Machine-learning models for differentiating benign and malignant breast masses: Integrating automated breast volume scanning intra-tumoral, peri-tumoral features, and clinical info... by Meixue Dai, Yueqiong Yan, Zhong Li, Jidong Xiao

“…Combining radiomics with clinical features further enhanced predictive performance. The LGBM model outperformed the other algorithms across subgroups, achieving a maximum AUC of 0.909, an accuracy of 0.878, and an F1-score of 0.971. …”
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Automatic Tuning of Extended Kalman Filter in Sensorless Synchronous Reluctance Motor Drives by Saverio Rigon, Benedikt Haus, Paolo Mercorelli, Mauro Zigliotto

“…A substantial reduction in the human environmental footprint can be achieved through the use of more efficient motors, such as synchronous reluctance motors (SynRM). …”
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Technical note: Evolution of convective boundary layer height estimated by Ka-band continuous millimeter wave radar at Wuhan in central China by Z. Zhang, Z. Zhang, Z. Zhang, K. Huang, K. Huang, K. Huang, F. Yi, F. Yi, F. Yi, W. Cheng, F. Liu, F. Liu, F. Liu, J. Zhang, Y. Jia

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Psychometric properties of the German version of the Traumatic Grief Inventory-Self Report Plus (TGI-SR+) by Julia Treml, Viktoria Schmidt, Elmar Braehler, Matthias Morfeld, Anette Kersting

“…Despite the same name, both versions of PGD differ in symptom count, content, and diagnostic algorithm. A single instrument to screen for both PGD diagnoses is critical for bereavement research and care. …”
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Enhanced EKF and SVSF for state of charge estimation of Li‐ion battery in electric vehicle using a fuzzy parameters model by Meriem Ben Lazreg, Sabeur Jemmali, Bilal Manai, Mahmoud Hamouda

“…The results show that the maximum root mean square error (RMSE) of the estimated SoC is kept within 1.51% with the FP‐EKF and 0.68% with the FP‐SVSF. Moreover, the reduction of the maximum absolute error may reach 0.34% with the FP‐EKF, and 0.82% with the FP‐SVSF, compared to the same algorithms without the proposed FP method. …”
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Target repositioning using multi-layer networks and machine learning: The case of prostate cancer by Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Mickaël Leclercq, Julien Prunier, Olivier Périn, Arnaud Droit

“…Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.…”
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On the role of diagnosis of prostate diseases at the outpatient appointment of a therapist by A. L. Kebina, A. S. Sycheva, A. L. Vertkin, G. Yu. Knorring

“…Identification of symptoms suspicious of malignant neoplasms using algorithmic approaches and subsequent consultation with the patient by a specialist urologist or oncologist should improve treatment outcomes.…”
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DRFW-TQC: Reinforcement Learning for Robotic Strawberry Picking with Dynamic Regularization and Feature Weighting by Anping Zheng, Zirui Fang, Zixuan Li, Hao Dong, Ke Li

“…Strawberry harvesting represents a labor-intensive agricultural operation where existing end-effector pose control algorithms frequently exhibit insufficient precision in fruit grasping, often resulting in unintended damage to target fruits. …”
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UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness by Oleksandr Liubimov, Ihor Turkin, Valeriy Cheranovskiy, Lina Volobuieva

“…The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. …”
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HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation by Simon Schlumbohm, Julia E. Neumann, Philipp Neumann

“…An additionally welcome update regarding improved feature rescue furthermore enhances the algorithms ability to quickly and robustly perform batch effect reduction.…”
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Advanced 5G Channel Estimation in mmWave MIMO Systems: Leveraging Compressive Sensing for Enhanced Performance by Zaid Albataineh, Mohammad Al Bataineh, Khaled Farouq Hayajneh, Raed Al Athamneh

“…The proposed approach combines compressive sampling matching pursuit (CoSaMP) and sparsity adaptive matching pursuit (SAMP) algorithms, augmented by a novel iterative reweighting strategy and adaptive thresholding mechanism. …”
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Elderly travel mode choice in Thailand-evaluating MNL and machine learning models by Anantaya Philuek, Panuwat Wisutwattanasak, Fareeda Watcharamaisakul, Chinnakrit Banyong, Anon Chantaratang, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…Results indicate that the Random Forest algorithms achieved the highest predictive performance on the comprehensive dataset (99.83% accuracy), while CatBoost demonstrated excellent performance on test data (94%). …”
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Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO₂: A Data-Driven Approach by Man Fang, Yutong Yao, Chao Pang, Xiehang Chen, Yutao Wei, Fan Zhou, Xiaokun Zhang, Yong Xiang

“…Specifically, the RF and XGBoost models have the highest fitting performance for IC and EC prediction, with R² values of 0.8882 and 0.8318, respectively. …”
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Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon by Yibo Luo, Chunlin Li, Jinhong Huang, Chengcheng Dong, Junjie Wang

“…This study presents a novel framework integrating fractional-order derivative (FOD) techniques with machine learning algorithms for SOC estimation in mangrove wetlands. …”
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GIS Analysis Model Integration and Service Composition Prospects by L. Ding, P. Cai, W. Huang, H. Zhang, F. Ding, W. Zhao, D. Tang, Z. Wang

“…Key algorithms are systematically integrated to optimize outcomes in urban planning, disaster management, and precision agriculture. …”
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Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma by Maodong Ye, Shuai Ren, Huanjuan Luo, Xiumin Wu, Hongwei Lian, Xiangna Cai, Yingchang Ji

“…Methods GNNs models were developed to predict the response to immunotherapy and to pinpoint key pathways. …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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Integrating Metabolomics and Machine Learning to Analyze Chemical Markers and Ecological Regulatory Mechanisms of Geographical Differentiation in <i>Thesium chinense</i> Turcz by Cong Wang, Ke Che, Guanglei Zhang, Hao Yu, Junsong Wang

“…Random forest and LASSO regression algorithms determined core markers for each production area: Anhui (4 markers), Henan (6 markers), and Shanxi (3 markers). …”
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Integrative analysis of multi-omics data identified PLG as key gene related to Anoikis resistance and immune phenotypes in hepatocellular carcinoma by Xueyan Wang, Lei Gao, Haiyuan Li, Yanling Ma, Bofang Wang, Baohong Gu, Xuemei Li, Lin Xiang, Yuping Bai, Chenhui Ma, Hao Chen

“…This study provides novel insights into the molecular subtypes of HCC through the application of robust clustering algorithms based on multi-omics data. The constructed CMLS serves as a valuable tool for early prognostic prediction and for screening potential drug candidates that may enhance the efficacy of immunotherapy, thereby establishing a foundation for personalized treatment strategies in HCC. …”
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Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery by Linming Huang, Fen Zhao, Guozheng Hu, Hasbagan Ganjurjav, Rihan Wu, Qingzhu Gao

“…This study explored the effectiveness of common machine learning algorithms in predicting pasture yield of temperate grasslands utilizing Landsat-8 data and ground sample data and provided the valuable support for long-term historical monitoring of pasture resources. …”
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