Edge computing-based ensemble learning model for health care decision systems by Asir Chandra Shinoo Robert Vincent, Sudhakar Sengan

“…The main drawback of traditional Machine Learning (ML) techniques is their failure to predict reliably. …”
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Leveraging Deep Learning and Internet of Things for Dynamic Construction Site Risk Management by Li-Wei Lung, Yu-Ren Wang, Yung-Sung Chen

“…This study develops and validates an innovative hazard warning system that leverages deep learning-based image recognition (YOLOv7) and Internet of Things (IoT) modules to enhance construction site safety. …”
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DART-Vetter: A Deep Learning Tool for Automatic Triage of Exoplanet Candidates by Stefano Fiscale, Laura Inno, Alessandra Rotundi, Angelo Ciaramella, Alessio Ferone, Christian Magliano, Luca Cacciapuoti, Veselin Kostov, Elisa V. Quintana, Giovanni Covone, Maria Teresa Muscari Tomajoli, Vito Saggese, Luca Tonietti, Antonio Vanzanella, Vincenzo Della Corte

“…In the identification of new planetary candidates in transit surveys, the employment of deep learning models proved to be essential to efficiently analyze a continuously growing volume of photometric observations. …”
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Physical-Abstract Bidirectional-Guided Learning for High-Resolution Radar Target Recognition by Yuying Zhu, Yinan Zhao, Zhaoting Liu, Meilin He

“…For that, this article proposes a physical-abstract bidirectional-guided learning network that leverages scattering center based physical characteristics to guide deep models training, thereby enhancing the robustness and interpretability of deep features. …”
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Motivated to Collaborate: A Self-determination Framework to Improve Group-Based Learning by Justine Rogers, Marina Nehme

“…This reality has been acknowledged by the universities and legal professional bodies. The Threshold Learning Outcomes (TLOs) for the Australian Law degree stipulate, for instance, that law students must acquire and be able to demonstrate skills in collaboration and communication. …”
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Learning curve and its effect on the results of transsphenoidal endoscopic surgery of pituitary adenomas by V. Yu. Cherebillo, Yu. I. Ryumina

“…Despite the evidence of the overall effectiveness and safety of endoscopic surgery, a variety of factors, as reported in the modern literature, affect the curves of surgical training in minimally invasive endoscopic methods, including transsphenoidal endoscopic surgery of the pituitary gland, and, accordingly, the results of surgical treatment.The objective of the work was the analysis of the results of treatment of patients diagnosed with pituitary adenoma by transsphenoidal endoscopic method for the period from 2019 to 2022 in experienced and inexperienced surgeons, the determination of the threshold for learning this method and ways to overcome it.Methods and materials. …”
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Novel Deep Learning Framework for Evaporator Tube Leakage Estimation in Supercharged Boiler by Yulong Xue, Dongliang Li, Yu Song, Shaojun Xia, Jingxing Wu

“…This framework establishes a strong correlation between leakage and multifaceted characteristic parameters, moving beyond traditional threshold-based diagnostics to enable the early quantitative assessment of evaporator tube leakage.…”
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Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus by Cui Wang, Xinping Xu, Shuai Luo, Man Luo, Sha Li, Jianhong Si

“…Background: Diabetes Mellitus (DM) is a global health concern with rising prevalence, and its link to PFAS exposure remains unclear. No machine learning (ML) models have yet been developed to predict DM based on PFAS exposure. …”
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Collaborating With Schools for Public Health Research in England: Lessons Learned for Successful Partnerships by Ivelina Tsocheva, Jasmine Chavda, James Scales, Rosamund Dove, Harpal Kalsi, Helen E Wood, Grainne Colligan, Louise Cross, Luke Sartori, Jessica Moon, Aisling Murray, Sarah Van Den Berg, Alice Hirst, Jessica Mitchell, Jason Le, Frances Balkwill, Kristian Petrovic, Esther Lie, Mia Keating, Britzer Vincent Paul Raj, Pavani Kotala, Gurch Randhawa, Ian S Mudway, Chris Griffiths

“…The average annual participant attrition for our study was 11.6%. The acceptable threshold outlined in the initial protocol was 20%. …”
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Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semisupervised Deep Learning by Jianzhen Chen, Zhijian Luo, Cheng Cheng, Jun Hou, Shaohua Zhang, Chenggang Shu

“…Through semisupervised learning, the model significantly reduced reliance on extensive annotated data while enhancing robustness and generalization. …”
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Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance? by Tomke S. Wacker, Abraham G. Smith, Signe M. Jensen, Theresa Pflüger, Viktor G. Hertz, Eva Rosenqvist, Fulai Liu, Dorte B. Dresbøll

“…While traditional methods for analyzing stomatal traits rely on labor-intensive manual measurements, machine learning (ML) tools offer a promising alternative. …”
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Quantifying training response in cycling based on cardiovascular drift using machine learning by Artur Barsumyan, Artur Barsumyan, Raman Shyla, Anton Saukkonen, Christian Soost, Jan Adriaan Graw, Rene Burchard, Rene Burchard, Rene Burchard

“…In the new era of technology, we propose an experimental method using machine learning (ML) to measure response quantified as aerobic fitness level based on cardiovascular drift and aerobic decoupling data.MethodsTwenty well-trained athletes in cycling-based sports performed monthly aerobic fitness tests over five months, riding at 75% of their functional threshold power for 60 min. …”
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AMNED: An Efficient Framework for Spiking Neuron Coding in AirComp Federated Learning by Juncheng Ji, Chan-Tong Lam, Ke Wang, Benjamin K. Ng

“…In advancing future ACFL technologies, Over-the-Air Computation (AirComp) has emerged as a groundbreaking innovation. AirComp Federated Learning (ACFL) integrates AirComp with federated learning, transforming distributed machine learning by enhancing data privacy and leveraging network device computation. …”
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Advanced deep transfer learning techniques for efficient detection of cotton plant diseases by Prashant Johri, SeongKi Kim, Kumud Dixit, Prakhar Sharma, Barkha Kakkar, Yogesh Kumar, Jana Shafi, Muhammad Fazal Ijaz

“…The findings of the paper emphasize the prospective of deep transfer learning as a viable technique for cotton plant disease diagnosis by providing a cost-effective and efficient solution for crop disease monitoring and management. …”
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The Neural Correlates and Behavioral Impact of Peripheral Noise Electrical Stimulation on Motor Learning by Li-Wei Chou, Man-Wai Kou, Hui-Min Lee, Felipe Fregni, Vincent Chen, Chung-Lan Kao

“…Somatosensory input plays a critical role in motor learning. Noise reduces the neural activation threshold and enhances the sensitivity of sensory neurons. …”
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A machine learning-based risk prediction model for diabetic oral ulceration by Wang Xiaoling, Wang BingQian, Zhu Zhenqi, Li Wen, Gu Shuyan, Chen Hanbei, Xin Feng, Chenglong Yang, Jutang li, Guoyao Tang, Jie Wei

“…However, current diagnostic methods often fall short in early detection and intervention. Machine learning (ML) has shown promise in predicting disease development, yet no relevant predictive models for DOU have been established. …”
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A Hybrid Deep Learning–Based Approach for Visual Field Test Forecasting by Ashkan Abbasi, PhD, Sowjanya Gowrisankaran, PhD, Wei-Chun Lin, MD, PhD, Xubo Song, PhD, Bhavna Josephine Antony, PhD, Gadi Wollstein, MD, Joel S. Schuman, MD, Hiroshi Ishikawa, MD

“…Design: A retrospective longitudinal study using deep learning–based VF forecasting models. Subjects and Controls: A total of 1750 subjects (healthy and glaucoma patients) with 19 437 Humphrey VF (24-2 Swedish Interactive Threshold Algorithm) tests collected from longitudinal glaucoma cohorts at the University of Pittsburgh and New York University. …”
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DBN-BAAE: Enhanced Lightweight Anomaly Detection Mechanism with Boosting Adversarial Autoencoder by Yanru Chen, Bei Wu, Wang Zhong, Yanru Guo, Dizhi Wu, Yi Ren, Yuanyuan Zhang

“…To address these issues, this work introduces a deep belief network-based boosting adversarial autoencoder termed DBN-BAAE, a novel lightweight anomaly detection mechanism based on boosting adversarial learning. The proposed lightweight mechanism saves computational overhead, enhances autoencoder training stability with an improved deep belief network (DBN) for pre-training, boosts encoder expression through ensemble learning, achieves high detection accuracy via an adversarial decoder, and employs a dynamic threshold to enhance adaptability and reduce the need for retraining. …”
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PCA-GWO-KELM Optimization Gait Recognition Indoor Fusion Localization Method by Xiaoyu Ji, Xiaoyue Xu, Suqing Yan, Jianming Xiao, Qiang Fu, Kamarul Hawari Bin Ghazali

“…Meanwhile, adaptive upper thresholds and adaptive dynamic time thresholds are constructed to void pseudo peaks and valleys. …”
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Predicting high confidence ctDNA somatic variants with ensemble machine learning models by Rugare Maruzani, Liam Brierley, Andrea Jorgensen, Anna Fowler

“…We benchmarked our models against rule-based filtering with a set of hard, medium, and soft thresholds. Precision-recall curves showed the high depth model outperformed rule-based filtering at all thresholds in Test Data (PR-AUC 0.71). …”
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