A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data by M. Atzeni, G. Cappon, J. K. Quint, F. Kelly, B. Barratt, M. Vettoretti

“…To address this, we designed a machine learning (ML) framework that leverages data from personal air quality monitors, health records, lifestyle, and living condition information to build models that perform short-term prediction of COPD exacerbations. …”
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Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applicat... by Asefa Adimasu Taddese, Assefa Chekole Addis, Bjorn T. Tam

“…Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. …”
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Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI by Aymin Javed, Nadeem Javaid, Muhammad Hasnain, Umair Sarfraz, Imran Ahmed, Muhammad Shafiq, Jin-Ghoo Choi

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Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions by Lindsey N. Anderson, Charles Tapley Hoyt, Jeremy D. Zucker, Andrew D. McNaughton, Jeremy R. Teuton, Klas Karis, Natasha N. Arokium-Christian, Jackson T. Warley, Zachary R. Stromberg, Benjamin M. Gyori, Benjamin M. Gyori, Neeraj Kumar

“…Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. …”
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Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling by Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, Travis J. Esau, Alaba Boluwade, Rehan Sadiq

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Analysis of Gene Expression Omnibus high-throughput sequencing data for the determination of microribonucleic acids in the blood plasma of patients with glioblastomas by A. A. Pushkin, D. Yu. Gvaldin, N. N. Timoshkina, E. E. Rostorguev, L. Yu. Vladimirova, E. A. Dzenkova

“…Determination of significant miRNAs using machine learning algorithms of the R 4.0.4 project. For significant miRNAs, target genes have been performed, an analysis of the improvement of functional characteristics and interactome analysis of target genes of miRNA were performed.Results. …”
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Integrating data from unmanned aerial vehicles and Sentinel-2 with PROSAIL-5D-driven machine learning for fuel moisture content estimation in agroecosystems by Jinlong Liu, Jia Jin, Jing Huang, Mengjuan Wu, Shaozheng Hao, Haoyi Jia, Tengda Qin, Yuqing Huang, Dan Chen, Nathsuda Pumijumnong

“…This study presents an advanced framework integrating multi-source remote sensing data fusion, physically based modeling, and machine learning to enable high-resolution and high-precision FMC estimation. …”
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Educational Effectiveness of Using Big Data Based and Its Evaluation with Cluster Analysis and Qualification Framework in Financial Services and Management by Yujie Jiao, Ruiting Zhang, Ying Zhu

“…We evaluated and predicted the quality of financial services and professional management using cluster analysis. Using K-prototype clustering analysis and TF-IDF word frequency methods, the differences in different evaluations of job positions and vocational skill requirements of college graduates were analyzed. …”
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Identification of Key Genes Related to Intramuscular Fat Content of Psoas Major Muscle in Saba Pigs by Integrating Bioinformatics and Machine Learning Based on Transcriptome Data by Zixia Huang, Yongli Yang, Jinhua Lai, Qiang Chen, Xiaoyi Wang, Shuyan Wang, Mingli Li, Shaoxiong Lu

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Overcoming diagnostic and data privacy challenges in viral disease detection: an integrated approach using generative AI, vision transformers, explainable AI, and federated learnin... by Asadi Srinivasulu, Anupam Agrawal, Ramchand Vedaiyan

“…This research tackles these issues by introducing an integrated framework that combines Generative AI, Vision Transformers, Explainable AI (XAI), and Federated Learning (FL) to improve diagnostic accuracy and safeguard data privacy. …”
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Analyzing Instructional Strategies in Science Education: A Dual Approach Combining Systematic Review and Secondary Qualitative Data Analysis by Şeyma Irmak, Duygu Yılmaz Ergül

“…This study employed a dual research approach, incorporating both systematic review and secondary qualitative data analysis, to investigate instructional strategies and their rationales utilized by pre-service and in-service science teachers (PaIST) in physics topics. …”
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Unmasking Machine Learning With Tensor Decomposition: An Illustrative Example for Media and Communication Researchers by Yu Won Oh, Chong Hyun Park

“…As online communication data continues to grow, manual content analysis, which is frequently employed in media studies within the social sciences, faces challenges in terms of scalability, efficiency, and coding scope. …”
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Enhanced recurrent attention-deep Q learning with optimal node constrains and effective penalty based model for data transmission scheduling on wireless sensor networks by D.R. Anita Sofia Liz, Yesubai Rubavathi C

“…In order to improve the effectiveness of data transmission scheduling in wireless sensor networks (WSNs), this paper proposes a unique method called Recurrent Attention-Deep Q Learning with Optimal Node Constraints and Effective Penalty based WSN Scheduling (RA-DQL-ONC&EP). …”
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Enhanced data-driven shear strength predictive modeling framework for RCDBs using explainable boosting-based ensemble learning algorithms coupled with Bayesian optimization by Imad Shakir Abbood, Noorhazlinda Abd Rahman, B.H. Abu Bakar

“…Nowadays, there is a boom in implementing machine learning (ML) approaches in solving various structural engineering problems. …”
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Mapping drought severity impact on arboriculture systems over Tadla and lower Tassaout plains in Morocco using Sentinel-2 data and machine learning approaches by Sabir Oussaoui, Abdelghani Boudhar, Abdessamad Hadri, Youssef Lebrini, Ismaguil Hanadé Houmma, Ismail Karaoui, El Mahdi El Khalki, Jamal-eddine Ouzemou, Christophe Kinnard

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Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong by Chun Sing Lam, Rong Hua, Herbert Ho-Fung Loong, Chun-Kit Ngan, Yin Ting Cheung

“…MethodsThis study used data from the National Health and Nutrition Examination Survey (NHANES) and the Hospital Authority Data Collaboration Laboratory (HADCL). …”
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Automatic analysis of three-dimensional cardiac tagged magnetic resonance images using neural networks trained on synthetic data by Stefano Buoso, Christian T. Stoeck, Sebastian Kozerke

“…We present a deep learning approach specifically designed for displacement analysis of 3D cardiac tagged MR images. …”
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Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis by Alessandra Cartocci, Alessio Luschi, Linda Tognetti, Elisa Cinotti, Francesca Farnetani, Aimilios Lallas, John Paoli, Caterina Longo, Elvira Moscarella, Danica Tiodorovic, Ignazio Stanganelli, Mariano Suppa, Emi Dika, Iris Zalaudek, Maria Antonietta Pizzichetta, Jean Luc Perrot, Gabriele Cevenini, Ernesto Iadanza, Giovanni Rubegni, Harald Kittler, Philipp Tschandl, Pietro Rubegni

“…This work aimed to evaluate and compare the effectiveness of machine learning (logistic regression of lesion features and patient metadata) and deep learning (CNN analysis of images) models in dermoscopy diagnosis and the management of aPFLs. …”
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Groundwater–CO₂ emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data by L. M. van der Poel, L. V. Bataille, B. Kruijt, W. Franssen, W. Jans, J. Biermann, A. Rietman, A. J. V. Buzacott, Y. van der Velde, R. Boelens, R. W. A. Hutjes

“…Using spatiotemporal data, we train and optimize a boosted regression tree (BRT) machine learning algorithm to predict immediate CO<span class="inline-formula">₂</span> fluxes and use Shapley values and various simulations to interpret the model's outputs. …”
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Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method by Chao Ren, Han Yu, Yan Xu, Zhao Yang Dong

“…Theoretical analysis shows RTL can guarantee system performance.…”
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