatova

“…Methods for distribution of intelligent software agents on the MKRPS nodes are proposed along with algorithms for optimizing the logical structure of the distributed knowledge base (DKB) to improve the performance of the MKRPS in terms of volume, cost and time criteria.Conclusions. …”
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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

“…Results The combined LASSO and Boruta algorithms identified nine optimal predictors, with the union feature set achieving superior performance. …”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

“…Maternal risk analysis can improve prenatal care, improve mother and baby health, and optimize healthcare resources by identifying misclassified observations using machine learning algorithms such as LDA, QDA, KNN, Decision Tree, Random Forest, Bagging, and Support Vector Machine, all of which have a significant impact on maternity health risk assessment. …”
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Large Language Model–Assisted Risk-of-Bias Assessment in Randomized Controlled Trials Using the Revised Risk-of-Bias Tool: Usability Study by Jiajie Huang, Honghao Lai, Weilong Zhao, Danni Xia, Chunyang Bai, Mingyao Sun, Jianing Liu, Jiayi Liu, Bei Pan, Jinhui Tian, Long Ge

“…When domain judgments were derived from LLM-generated signaling questions using the RoB2 algorithm rather than direct LLM domain judgments, accuracy improved substantially for Domain 2 (adhering; 55-95) and overall (adhering; 70-90). …”
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