Evaluating groundwater potential with the synergistic use of geospatial methods and advanced machine learning approaches by Vicky Anand, Vishnu D. Rajput, Tatiana Minkina, Saglara Mandzhieva, Aastha Sharma, Deepak Kumar, Sunil Kumar

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Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…The main characteristics were the size of the training and validation datasets, accuracy, sensitivity, specificity, AUROC (Area Under Receiver Operating Characteristic Curve). A number of studies investigate the comparative characteristics of algorithms. …”
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Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007–2018 by Efrain Riveros Perez, Bibiana Avella-Molano

“…The performance of five machine learning algorithms (logistic regression, support vector machine, random forest, XGBoost and CatBoost) was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC). …”
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Application of the Integral Energy Criterion and Neural Network Model for Helicopter Turboshaft Engines’ Vibration Characteristics Analysis by Serhii Vladov, Maryna Bulakh, Denys Baranovskyi, Eduard Kisiliuk, Victoria Vysotska, Maksym Romanov, Jan Czyżewski

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A study of measurement scenarios for the future CO2M mission: avoidance of detector saturation and the impact on XCO₂ retrievals by M. Weimer, M. Hilker, S. Noël, M. Reuter, M. Buchwitz, B. Fuentes Andrade, R. Lang, B. Sierk, Y. Meijer, H. Bovensmann, J. P. Burrows, H. Bösch

“…We use the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm, which has been selected to be one of the operational greenhouse gas retrieval algorithms to be implemented within the CO2M ground segment. …”
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An Efficient Analytical Method for Determining the Effects of Silver Nanoparticles on Vibrio parahaemolyticus in Estuarine Water by Xiangyi HOU, Xiaoyang WANG, Yuanyuan ZHANG, Ruohan LIANG, Feng LU, Qianqian YANG, Xiaodan PU, Yan ZHANG, Keming QU, Xuzhi ZHANG

“…Based on a 32-channel C4 detector and special algorithms, we developed a 32-channel electronic microbial growth analyzer (EMGA). …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

“…Six machine learning algorithms were selected and trained by our dataset. …”
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The Technical Development of a Prototype Lower-Limb Therapy Device for Bed-Resting Users by Juan Fang, Adrien Cerrito, Simón Gamero Schertenleib, Patrick von Raumer, Kai-Uwe Schmitt

“…The advancements in well-controlled movement, multi-modal training patterns, convenient operation, and intuitive feedback enable the compact therapy device to be a potential system for bed-resting users to improve physical activity and cognitive functionality.…”
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Machine Learning–Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study by Hye-Jin Kim, Heeji Choi, Hyo-Jung Ahn, Seung-Ho Shin, Chulho Kim, Sang-Hwa Lee, Jong-Hee Sohn, Jae-Jun Lee

“…After PSM, the high ASCVD risk group had higher alcohol or tobacco use, lower omega-3 intake, higher BMI, less physical activity, and spent less time sitting. In 5 ML models, the extreme gradient boosting model showed the highest area under the receiver operating characteristics curve, indicating superior overall discrimination between high and low ASCVD risk groups. …”
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Integrative bioinformatics analysis of pyroptosis-related genes and immune infiltration patterns in childhood asthma by Di Lian, Chenye Lin, Meiling Xie, JianXing Wei, Xueling Huang, Ke Lian, Qiuyu Tang

“…Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that PRDEGs were primarily enriched in biological processes related to the immune response, cell disassembly, and inflammatory pathways.ResultsImmune cell infiltration analysis using the CIBERSORT algorithm revealed significant differences between the CA and control groups, with increased macrophages M0, activated mast cells, and γδ T cells and decreased resting natural killer cells in the CA group. …”
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Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning by Supasuta Busayakanon, Morakot Kaewthamasorn, Natchapon Pinetsuksai, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang, Veerayuth Kittichai

“…The input data were subjected to SSL model training using the Bootstrap Your Own Latent (BYOL) algorithm with Residual Network 50 (ResNet50), ResNet101, and ResNet152 as the backbones. …”
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Bioinformatics-based screening and validation of PANoptosis-related biomarkers in periodontitis by Qing Sun, Qing Sun, JinYue Hu, JinYue Hu, RuYue Wang, RuYue Wang, ShuiXiang Guo, ShuiXiang Guo, GeGe Zhang, GeGe Zhang, Ao Lu, Ao Lu, Xue Yang, Xue Yang, LiNa Wang, LiNa Wang

“…Functional enrichment analysis was performed for these key genes. Machine learning algorithms were then applied to screen for potential biomarkers. …”
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Control over biopower in cognitive and surveillance capitalism by Stanković-Pejnović Vesna

“…Digital networks do not only collect data on users, but they "cluster" these users with the help of algorithms and encourage specific desired behaviors. …”
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Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain by Jay Gopal, Jonathan Bao, Tessa Harland, Julie G. Pilitsis, Steven Paniccioli, Rachael Grey, Michael Briotte, Kevin McCarthy, Ilknur Telkes

“…Alpha-theta peak power ratio differed significantly between CP3-CP4 and T3-T4 (p = 0.019), with the lowest activity in CP3-CP4 during tonic stimulation. The decision tree model performed best, achieving 88.2% accuracy, an F1 score of 0.857, and an area under the curve (AUC) of the receiver operating characteristic (ROC)  of 0.879. …”
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Understanding discrepancies in soil moisture from SMAP and AMSR2: insights into performance and dry-down behavior by Zhiqing Peng, Tianjie Zhao, Jiancheng Shi, Lu Hu, Thomas J. Jackson, Michael H. Cosh, Hui Lu, Xiaodong Gao, Jingyao Zheng, Panpan Yao, Qian Cui, Peng Guo, Peilin Song, Zushuai Wei, Mengjia Wang, Anmin Fu

“…This study addresses these issues by producing two new enhanced-resolution (approximately 10-km) SM datasets using the same multi-channel collaborative algorithm (MCCA). These datasets are retrieved from the L-band brightness temperature (Tb) from Soil Moisture Active Passive (SMAP) and the C/X/Ku-band Tb from Advanced Microwave Scanning Radiometer 2 (AMSR2), referred to as MCCA SMAP and MCCA AMSR2. …”
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Online Format of Extracurricular Education: a New Reality of Project-Based Education for Senior High School Students by N. G. Davydova, A. N. Kosarikov, D. M. Kirillov, A. V. Igumnov

“…The transferring of traditional public events of the final into a remote format was carried out using a set of organizational measures and information technologies according to the following algorithm: the choice of technologies for videoconference and broadcasting, work with the website, training and testing of the technical capabilities of participants, work on synchronization, support and assistance to participants with a final questionnaire.Results. …”
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School-Based Online Surveillance of Youth: Systematic Search and Content Analysis of Surveillance Company Websites by Alison O'Daffer, Wendy Liu, Cinnamon S Bloss

“…ObjectiveThe two goals of this study were to (1) comprehensively identify school-based online surveillance companies currently in operation, and (2) collate and analyze company-described surveillance services, monitoring processes, and features provided. …”
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Recent advances in anion exchange membrane technology for water electrolysis: a review of progress and challenges by Abdul Qayoom, Muhammad Shakeel Ahmad, H. Fayaz, Atika Qazi, Jeyraj Selvaraj, Rahadian Zainul, Krismadinata, Nasrudin Abd Rahim, Farruh Atamurotov, Thien Khanh Tran, Basma Souayeh, Natei Ermias Benti

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Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis by Shifen Wang, Hong Tao, Xingyun Zhao, Siwen Wu, Chunwei Yang, Yuanfei Shi, Zhenshu Xu, Dawei Cui

“…Moreover, four hub genes (CXCL9, CCL18, C1QA and CTSC) were significantly screened from the three datasets using RF algorithms. They were closely correlated with the overall survival of DLBCL patients. …”
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Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD by Miao-Lin Lei, Guan-Wei Bi, Xiao-Lin Yin, Xiao-Lin Yin, Yue Wang, Yue Wang, Yue Wang, Zi-Ru Sun, Zi-Ru Sun, Zi-Ru Sun, Xin-rui Guo, Hui-peng Zhang, Xiao-han Zhao, Feng Li, Feng Li, Yan-Bo Yu

“…Diagnostic models were constructed using six machine learning algorithms, and their performance was evaluated by cross-validated area under the receiver operating characteristic curve (AUC). …”
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