Deep Learning-Enhanced Portable Chemiluminescence Biosensor: 3D-Printed, Smartphone-Integrated Platform for Glucose Detection by Chirag M. Singhal, Vani Kaushik, Abhijeet Awasthi, Jitendra B. Zalke, Sangeeta Palekar, Prakash Rewatkar, Sanjeet Kumar Srivastava, Madhusudan B. Kulkarni, Manish L. Bhaiyya

“…Comparative analysis was conducted using multiple deep learning models, including Random Forest, the Support Vector Machine (SVM), InceptionV3, VGG16, and ResNet-50, to identify the optimal architecture for accurate glucose detection. …”
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The Intelligent Infectious Disease Active Surveillance and early warning system in China: An application of dengue prevention and control by Liangyu Kang, Jian Hu, Kangning Cai, Wenzhan Jing, Min Liu, Wannian Liang

“…Based on these multi-channel data, users can select appropriate warning indicators and AI models to automatically trigger early warnings. Using vast amounts of surveillance data, the system can construct machine learning models to accurately assess the transmission risk of infectious diseases. …”
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NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care by Tarun Vats, Shrey Mehra, Uday Madan, Amit Chhabra, Akashdeep Sharma, Kunal Chhabra, Sarabjeet Singh, Utkarsh Chauhan

“…By utilizing NN predictions as input features for RF training and refining RF through grid search with cross-validation, the ensemble model produces highly precise predictions, facilitating strategic crop cultivation for optimal yields across diverse environmental conditions. …”
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Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images by Yifei Peng, Jun Sun, Zhentao Cai, Lei Shi, Xiaohong Wu, Chunxia Dai, Yubin Xie

“…This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F₁, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. …”
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Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review by Ethan E Abbott, Donald Apakama, Lynne D Richardson, Lili Chan, Girish N Nadkarni

“…These efforts aim to harness SDOH data optimally, enhancing patient care and mitigating health disparities. …”
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Abnormal Operation Detection of Automated Orchard Irrigation System Actuators by Power Consumption Level by Shahriar Ahmed, Md Nasim Reza, Md Rejaul Karim, Hongbin Jin, Heetae Kim, Sun-Ok Chung

“…Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. …”
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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…This study showed that blood cell detection for diagnosing acute leukemia based on images had 99% accuracy and outperformed other advanced models, including DenseNet121, ResNet-50, Incep-tionv3, MobileNet, and EfficientNet. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Extensive comparison across 50 diverse single cell datasets against 18 existing methods demonstrates that GNODEVAE consistently outperforms three major categories of benchmark methods: 8 machine learning dimensionality reduction techniques, 7 deep generative VAE variants, and 3 graph-based and contrastive learning deep predictive models. …”
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Classification of maize seed hyperspectral images based on variable-depth convolutional kernels by Yating Hu, Hongchen Zhang, Hongchen Zhang, Changming Li, Qianfu Su, Wei Wang

“…A four-layer CNN framework was constructed, and a total of 12 models were developed by varying the convolutional kernel depth to evaluate the impact on classification performance.ResultsExperimental results show that the proposed VD-CNN achieves optimal performance when the convolutional kernel depth is set to 15, attaining a training accuracy of 98.65% and a test accuracy of 96.97%. …”
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Exhaled volatile organic compounds as novel biomarkers for early detection of COPD, asthma, and PRISm: a cross-sectional study by Jiaxin Tian, Qiurui Zhang, Minhua Peng, Leixin Guo, Qianqian Zhao, Wei Lin, Sitong Chen, Xuefei Liu, Simin Xie, Wenxin Wu, Yijie Li, Junqi Wang, Jin Cao, Ping Wang, Min Zhou

“…Subsequently, classification models were established by machine learning algorithms, based on these VOC markers along with baseline characteristics. …”
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ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data. by Brett A McKinney, Bill C White, Diane E Grill, Peter W Li, Richard B Kennedy, Gregory A Poland, Ann L Oberg

“…Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. …”
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Artificial intelligence-based epitope discovery of Mpox virus: Rational vaccine design by Adane Adugna, Desalegn Abebaw, Abebaw Admasu, Bantayehu Addis Tegegne, Zigale Hibstu Teffera, Tiruzer Hibstu, Gelagey Baye, Baye Ashenef, Enyew Fenta Mengistu, Mohammed Jemal

“…Additionally, researchers can model and simulate immune interactions using AI to refine epitope selection on the basis of predicted immunogenicity and stability, ensuring well-optimized vaccine candidates. …”
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A Rice-Mapping Method with Integrated Automatic Generation of Training Samples and Random Forest Classification Using Google Earth Engine by Yuqing Fan, Debao Yuan, Liuya Zhang, Maochen Zhao, Renxu Yang

“…Due to cloud occlusion and banding problems, data extraction from Landsat series remote sensing images with medium spatial resolution is not optimal. Therefore, this study proposes a rice mapping method (LR) using Google Earth Engine (GEE), which uses Landsat images and integrates automatic generation of training samples and a machine learning algorithm, with the assistance of phenological methods. …”
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Frequency-Domain Robust Performance Condition for Controller Uncertainty in SISO LTI Systems: A Geometric Approach by Vahid Raissi Dehkordi, Benoit Boulet

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Analysis of the use of artificial intelligence systems for the development of physical exercise programs during rehabilitation of nephrology patients by V.V. Bezruk, D.D. Ivanov, I.D. Shkrobanets, M.A. Ivanchuk, P.R. Ivanchuk, I.S. Seman-Minko, O.I. Pervozvanska

“…Artificial intelligence is a tool in the hands of a physician to provide medical care; the quality of this tool will depend on the qualifications of the physician who will teach (machine learning) AI to use their knowledge and competencies to optimize the process of creating rehabilitation complexes for patients with kidney disease from the standpoint of evidence-based medicine.…”
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Development of a four autophagy-related gene signature for active tuberculosis diagnosis by Baoyan Ren, Feng Jia, Qixun Fang, Jingping Xu, Jingping Xu, Kangfeng Lin, Kangfeng Lin, Renhui Huang, Zhenqiong Liu, Xingan Xing, Xingan Xing

“…Marker genes were identified from differentially expressed autophagy-related genes using a Random Forest classifier. The optimal gene signature was selected based on optimal performance through a linear Support Vector Machine (SVM) classifier with cross-validation. …”
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Design and Experimental Analysis of an Air-Suction Wheat Precision Hill-Seed Metering Device by Ziheng Fang, Jing Zhang, Jincheng Chen, Feng Pan, Baiwei Wang, Chao Ji

“…Fluent simulations were used to determine the influence of orifice type on gas chamber flow fields; DEM-CFD-coupled simulation identified an appropriate negative pressure range of 2.6~3.4 kPa for optimal performance during seeding operations. Orthogonal experiments were carried out with mould hole diameter, negative pressure size, and seed plate speed as test factors alongside a qualification index, multiple sowing index, and missed sowing index as response indicators—leading to regression equation establishment, which yielded the optimal parameter combination: mould hole diameter at 1.8 mm; gas chamber negative pressure at 3.2 kPa; and a seed plate speed of 74 r·min⁻¹, with the corresponding forwards speed of the machine being 7 km·h⁻¹—resulting in a qualification index of 91.66%, multiple sowing index of 5.98%, and missed sowing index of 2.36%. …”
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Hot Deformation Behavior and Hot Processing Map of 50CrVA Spring Steel by Yang Zhao, Jian Zheng, Zhi Liu, Liqing Chen

“…It is important to explore the hot deformation behavior and establish the hot processing map of steel to design and optimize the hot rolling process. In this paper, 50CrVA spring steel was used as the experimental material. …”
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Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration by Shaofeng Tan, Jie Pei, Yaopeng Zou, Huajun Fang, Tianxing Wang, Jianxi Huang

“…Multi-modal information, including 2D/3D spectral indices, textural features, temperature data, and canopy structural attributes, was derived and integrated for rice yield prediction using ensemble Machine Learning (ML) models. Single-temporal and multi-temporal modeling strategies were compared. …”
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Design of federated routing mechanism in cross-domain scenario by Peizhuang CONG, Yuchao ZHANG, Ye TIAN, Wendong WANG, Dan LI

“…With the development of multi-network integration,how to ensure efficient interconnections among multiple independent network domains is becoming a key problem.Traditional interdomain routing protocol fails due to the limitation of domain information privacy,where each autonomous domain doesn’t share any specific intra-domain information.A machine learning-based federated routing mechanism was proposed to overcome the existing shortcomings.Each autonomous domain shares intra-domain information implicitly via neural network models and parameters.It not only breaks data islands problems but also greatly reduces the amount of transmitted data shared between domains,then decreases convergence delay of entire network information.Based on the federated routing mechanism,border routers can formulate global optimal routing strategies according to the status of entire network.…”
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