Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review by Wilson W S Tam, Kang Sim, Jing Ling Tay, Yun Ling Ang

“…Objectives We aimed to examine the predictive accuracy of functioning, relapse or remission among patients with psychotic disorders, using machine learning methods. …”
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Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation by Sambadi Majumder, Chase M. Mason

“…Abstract Premise Here we demonstrate the application of interpretable machine learning methods to investigate intraspecific functional trait divergence using diverse genotypes of the wide‐ranging sunflower Helianthus annuus occupying populations across two contrasting ecoregions—the Great Plains versus the North American Deserts. …”
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Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning by Evgeny Y. Pavlenko

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Printed Strain Sensors for Motion Recognition: A Review of Materials, Fabrication Methods, and Machine Learning Algorithms by Nathan Zavanelli, Kangkyu Kwon, Woon-Hong Yeo

“…Next is a review of recent advances in nanomaterial printing to produce the complex structures necessary for functional devices. Next, we summarize machine learning approaches for human gesture recognition and the myriad applications and use cases for human-interfaced strain sensors. …”
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Deep learning-based image classification of sea turtles using object detection and instance segmentation models. by Jong-Won Baek, Jung-Il Kim, Chang-Bae Kim

“…Model performance during and after finishing training was evaluated by loss functions and various indexes, respectively. Based on loss functions, YOLOv5-seg demonstrated a lower error rate in detecting rather than classifying sea turtles than the YOLOv5. …”
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The communicative and organisational competencies of the librarian in networked learning support: a comparative analysis of the roles of the facilitator and the librarian. by Trine Schreiber, Camilla Moring

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Addressing survey fatigue bias in longitudinal social contact studies to improve pandemic preparedness by Shozen Dan, Zhi Ling, Yu Chen, Joshua Tegegne, Veronika K. Jaeger, André Karch, Swapnil Mishra, Oliver Ratmann, The Machine Learning &Global Health network

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Smart Tools for Smart Learning: IoT-Based Landslide Early Warning System with TILT Sensors and Apps by Fatmaryanti Siska Desy, Al Hakim Yusro, Widoyoko Sugeng Eko Putro, Akhdinirwanto Raden Wakhid

“…These findings highlight the dual benefits of this system as both an educational tool and a functional early warning tool. By combining IoT technology with hands-on learning, this approach bridges theoretical knowledge and practical application, empowering students to understand and mitigate landslide risks.…”
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Design of reinforcement learning based robust μ-synthesis controller for single phase grid-connected VSI by P. Shambhu Prasad, Alivelu M. Parimi

“…A novel methodology of tuning the weighting functions of the controller with advanced machine learning-based reinforcement learning has been adapted and performance specifications of the controller have been studied with tuned weighting functions. …”
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Integrating learner characteristics and generative AI affordances to enhance self-regulated learning: a configurational analysis by Xiu-Yi Wu, Thomas K. F. Chiu

“…The research explores how factors such as technological proficiency, user engagement, research skills, and feedback quality interact with the functionalities of GenAI tools to enhance SRL capacities. …”
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PHYSICAL PREPARATION OF THE FUTURE BACHELOR OF PHYSICAL EDUCATION AS A CONDITION OF EFFECTIVE LEARNING EFFICIENCY AND FUTURE CAREERS by Vladimir N. Kurys, Vadim S. Denisenko

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A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal by Yunpeng Ge, Kaiyang Ying, Guo Yu, Muhammad Ubaid Ali, Abubakr M. Idris, Abubakr M. Idris, Asfandyar Shahab, Habib Ullah, Habib Ullah

“…For adsorption, surface area and pore volume are distinctly important; in redox reactions for heavy metal removal, functional groups like C-O and C=O play vital roles. …”
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Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning by Frank Rhein, Timo Sehn, Michael A. R. Meier

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Cultural heritage reuse applying fuzzy expert knowledge and machine learning: Venice’s fortresses case study by Nicola Camatti, Giacomo di Tollo, Francesco Gastaldi, Federico Camerin

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Fast prediction method for fatigue life of pump truck boom structure based on ensemble learning model by DONG Qing, SU Youcheng, XU Gening, SHE Lingjuan, CHANG Yibin

“…ObjectiveTo rapidly and accurately assess the fatigue life of in-service concrete pump truck boom structures, a fatigue life prediction method based on an ensemble learning model is proposed, utilizing monitoring data and machine learning techniques.MethodsFirstly, a concrete pump truck information acquisition system was employed to obtain functional and performance characteristics during the operational phase of the pump truck. …”
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Survey on Backdoor Attacks on Deep Learning: Current Trends, Categorization, Applications, Research Challenges, and Future Prospects by Muhammad Abdullah Hanif, Nandish Chattopadhyay, Bassem Ouni, Muhammad Shafique

“…In this paper, we highlight the complete attack surface that can be exploited to inject hidden malicious functionality (backdoors) in machine learning models. …”
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Multi-objective Optimization of Staggered Tube Banks in Cross-flow Using Machine Learning and Genetic Algorithm by A. Tamanaei, F. Kowsary, S. Sahamifar, F. Samadi

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Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning by Yuanyuan Wu, Cai Liu, Jinge Huang, Fang Wang

“…Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to analyze the related pathways and functions of the DEPs. Then, we used machine learning methods to screen the feature proteins. …”
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RSLpred2: An Integrated Web Server for the Annotation of Rice Proteome Subcellular Localization Using Deep Learning by Naveen Duhan, Rakesh Kaundal

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Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study by Junchen Guo, Yunyun Dai, Sishan Jiang, Junqingzhao Liu, Xianghua Xu, Yongyi Chen

“…Significant differences were identified among the four PCOC phases of care in terms of the symptom distress, palliative care problem severity, functional status and daily living activities. The machine learning model developed in this study achieved areas under the curve (AUCs) of 0.997, 0.996, 0.999, and 0.999 for predicting the stable, unstable, deteriorating, and terminal phases in the training group, respectively. …”
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