What has changed in the attitude of health workers towards vaccination after the pandemic COVID-19 by S. M. Kharit, L. V. Romanov, Yu. E. Konstantinova, S. A. Bogdan

“…To improve the work on vaccination, 44.5% indicated the need for information on the official websites of the Ministry of Health of the Russian Federation, FMBA, Rospotrebnadzor and RoszdravnadzorConclusion The results of the survey indicate the need to modernize the system of training in vaccination of medical workers of various specialties, the development of new forms of information presentation, the use of official websites, the development of algorithms for communicating with the population on adherence to vaccination,…”
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Predicting Stroke-Associated Pneumonia in Acute Ischemic Stroke: A Machine Learning Model Development and Validation Study with CBC-Derived Inflammatory Indices by Xie M, Liu Z, Dai F, Cao Z, Wang X

“…Our aim was to identify SAP risk factors and develop a machine learning (ML) model for early risk stratification.Methods: This retrospective study analyzed 574 ischemic stroke patients, divided into training (75%) and testing (25%) sets. Nine ML models were trained using 10-fold cross-validation, with performance evaluated by accuracy, AUC-ROC, and F1-score. …”
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An explainable machine learning model in predicting vaginal birth after cesarean section by Ming Yang, Dajian Long, Yunxiu Li, Xiaozhu Liu, Zhi Bai, Zhongjun Li

“…Seven predicting models based on ML were developed and evaluated by area under the receiver operating characteristic (AUC) curve. …”
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Correction of CAMS PM₁₀ Reanalysis Improves AI-Based Dust Event Forecast by Ron Sarafian, Sagi Nathan, Dori Nissenbaum, Salman Khan, Yinon Rudich

“…Our bias-corrected PM₁₀ fields are, on average, 12 μg m⁻³ more accurate, often reducing CAMS errors by significant percentages. To evaluate the contribution, we train a deep neural network to predict city-scale dust events (0–72 h) over the Balkans using PM₁₀ fields. …”
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Integrative machine learning and molecular simulation approaches identify GSK3β inhibitors for neurodegenerative disease therapy by Hassan H. Alhassan

“…Among all models, the Random Forest (RF) algorithm had the best prediction accuracy, with a value of 0.6832 on the test set and 0.7432 on the training set, and was employed to screen the target library of 11,032 phytochemicals. …”
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A novel dataset and deep learning object detection benchmark for grapevine pest surveillance by Giorgio Checola, Paolo Sonego, Roberto Zorer, Valerio Mazzoni, Franca Ghidoni, Alberto Gelmetti, Pietro Franceschi

“…Assisted by entomologists, we performed the annotation process, trained, and compared the performance of two state-of-the-art object detection algorithms: YOLOv8 and Faster R-CNN. …”
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Safe opioid prescribing: a prognostic machine learning approach to predicting 30-day risk after an opioid dispensation in Alberta, Canada by Vishal Sharma, Dean T Eurich, Salim Samanani, Vinaykumar Kulkarni, Luke Kumar

“…Pregnant and cancer patients were excluded.Exposure Each opioid dispensation served as an exposure.Main outcomes/measures Opioid-related adverse outcomes were identified from linked administrative health data. Machine learning algorithms were trained using 2017 data to predict risk of hospitalisation, emergency department visit and mortality within 30 days of an opioid dispensation. …”
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Predicting ESWL success for ureteral stones: a radiomics-based machine learning approach by Ran Yang, Dan Zhao, Chunxue Ye, Ming Hu, Xiao Qi, Zhichao Li

“…Patients were randomly divided into a training set (n = 230) and a test set (n = 99) in a 7:3 ratio. …”
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Torg-Pavlov ratio qualification to diagnose developmental cervical spinal stenosis based on HRViT neural network by Yao Wu, Zhenxi Zhang, Jie Liang, Weiwen Wu, Weifei Wu

“…The accuracy of the TPR measurement was evaluated using mean absolute error (MAE), intra-class correlation coefficient (ICC), r value and Bland-Altman plot. …”
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Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…This study aims to improve DDoS detection accuracy by training a robust Machine Learning (ML) model using effective hyper-parameter tuning and Cross-Validation (CV) techniques. …”
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AI and Primary Care: Scoping Review by Gellert Katonai, Nora Arvai, Bertalan Mesko

“…However, persistent implementation barriers such as usability challenges, training gaps, and workflow integration issues must be addressed. …”
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Studying Alzheimer’s disease through an integrative serum metabolomic and lipoproteomic approach by Alessia Vignoli, Giovanni Bellomo, Federico Paolini Paoletti, Claudio Luchinat, Leonardo Tenori, Lucilla Parnetti

“…Results In the training set, our model classified AD-dem and MCI with an accuracy of 81.7%. …”
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Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization by Sumet Mehta, Fei Han, Muhammad Sohail, Bhekisipho Twala, Asad Ullah, Fasee Ullah, Arfat Ahmad Khan, Qinghua Ling

“…Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. …”
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Optimasi Klasifikasi Sentimen Komentar Pengguna Game Bergerak Menggunakan Svm, Grid Search Dan Kombinasi N-Gram by Syahroni Wahyu Iriananda, Renaldi Widi Budiawan, Aviv Yuniar Rahman, Istiadi Istiadi

“…Grid Search (GS) was utilized for hyperparameter optimization to achieve the highest possible accuracy. To evaluate the impact of these methods, experiments were conducted across various scenarios, including different data quantities, hyperparameter settings, training and testing dataset ratios, and N-Gram configurations. …”
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Assessing the Impact of Social Media Usage and Performance of the Selected Small and Medium Businesses in Ntungamo District. by Nyamwija, Bonitah

“…The study findings further assessed the challenges encountered when small business owner-managers in the Ntungamo district use social media in their business and these included time constraints, limited expertise, Negative feedback and online reputation management, and keeping up with algorithm changes. Based on the challenges, the study findings further examined the solutions to the challenges and these were; Monitoring social media channels regularly and responding promptly and professionally to customer feedback, whether positive or negative, Investing in social media training,g and Prioritizing social media management in the study area.…”
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A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models by Hamidreza Najafi, Kimia Savoji, Marzieh Mirzaeibonehkhater, Seyed Vahid Moravvej, Roohallah Alizadehsani, Siamak Pedrammehr

“…Our solution employs a GAN model trained with a reinforcement learning (RL)-based algorithm to mitigate this imbalance and enhance segmentation accuracy. …”
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Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model by Zeng Y, Lu H, Li S, Shi QZ, Liu L, Gong YQ, Yan P

“…The area under the receiver operating characteristic curve (AUC) was used to evaluate model’s performance, and then the TreeShap algorithm was employed to interpret the variable contributions.Results: A total of 184 children were enrolled in this study, of whom 19 (10.33%) developed ATB-DILI. …”
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Research and application of deep learning object detection methods for forest fire smoke recognition by Luhao He, Yongzhang Zhou, Lei Liu, Yuqing Zhang, Jianhua Ma

“…To improve the model’s applicability and generalizability, two publicly available fire image datasets, WD (Wildfire Dataset) and FFS (Forest Fire Smoke), encompassing various complex scenarios and external conditions, were employed. After 501 training epochs, the model’s detection performance was comprehensively evaluated via multiple metrics, including precision, recall, and mean average precision (mAP50 and mAP50-95). …”
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Scalable Hyperspectral Enhancement via Patch-Wise Sparse Residual Learning: Insights from Super-Resolved EnMAP Data by Parth Naik, Rupsa Chakraborty, Sam Thiele, Richard Gloaguen

“…In this contribution, we propose a novel parallel patch-wise sparse residual learning (P²SR) algorithm for resolution enhancement based on fusion of HSI and MSI. …”
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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenari... by Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari

“…Feature importance analysis demonstrated the value of the enhanced map composition, and applying the LandTrendr algorithm simplified the diachronic land use and land cover (LULC) classification and change analysis by supporting automatic training data collection. …”
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