A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change by Julia Forster, Stefan Bindreiter, Birthe Uhlhorn, Verena Radinger-Peer, Alexandra Jiricka-Pürrer

“…ML can predict future scenarios beyond rigid linear models, identifying patterns, trends, and correlations within complex systems and depicting hidden relationships. …”
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Exploring IRGs as a Biomarker of Pulmonary Hypertension Using Multiple Machine Learning Algorithms by Jiashu Yang, Siyu Chen, Ke Chen, Junyi Wu, Hui Yuan

“…An animal model of PAH was also established to validate hub gene expression patterns. Results: Among the 113 machine learning algorithms, the Lasso + LDA model achieved the highest AUC of 0.741. …”
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Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease. by Julia Ledien, Zulma M Cucunubá, Gabriel Parra-Henao, Eliana Rodríguez-Monguí, Andrew P Dobson, Susana B Adamo, María-Gloria Basáñez, Pierre Nouvellet

“…Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.…”
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Decoding the footsteps of the African savanna: Classifying wildlife using seismic signals and machine learning by René Steinmann, Tarje Nissen‐Meyer, Fabrice Cotton, Frederik Tilmann, Beth Mortimer

“…To address the issue of the site effect, we trained machine learning models on data recorded on various sites. …”
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Toward a physics-guided machine learning approach for predicting chaotic systems dynamics by Liu Feng, Yang Liu, Benyun Shi, Jiming Liu

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Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior by Bashiri Iddy Muzzo, Kelvyn Bladen, Andres Perea, Shelemia Nyamuryekung’e, Juan J. Villalba

“…This study classified cows’ foraging behaviors using machine learning (ML) models evaluated through random test split (RTS) and cross-validation (CV) data partition methods. …”
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Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis by Chao Tan, Hao Liu, Zhen Zhang, Xinyu Liu, Yinquan Ai, Xiumin Wu, Enlin Jian, Yongyan Song, Jin Yang

“…ObjectiveDespite growing interest in the application of machine learning (ML) in proteomics, a comprehensive and systematic mapping of this research domain has been lacking. …”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…These models closely mirrored actual prescribing patterns, suggesting feasibility for clinical integration. …”
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Single-cell sequencing combined with machine learning to identify glioma biomarkers and therapeutic targets by Yu Yan, Zhengmin Chu, Qi Zhong, Genghuan Wang

“…Further analyses examined immune infiltration patterns and functional pathways. Importantly, we analyzed the relationship between prognostic-related genes and ubiquitination, and further characterized the characteristics of ubiquitination-related prognostic genes. …”
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Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus by Marco Tjakra, Kristína Lidayová, Christophe Avenel, Christel A.S. Bergström, Shakhawath Hossain

“…This study presents a machine-learning-driven framework that integrates microrheological features into diffusional fingerprinting to characterize nano- and micro-scale particle diffusion patterns in mucus and assess the effect of mucus microrheology on such movements. …”
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Advancing sustainable mobility in India with electric vehicles: market trends and machine learning insights by Ezhilmaran Devarasan, Deepikaa Nagarajan, Jenisha Rachel

“…Objectives include analysing sales trajectories over the past decade, estimating EV sales across states, exploring category-specific trends, identifying drivers of regional disparities, investigating EV adoption patterns in the Tamil Nadu, evaluating the advantages and disadvantages of EVs. …”
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Machine Learning Approaches to Predict No-Shows in Saudi Arabian Primary and General Healthcare Settings by Abdulrahman Alshehri, Abdullah Saeed, Abdullah AlShafea, Sabah Althubiany, Mohammed Alshehri, Amer Alzahrani, Khalid Hakami, Lamia Ibrahim, Abdulrahim Alshehri, Rana Alamri

“…Regular appointments and PHCCs showed different attendance patterns than hospitals, while walk-in appointments did. …”
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Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring by Sehat Ullah, Sangeen Khan, David Vanecek, Inam Ur Rehman

“…Furthermore, health carers cannot forecast abnormalities based on health data. Machine Learning (ML) can analyze massive amounts of data and perceive patterns to anticipate anomalous health conditions. …”
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Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

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Machine heuristic in algorithm aversion: Perceived creativity and effort of output created by or with artificial intelligence by Sigurd Birk Heimstad, Anders Hauge Wien, Tarje Gaustad

“…Our main theoretical contribution is the identification of the machine heuristic (MH), individuals' preexisting beliefs about AI capabilities, as a key moderator. …”
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An evolution of forensic linguistics: From manual analysis to machine learning – A narrative review by R. Thamizh Mani, Vikram Palimar, Mamatha Shivananda Pai, T.S. Shwetha, M. Nirmal Krishnan

“…Forensic linguistics has evolved from manual textual analysis to machine learning (ML)-driven methodologies, fundamentally transforming its role in criminal investigations. …”
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Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices by Gavriela Asiminari, Lefteris Benos, Dimitrios Kateris, Patrizia Busato, Charisios Achillas, Claus Grøn Sørensen, Simon Pearson, Dionysis Bochtis

“…This study aimed to simplify field efficiency estimation by training machine learning regression algorithms on data generated from a farm management information system covering a combination of different field areas and shapes, working patterns, and machine-related parameters. …”
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Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma by Xie J, Zhang M, Qi M

“…Key triaptosis-related genes and pathways were identified and incorporated into machine learning models to construct a prognostic signature. …”
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Predicting high confidence ctDNA somatic variants with ensemble machine learning models by Rugare Maruzani, Liam Brierley, Andrea Jorgensen, Anna Fowler

“…Rule-based variant filtering methods either remove a substantial number of true positive ctDNA variants along with false variant calls or retain an implausibly large number of total variants. Machine Learning (ML) enables identification of complex patterns which may improve ability to distinguish between real somatic ctDNA variants and false positive calls. …”
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Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

“…Abstract Purpose Exosomes are recognized as essential mediators in the intercellular communication between tumor cells, serving a pivotal function in tumor development. Nevertheless, the patterns of expression and medical relevance of exosome-related genes (ERGs) in nasopharyngeal carcinoma (NPC) remain insufficiently characterized. …”
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