Deep learning method for cucumber disease detection in complex environments for new agricultural productivity by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Xin Liu

“…This study proposes YOLO-Cucumber, an improved lightweight detection algorithm based on YOLOv11n, incorporating four key innovations: (1) Deformable Convolutional Networks (DCN) for enhanced feature extraction of irregular targets, (2) a P2 prediction layer for fine-grained detection of early-stage lesions, (3) a Target-aware Loss (TAL) function addressing class imbalance, and (4) Channel Pruning via Batch Normalization (CPBN) for model compression. …”
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Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation by Qiang Zhang, Zhe Wu, Boshuo An, Ruitian Sun, Yanping Cui

“…In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. …”
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An Ensemble Learning Method for the Kernel-Based Nonlinear Multivariate Grey Model and its Application in Forecasting Greenhouse Gas Emissions by Lan Wang, Nan Li, Ming Xie

“…In order to give policy makers more power to set the specific target of GHG emission reduction, we propose an ensemble learning method with the least squares boosting (LSBoost) algorithm for the kernel-based nonlinear multivariate grey model (KGM) (1, N), and it is abbreviated as BKGM (1, N). …”
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Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study by Una Kjällquist, Nikos Tsiknakis, Balazs Acs, Sara Margolin, Luisa Edman Kessler, Scarlett Levy, Maria Ekholm, Christine Lundgren, Erik Olsson, Henrik Lindman, Antonios Valachis, Johan Hartman, Theodoros Foukakis, Alexios Matikas

“…The machine learning model achieved AUC under ROC of 0.77 in training and 0.83 in validation cohorts for prediction of indication for adjuvant chemotherapy according to ROR/Prosigna. …”
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Duty of care, data science, and gambling harm: A scoping review of risk assessment models by Virve Marionneau, Kim Ristolainen, Tomi Roukka

“…Measures to assess the prediction ability of models are not optimal. Industry funding or involvement is prevalent in model development. …”
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Circadian Regulator-Mediated Molecular Subtypes Depict the Features of Tumor Microenvironment and Indicate Prognosis in Head and Neck Squamous Cell Carcinoma by Ling Aye, Zhanying Wang, Fanghua Chen, Yujun Xiong, Jiaying Zhou, Feizhen Wu, Li Hu, Dehui Wang

“…Circadian score was an independent risk factor and exhibited excellent predictive efficiency in both the training cohort from the TCGA database and the validation cohort from the GEO database. …”
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Leveraging artificial intelligence to strengthen surgical systems in sub-Saharan Africa by Osedebamen Ralph-Okhiria, Ikhide Alonge

“…AI holds great potential throughout the surgical care pathway, from diagnosing and planning interventions via AI-based imaging and predictive algorithms to enabling more precise, minimally invasive procedures using AI-directed robotic platforms and navigation systems. …”
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Forecasting Surface Velocity Fields Associated With Laboratory Seismic Cycles Using Deep Learning by G. Mastella, F. Corbi, J. Bedford, F. Funiciello, M. Rosenau

“…Using data from two types of experiments, we explore the prediction performances of multiple Deep Learning (DL) and ML algorithms. …”
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Global research trends in AI-assisted blood glucose management: a bibliometric study by Li Yuan, Li Yuan, Yangtian Wang, Meiping Xing, Tao Liu, Dan Xiang

“…Major research clusters included CGM, machine learning algorithms, and predictive modeling. The United States, Italy, and the UK were prominent contributors, with key journals such as Diabetes Technology & Therapeutics leading the field.ConclusionAI technologies are significantly advancing blood glucose management, especially through machine learning and predictive models. …”
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Development of Explainable Machine Learning Models to Identify Patients at Risk for 1-Year Mortality and New Distant Metastases Postendoprosthetic Reconstruction for Lower Extremit... by Jiawen Deng, BHSc, Myron Moskalyk, BHSc, MSc, Madhur Nayan, MD, PhD, Ahmed Aoude, MEng, MD, FRCSC, Michelle Ghert, MD, FRCSC, Sahir Bhatnagar, PhD, Anthony Bozzo, MSc, MD, FRCSC

“…LightGBM was identified as the best-performing algorithm for both outcomes. For 1-year mortality prediction without percent necrosis, LightGBM achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% confidence interval [CI] 0.70-0.86) during cross-validation and 0.72 on internal validation. …”
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Application of Artificial Intelligence in Prosthodontics in the 21st century by Lavanya V, Keerthivasan MS, Venkatakrishnan CJ, Tamizhesai BV, Anandh V

“…In removable prosthodontics convolutional neural networks CNNs have enabled accurate classification of partially edentulous arches and prediction of facial aesthetics. For maxillofacial prostheses AI supports non-surgical rehabilitation using smart devices enhancing color matching and comfort. …”
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AI-Driven Surrogate Model for Room Ventilation by Jaume Luis-Gómez, Francisco Martínez, Alejandro González-Barberá, Javier Mascarós, Guillem Monrós-Andreu, Sergio Chiva, Elisa Borrás, Raúl Martínez-Cuenca

“…This paper explores a novel methodology to create a Machine Learning (ML) model for the predictive control of a ventilation system combining Computational Fluid Dynamics (CFD) with Artificial Intelligence (AI). …”
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Cluster-based efficient information dissemination in dynamic networks by Zhiwei Yang, Weigang Wu, Yong Li, Yishun Chen

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Amismart an advanced metering infrastructure for power consumption monitoring and forecasting in smart buildings by Sarah Hadri, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri, Mohamed El aroussi, Zaradatcht Taifour, Jaafar Gaber

“…Abstract Load forecasting is considered to be the core of an efficient predictive energy management for buildings. In this context, the deployment of smart meters and sensors enabled continuous energy usage monitoring in modern buildings. …”
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Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma by Youliang Zhou, Yi Zhou, Jiabin Hu, Yao Xiao, Yan Zhou, Liping Yu

“…This study constructed a novel AMD1-related scoring system for predicting the prognosis and treatment responsiveness of patients with HCC, enabling the prediction of prognosis and identification of potential treatment targets.…”
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Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. …”
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Evaluation of inflammatory serum parameters as a diagnostic tool in patients with endometriosis: a case-control study by Mariz Kasoha, Panagiotis Sklavounos, Istvan Molnar, Meletios P. Nigdelis, Bashar Haj Hamoud, Erich-Franz Solomayer, Gilbert Georg Klamminger

“…Abstract Even though non-invasive prediction of endometriosis may seem technically feasible using sophisticated machine learning algorithms, a standard clinical use case for non-surgical diagnosis of endometriosis has not yet been established. …”
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Quantifying the Geomorphological Susceptibility of the Piping Erosion in Loess Using LiDAR-Derived DEM and Machine Learning Methods by Sisi Li, Sheng Hu, Lin Wang, Fanyu Zhang, Ninglian Wang, Songbai Wu, Xingang Wang, Zongda Jiang

“…The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. …”
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Development of a 101.6K liquid‐phased probe for GWAS and genomic selection in pine wilt disease‐resistance breeding in Masson pine by Jingyi Zhu, Qinghua Liu, Shu Diao, Zhichun Zhou, Yangdong Wang, Xianyin Ding, Mingyue Cao, Dinghui Luo

“…The DNNGP (deep neural network‐based method for genomic prediction) model demonstrated superior performance in GS, achieving a maximum predictive accuracy of 0.71. …”
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The impact of a coach-guided personalized depression risk communication program on the risk of major depressive episode: study protocol for a randomized controlled trial by JianLi Wang, Cindy Feng, Mohammad Hajizadeh, Alain Lesage

“…Individuals are eligible, if they: (1) are 18 years or older, (2) have not had a depressive episode in the past two months, (3) are at high risk of MDE based on the sex-specific risk predictive algorithms for MDE (predicted risk of 6.5% + for men and of 11.2% + for women), (4) can communicate in either English or French, and (5) agree to be contacted for follow-up interviews. …”
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