The good and the bad: using C reactive protein to distinguish bacterial from non-bacterial infection among febrile patients in low-resource settings by Camille Escadafal, Sabine Dittrich, Sandra Incardona, B Leticia Fernandez-Carballo

“…CRP testing may be best used as part of a panel of diagnostic tests and algorithms. Further studies in low-resource settings, particularly with regard to impact on antibiotic prescribing and cost-effectiveness of CRP testing, are warranted.…”
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False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions by Maxim Edelson, Anh Pham, Tsung-Ting Kuo

“…Abstract As collaborative Machine Learning on cross-institutional, fully distributed networks become an important tool in predictive health modeling, its inherent security risks must be addressed. …”
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Shipyard Manpower Digital Recruitment: A Data-Driven Approach for Norwegian Stakeholders by Bogdan Florian Socoliuc, Andrei Alexandru Suciu, Mădălina Ecaterina Popescu, Doru Alexandru Plesea, Florin Nicolae

“…The application of machine learning algorithms provides predictive insights that support real-time adjustments to job postings, optimizing recruitment strategies. …”
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An integrated framework for satellite-based flood mapping and socioeconomic risk analysis: A case of Thailand by Nutchapon Prasertsoong, Nattapong Puttanapong

“…The study also merges these satellite-based indices with official provincial GDP data from 2018 to 2022 to empirically analyze socioeconomic impacts using four machine learning algorithms. The result obtained from Random Forest (RF) demonstrates the highest predictive power for GDP forecasting (r-squared value of 0.912). …”
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Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead by Haytham Elmousalami, Aljawharah A. Alnaser, Felix Kin Peng Hui

“…A multi-horizon prediction approach is proposed, using WSF model outputs as inputs for WPF modeling. …”
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A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning by Gbenga Lawrence Alawode, Pere Joan Gelabert, Marcos Rodrigues

“…Using machine learning algorithms, geospatial data, and 124 historical wildfire perimeters from 2000 to 2015, we developed a predictive model with high accuracy (Area Under the Receiver Operating Characteristics Curve = 0.81 ± 0.03) over 32,108 km2 at a 30-meter resolution. …”
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Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models by Martín Pérez Salomón, Sanchez Jimenez Flora, Fuentes Cantero Sandra, Jímenez Barragan Marta, Sanchez Mora Catalina, Borreguero Leon Juan M., Teresa Arrobas Velilla, Valido Morales Agustín, Delgado Torralbo Juan A., León Justel Antonio

“…The objective of this study was to optimize predictive models for critical COVID-19 disease. Clinical data, laboratory data and genetic polymorphisms were integrated into AI models to compare the performance of different machine learning algorithms.…”
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The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations—a narrative review by Prasanthi Samathoti, Rajasekhar Komarla Kumarachari, Sarad Pawar Naik Bukke, Eashwar Sai Komarla Rajasekhar, Ashish Ashokkumar Jaiswal, Zohre Eftekhari

“…Nanomedicine leverages nanoparticles for targeted drug delivery, enhancing therapeutic efficacy while minimizing adverse effects. Concurrently, AI algorithms facilitate early cancer detection, personalized treatment planning, and predictive analytics, thereby optimizing clinical outcomes. …”
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Use of Data Mining for Intelligent Evaluation of Imputation Methods by David Red, Carlos R. Primorac

“…Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance. …”
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Optimizing Scheduled Virtual Machine Requests Placement in Cloud Environments: A Tabu Search Approach by Mohamed Koubàa, Abdullah S. Karar, Faouzi Bahloul

“…To leverage this opportunity, we propose an advanced VM placement algorithm designed to maximize the number of hosted SVMs in cloud data centers. …”
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The Machine Learning-Based Task Automation Framework for Human Resource Management in MNC Companies by Suchitra Deviprasad, N. Madhumithaa, I. Walter Vikas, Archana Yadav, Geetha Manoharan

“…MNCs are now beginning to use ML algorithms in combination with Artificial Intelligence (AI) to streamline the HR processes. …”
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Explainable data mining model for hyperinsulinemia diagnostics by Nevena Rankovic, Dragica Rankovic, Mirjana Ivanovic, Igor Lukic

“…Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. …”
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Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation by Siqi Liao, Wanting Sun, Haoran Zheng, Qiyang Xu

“…The experimental results demonstrate that the Random Forest model exhibits superior predictive accuracy to traditional decision tree methods, and an R² of 0.9728 can be achieved for phenol concentration prediction. …”
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An advanced structural health monitoring IoT platform for offshore wind turbine blades by Zhou Xingguo, Tian Yankang, Qin Yi, Charitidis Costas A., Milickovic Tanja K., Termine Stefania

“…This research focuses on developing a comprehensive, real-time monitoring system that utilises advanced sensor networks and edge computing, empowering advanced predictive algorithms to strengthen in-time maintenance of turbine blades, improving operational efficiency and reducing maintenance cost.…”
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Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica by Roberto Vargas-Masís, Diego Quesada

“…Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. …”
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Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression M... by Ru Yu, Xiaoli Wang, Xiaojun Xu, Zhiwen Zhang

“…Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. …”
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Construction and Comprehensive Prognostic Analysis of a Novel Immune-Related lncRNA Signature and Immune Landscape in Gastric Cancer by Xiaolong Liang, Lang Zha, Gangfeng Yu, Xiong Guo, Chuan Qin, Anqi Cheng, Ziwei Wang

“…Therefore, effective predictive biomarkers are urgently needed for GC patients to realize the benefits of immunotherapy. …”
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Analytical framework for household energy management: integrated photovoltaic generation and load forecasting mechanisms by Zhenping Xie, Yansha Li

“…The KNN-GA-MBP algorithm demonstrates the best prediction performance among the three algorithms, with an RMSE of only 0.39 kW, this represents a 43.37% improvement in RMSE over the KNN-MBP algorithm and a 71.89% improvement over the MBP algorithm.…”
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Forecasting the volatility of Financial Time Series by Tree Ensembles by O. S. Vidmant

“…More stringent liquidity and transparency standards in relation to the financial sector also encourage participants to experiment with protective mechanisms and to create predictive algorithms that can not only reduce the losses from the volatility of financial instruments but also benefit from short-term investment manipulations. …”
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Towards a rigorous assessment of systems biology models: the DREAM3 challenges. by Robert J Prill, Daniel Marbach, Julio Saez-Rodriguez, Peter K Sorger, Leonidas G Alexopoulos, Xiaowei Xue, Neil D Clarke, Gregoire Altan-Bonnet, Gustavo Stolovitzky

“…Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.…”
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