Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)... by 周贯旭(ZHOU Guanxu), 姜红(JIANG Hong), 徐雪芳(XU Xuefang)

“…The feature fusion CNN model based on spectra and images can achieve good classification of the four strains, which outperforms the other three traditional machine learning algorithm models. …”
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Leveraging Green Finance Innovation to Curb Pollution Emissions: Evidence From High-polluting Firms in China by Chunheng Fu, Qing Yu, Yi Liu, Xiaohui Xu

“…A double machine learning model is used to estimate the causal effects. …”
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Integrating Ambient In-Home Sensor Data and Electronic Health Record Data for the Prediction of Outcomes in Amyotrophic Lateral Sclerosis: Protocol for an Exploratory Feasibility S... by William E Janes, Noah Marchal, Xing Song, Mihail Popescu, Abu Saleh Mohammad Mosa, Juliana H Earwood, Vovanti Jones, Marjorie Skubic

“…ObjectiveThis study aims to describe a federated approach to assimilating sensor and EHR data in a machine learning algorithm to predict decline among people living with ALS. …”
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Global burden of vertebral fractures from 1990 to 2021 and projections for the next three decades by Honghui Lei, Zebin Huang, Fangyong Wang, Tao Liu, Yang Yu, Sitong Su, Meiling Cheng, Haoyuan Chen

“…The annual percentage change (EAPC) was calculated to represent temporal trends from 1990 to 2021. Machine learning was used to predict the global burden of vertebral fractures over the next 30 years. …”
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Assessing the economic impact of climate risk on green and low-carbon transformation by Chen Qin, Hongli Lou, Li Li

“…To bridge this gap, we propose a novel framework that integrates the Integrated Green Transition Model (IGTM) and the Sustainable Transition Optimization Framework (STOF).MethodsIGTM employs agent-based modeling and network dynamics to simulate the cascading impacts of green policies on energy systems and socio-economic outcomes, while STOF leverages advanced optimization and machine learning techniques to balance economic growth, emission reductions, and social equity under diverse scenarios.ResultsBy synthesizing these approaches, our study provides actionable insights into the economic impact of climate risk and offers robust strategies for optimizing investments in renewable energy and policy interventions. …”
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Random forest algorithm integrated with an initial basic feasible solution in buckling analysis of a two-dimensional functionally graded porous taper beam by Ravikiran Chinthalapudi, Jagadesh Kumar Jatavallabhula, Geetha Narayanan Kannaiyan, Bridjesh Pappula, Seshibe Makgato

“…This work provides practical insights for the design and optimization of advanced graded structures where conventional models fall short, establishing a novel pathway for the integration of machine learning in structural mechanics.…”
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Dataset on droplet spreading and rebound behavior of water and viscous water-glycerol mixtures on superhydrophobic surfaces with laser-made channelsMendeley Data by Matic Može, Samo Jereb, Robert Lovšin, Jure Berce, Matevž Zupančič, Iztok Golobič

“…It can help validate theoretical and numerical models of droplet spreading, retracting, and rebounding from poorly wettable surfaces, optimize superhydrophobic surfaces for applications such as self-cleaning and drag reduction, and contribute to machine learning models predicting droplet behavior. …”
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Hyperspectral Imaging for the Dynamic Mapping of Total Phenolic and Flavonoid Contents in Microgreens by Pawita Boonrat, Manish Patel, Panuwat Pengphorm, Preeyabhorn Detarun, Chalongrat Daengngam

“…This study investigates the application of hyperspectral imaging (HSI) combined with machine learning (ML) models for the dynamic mapping of total phenolic content (TPC) and total flavonoid content (TFC) in sunflower microgreens. …”
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Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone Data: Development and Validation Study by Mehrab Beikzadeh, Ian W Holloway, Kimmo Kärkkäinen, Chenglin Hong, Cory Cascalheira, Elizabeth S C Wu, Callisto Boka, Alexandra C Avendaño, Elizabeth A Yonko, Majid Sarrafzadeh

“…MethodsWe developed a mobile phone app to collect participants’ messaging, location, and app use data and trained a machine learning model to predict risk behaviors for STI and HIV transmission. …”
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A systematic review of computational simulation methods for predicting the toxicity of chemical compounds by Akram Tabrizi, Fatemeh Paridokht, Yaser Khorshidi Behzadi, Rezvan Zendehdel

“…Various methods, including Quantitative Structure-Activity Relationship (QSAR), machine learning, and molecular dynamics, were widely used to predict the toxicity of chemical compounds, with the predictive accuracy of these models generally being high. …”
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Exoplanet Classification Through Vision Transformers with Temporal Image Analysis by Anupma Choudhary, Sohith Bandari, B. S. Kushvah, C. Swastik

“…Traditional methods demand substantial effort, time, and cost, highlighting the need for advanced machine learning techniques to enhance classification efficiency. …”
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Predicting depression risk in middle-aged and elderly adults in China using CNN-BiLSTM-Attention mechanism and LSTM+SHAP framework by Shengxian Bi, Gang Li, Huawei Tan, Yingchun Chen, Dandan Guo

“…However, current research predominantly employs machine learning (ML) methods to predict depression risk, often overlooking the spatiotemporal heterogeneity of this risk. …”
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An Advanced Generative AI-Based Anomaly Detection in IEC61850-Based Communication Messages in Smart Grids by Aydin Zaboli, Yong-Hwa Kim, Junho Hong

“…Moreover, the system shows a substantial improvement in advanced evaluation metrics, including a Matthews Correlation Coefficient (MCC) of 0.95, highlighting its robust capability to accurately differentiate between normal and anomalous events. The ToD model adapts effectively to new attack scenarios without extensive retraining, unlike traditional machine learning (ML) models or HITL, which require frequent updates. …”
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Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer by Ayan Chatterjee, Michael A. Riegler, K. Ganesh, Pål Halvorsen

“…Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. …”
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Borohydride Synthesis of Silver Nanoparticles for SERS Platforms: Indirect Glucose Detection and Analysis Using Gradient Boosting by Viktoriia Bakal, Olga Gusliakova, Anastasia Kartashova, Mariia Saveleva, Polina Demina, Ilya Kozhevnikov, Evgenii Ryabov, Daniil Bratashov, Ekaterina Prikhozhdenko

“…To enable glucose sensing, the substrates were further functionalized with glucose oxidase (GOx), allowing detection in the 1–10 mM range. Machine learning classification and regression models based on gradient boosting were employed to analyze SERS spectra, enhancing the accuracy of quantitative predictions (R² = 0.971, accuracy = 0.938, limit of detection = 0.66 mM). …”
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Utilisation of Deep Neural Networks for Estimation of Cajal Cells in the Anal Canal Wall of Patients with Advanced Haemorrhoidal Disease Treated by LigaSure Surgery by Inese Fišere, Edgars Edelmers, Šimons Svirskis, Valērija Groma

“…Haemorrhoidectomy specimens were collected from patients undergoing surgery with the LigaSure device. A YOLOv11-based machine learning model, trained on 376 immunohistochemical images, automated ICC detection using the CD117 marker, achieving a mean average precision (mAP50) of 92%, with a recall of 86% and precision of 88%. …”
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Agricultural Non-Point Source Pollution: Comprehensive Analysis of Sources and Assessment Methods by Fida Hussain, Shakeel Ahmed, Syed Muhammad Zaigham Abbas Naqvi, Muhammad Awais, Yanyan Zhang, Hao Zhang, Vijaya Raghavan, Yiheng Zang, Guoqing Zhao, Jiandong Hu

“…It assesses current evaluation models, encompassing field- and watershed-scale methodologies, and investigates novel technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) that possess the potential to enhance pollution monitoring and predictive precision. …”
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Assessing Earthquake-Triggered Ecosystem Carbon Loss Using Field Sampling and UAV Observation by Wen Zeng, Baofeng Di, Yu Zhan, Wen He, Junhui Li, Ziquan Zuo, Siwen Yu, Tan Mi

“…This study quantifies ecosystem carbon loss from the Luding Earthquake by integrating field sampling, UAV-based LiDAR, and machine learning models to assess vegetation and soil carbon stocks. …”
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Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks by Haithem Ben Chikha, Alaa Alaerjan, Randa Jabeur

“…The proposed model combines the deep learning capabilities of DRNs for feature extraction with Principal Component Analysis (PCA) for dimensionality reduction and feature refinement. …”
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Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement by Gabrielle Hoyer, Kenneth T. Gao, Felix G. Gassert, Johanna Luitjens, Fei Jiang, Sharmila Majumdar, Valentina Pedoia

“…We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. …”
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