Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach by Mehewish Musheer Sheikh, Mamatha Balachandra, Narendra V. G., Arun G. Maiya

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Multi-physiological signal fusion for objective emotion recognition in educational human–computer interaction by Wanmeng Wu, Enling Zuo, Weiya Zhang, Xiangjie Meng

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Trajectory Prediction and Intention Recognition Based on CNN-GRU by Jinghao Du, Dongdong Lu, Fei Li, Ke Liu, Xiaolan Qiu

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Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department by Arianna Scala, Teresa Angela Trunfio, Massimo Majolo, Michelangelo Chiacchio, Giuseppe Russo, Paolo Montuori, Giovanni Improta

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Failure State Identification and Fault Diagnosis Method of Vibrating Screen Bolt Under Multiple Excitation of Combine Harvester by Jiaojiao Xu, Tiantian Jing, Meng Fang, Pengcheng Li, Zhong Tang

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Design and implementation of classical literature sentiment analysis system based on ensemble learning and graph neural network by Qianru Gao, Jiachen Huang

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Digital Mapping of Soil Drainage Classes Using Multitemporal RADARSAT-1 and ASTER Images and Soil Survey Data by Mohamed Abou Niang, Michel Nolin, Monique Bernier, Isabelle Perron

“…Discriminant analysis classification (DAC) and decision tree classifiers (DTC) were used for digital mapping of soil drainage in the Bras-d’Henri watershed (QC, Canada) using earth observation data (RADARSAT-1 and ASTER) and soil survey dataset. …”
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Artificial intelligence-based prediction of second stage duration in labor: a multicenter retrospective cohort analysisResearch in context by Xiaoqing Huang, Xiaodan Di, Suiwen Lin, Minrong Yao, Suijin Zheng, Shuyi Liu, Wayan Lau, Zhixin Ye, Zilian Wang, Bin Liu

“…Since durations beyond 3 h were rare, we developed binary classification models with thresholds at 1 h and 2 h. …”
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A Full-Scale Shadow Detection Network Based on Multiple Attention Mechanisms for Remote-Sensing Images by Lei Zhang, Qing Zhang, Yu Wu, Yanfeng Zhang, Shan Xiang, Donghai Xie, Zeyu Wang

“…The model achieved an overall accuracy (OA) of 97.50%, an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula> score of 94.07%, an intersection over union (IOU) of 88.87%, a precision of 95.06%, and a BER of 4.05%, respectively. …”
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Influential drivers of the cost-effectiveness of respiratory syncytial virus vaccination in European older adults: a multi-country analysis by Xiao Li, Lander Willem, Caroline Klint Johannesen, Arantxa Urchueguía-Fornes, Toni Lehtonen, Richard Osei-Yeboah, Heini Salo, Alejandro Orrico-Sánchez, Javier Díez-Domingo, Mark Jit, for PROMISE investigators, Joke Bilcke, Harish Nair, Philippe Beutels

“…Methods A static multi-cohort model was parameterised using country- and age-specific hospitalisations using three approaches: (A) the International Classification of Diseases (ICD)-coded hospitalisations, (B) laboratory RSV-confirmed hospitalisations and (C) time-series modelling (TSM). …”
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Effects of land use change on ecosystem service value under different scenarios in the Guangdong-Hong Kong-Macao Greater Bay area by Xiaohui Ding, Ji Yang, Gen Zhang, Lingjia Liu, Quan Liu, Ruiyou Li, Jia Hu

“…This study estimated the 2030 ESVs of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), an area with severe ecological degradation, under four scenarios, including natural development (S1), urban development (S2), cultivated land protection (S3) and ecological protection (S4). First, the historical land use dataset was derived from the Landsat and Sentinel-2 images using the classification method of random forest. …”
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An illustration of multi-class roc analysis for predicting internet addiction among university students. by Nishat Tasnim Thity, Atikur Rahman, Adisha Dulmini, Mst Nilufar Yasmin, Rumana Rois

“…Predictions were made using different machine learning (ML) (decision tree (DT), random forest (RF), support vector machines (SVMs), and logistic regression (LR)) models. …”
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Feature recommendation strategy for graph convolutional network by Jisheng Qin, Xiaoqin Zeng, Shengli Wu, Yang Zou

“…Graph Convolutional Network (GCN) is a new method for extracting, learning, and inferencing graph data that builds an embedded representation of the target node by aggregating information from neighbouring nodes. GCN is decisive for node classification and link prediction tasks in recent research. …”
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Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm by ZHONG Hengyan, CHEN Chun, OUYANG Yongzhong, ZHOU Lin, GUO Weiqing

“…The accuracy of the training set and detection set reached 100% when the number of decision trees was 25. HS-APCI-MS combined with RF algorithm had a significant identification effect on TG, and the classification effect of RF was superior to that of PCA.ConclusionAtmospheric pressure chemical ionization mass spectrometry, combined with the RF algorithm, can rapidly and non-destructively identify TG and FTG, providing a new technical method for authenticating TG.…”
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Machine learning-based drought prediction using Palmer Drought Severity Index and TerraClimate data in Ethiopia. by Tadele Melese, Gizachew Assefa, Baye Terefe, Tatek Belay, Getachew Bayable, Abebe Senamew

“…This study investigates the classification of the Palmer Drought Severity Index (PDSI) using machine learning models trained on TerraClimate data, incorporating variables such as precipitation, temperature, soil moisture, and vapor pressure deficit. …”
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Rapid Point-of-Care Influenza Testing for Patients in German Emergency Rooms – A Cost-Benefit Analysis by Roland Diel, Albert Nienhaus

“… # Methods A deterministic decision-analytic model simulated the incremental costs of using the Sofia® Influenza A+B test compared to those of using clinical judgement alone to confirm or exclude influenza in adult ILI (influenza-like illness) patients in German ERs prior to hospitalization. …”
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A decentralised federated learning scheme for heterogeneous devices in cognitive IoT by Huanhuan Ge, Xingtao Yang, Jinlong Wang, Zhihan Lyu

“…Edge devices learned from the knowledge of multiple neighbours via knowledge transfer, and knowledge fusion was employed to aggregate the knowledge of multiple neighbours, thereby improving the performance of local models, and addressing data heterogeneity. Comprehensive experiments were performed on three image classification tasks. …”
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Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…This study constructed a XAI-based EBM model using global plasma metabolomics profiling to identify metabolites predictive of RA patients and to develop a classification model that can distinguish RA patients from healthy controls. …”
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Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study by Wen-Zhen Tang, Shu-Tian Mo, Yuan-Xi Xie, Tian-Fu Wei, Guo-Lian Chen, Yan-Juan Teng, Kui Jia

“…Information collected from the test set was applied to validate the model. The concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and a Kaplan-Meier survival curve were used to evaluate the accuracy and reliability of the model. …”
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Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media by Imran Shafi, Sadia Din, Zahid Hussain, Imran Ashraf, Gyu Sang Choi

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