Real-Time Object Detection Using Low-Resolution Thermal Camera for Smart Ventilation Systems by Jun-Hee Lee, Eung-Tea Kim

“…Deep learning-based object detection research has primarily evolved around RGB camera imagery. …”
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Automatic detection of optic canal fractures and recognition and segmentation of anatomical structures in the orbital apex based on artificial intelligence by Yu-Lin Li, Yu-Hao Li, Mu-Yang Wei, Guang-Yu Li

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RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography by Yejia Gan, Wanzhong Huang, Yan Deng, Xiaoying Xie, Yuanyuan Gu, Yaozhuang Zhou, Qian Zhang, Maosheng Zhang, Yangchun Liu

“…IntroductionAortic stenosis (AS) is a valvular heart disease that obstructs normal blood flow from the left ventricle to the aorta due to pathological changes in the valve, leading to impaired cardiac function. Echocardiography is a key diagnostic tool for AS; however, its accuracy is influenced by inter-observer variability, operator experience, and image quality, which can result in misdiagnosis. …”
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Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks by Riadh Azzaz, Mohammad Jahazi, Samira Ebrahimi Kahou, Elmira Moosavi-Khoonsari

“…The Adam optimizer and non-linear sigmoid activation function were employed. The best ANN model included four hidden layers and 448 neurons. …”
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Short-term solar irradiance forecasting model based on hyper-parameter tuned LSTM via chaotic particle swarm optimization algorithm by V Ashok Gajapati Raju, Janmenjoy Nayak, Pandit Byomakesha Dash, Manohar Mishra

“…The main objective of the CPSO is to minimize the prediction error through optimizing the LSTM's hyper-parameters such as neurons in hidden layers, learning rate, batch size, dropout rate and activation function. …”
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AI‐Powered Advancements in Food Analysis and Safety: Ensuring Quality, Protection, and Precision in Modern Food Systems: A Review by Ammar B. Altemimi, Farhang Hameed Awlqadr, Raqad R. Al‐Hatim, Syamand Ahmed Qadir, Mohammed N. Saeed, Aryan Mahmood Faraj, Tablo H. Salih, Hala S. Mahmood, Mohammad Ali Hesarinejad, Francesco Cacciola

“…It introduces the connection of AI technologies, including machine learning, deep learning, and computer vision, to enhancing food safety, control, and analysis. …”
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A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images by Yifan Wang, Mofan Li, Gen Li, Zihan Hu, Zehua Dong, Han Li

“…First, we transfer a DeepLab V3+ network model for road segmentation to bridge segmentation, fine-tuning it with bridge scattering signal data, while a new loss supervision function CentroidLoss, has been added to the model to improve the integrity of the bridge signal segmentation. …”
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Fire and Smoke Detection Based on Improved YOLOV11 by Zhipeng Xue, Lingyun Kong, Haiyang Wu, Jiale Chen

“…Although they are relatively simple to implement, their performance is limited in complex and variable practical applications. In contrast, deep learning-based methods can automatically learn deep features in data and have higher accuracy and stronger generalization ability. …”
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Hyperspectral Image Reconstruction Based on Blur–Kernel–Prior and Spatial–Spectral Attention by Hongyu Xie, Mingyu Yang, Huansong Huang, Mingle Zhang, Wei Zhang, Qingbin Jiao, Liang Xu, Xin Tan

“…The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mn>1</mn></msub></semantics></math></inline-formula> loss function, combined with spectral dimension loss and peak signal-to-noise ratio loss, is utilized to constrain and ensure reconstruction accuracy. …”
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Rapid and accurate classification of mung bean seeds based on HPMobileNet by Shaozhong Song, Shaozhong Song, Shaozhong Song, Zhenyang Chen, Helong Yu, Helong Yu, Mingxuan Xue, Mingxuan Xue, Junling Liu

“…Mung bean seeds are very important in agricultural production and food processing, but due to their variety and similar appearance, traditional classification methods are challenging, to address this problem this study proposes a deep learning-based approach. In this study, based on the deep learning model MobileNetV2, a DMS block is proposed for mung bean seeds, and by introducing the ECA block and Mish activation function, a high-precision network model, i.e., HPMobileNet, is proposed, which is explored to be applied in the field of image recognition for the fast and accurate classification of different varieties of mung bean seeds. …”
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An Improved Backbone Fusion Neural Network for Orchard Extraction by Baiyu Dong, Ziqi Wang, Chongzhi Chen, Ke Wang, Jing Zhang

“…Semantic segmentation deep learning models utilizing convolutional neural networks (CNNs) or vision transformers have become the cornerstone for such tasks. …”
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Assessment of plant diversity index in degraded desert grassland using UAV hyperspectral multimodal data and Encoder-CNN by Zhaohui Tang, Chuanzhong Xuan, Tao Zhang, Xinyu Gao, Suhui Liu, Mengqin Zhang

“…Abstract The biodiversity function of the desert steppe ecosystem faces many challenges under the pressure of climate change and human activities. …”
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Unveiling ac4C modification pattern: a prospective target for improving the response to immunotherapeutic strategies in melanoma by Jianlan Liu, Pengpeng Zhang, Chaoqin Wu, Binlin Luo, Xiaojian Cao, Jian Tang

“…Taken together, the acRGS could function as a reliable and prospective tool to improve the clinical prognosis for melanoma individuals.…”
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A Hierarchical Path Planning Framework of Plant Protection UAV Based on the Improved D3QN Algorithm and Remote Sensing Image by Haitao Fu, Zheng Li, Jian Lu, Weijian Zhang, Yuxuan Feng, Li Zhu, He Liu, Jian Li

“…Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. …”
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Brain Morphometry and Cognitive Features in the Prediction of Irritable Bowel Syndrome by Arvid Lundervold, Ben René Bjørsvik , Julie Billing , Birgitte Berentsen , Gülen Arslan Lied , Elisabeth K. Steinsvik , Trygve Hausken , Daniela M. Pfabigan , Astri J. Lundervold 

“…While brain–gut interactions are recognized in IBS pathophysiology, the relationship between brain morphometry, cognitive function, and clinical features remains poorly understood. …”
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HMS-Net: A Hierarchical Multilabel Fine-Grained Ship Detection Network in Remote Sensing Images by Yunchao Yang, Zhengning Zhang, Pengming Feng, Yiming Yan, Guangjun He, Shaobo Liu, Pengyong Zhang, Haorao Gao

“…The advent of deep learning has significantly advanced the efficiency and accuracy of fine-grained ship detection in remote sensing images. …”
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DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images by Dongbin Liu, Jiandong Fang, Yudong Zhao

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Research on Mobile Robot Path Planning Based on MSIAR-GWO Algorithm by Danfeng Chen, Junlang Liu, Tengyun Li, Jun He, Yong Chen, Wenbo Zhu

“…In order to verify the effectiveness of the MSIAR-GWO algorithm, it is compared with a variety of commonly used swarm intelligence optimization algorithms in benchmark test functions and raster maps of different complexities in comparison experiments, and the results show that the MSIAR-GWO shows excellent stability, higher solution accuracy, and faster convergence speed in the majority of the benchmark-test-function solving. …”
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Identification of Exosome-Associated Biomarkers in Diabetic Foot Ulcers: A Bioinformatics Analysis and Experimental Validation by Tianbo Li, Lei Gao, Jiangning Wang

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Dysfunction in mitochondrial electron transport chain drives the pathogenesis of pulmonary arterial hypertension: insights from a multi-omics investigation by Xin Zhang, Jieling Li, Minyi Fu, Xijie Geng, Junjie Hu, Ke-Jing Tang, Pan Chen, Jianyong Zou, Xiaoman Liu, Bo Zeng

“…Methods We integrated three microarray datasets from the Gene Expression Omnibus (GEO), including 222 lung samples (164 PAH, 58 controls), for differential expression and functional enrichment analyses. Machine learning identified key mitochondria-related signaling pathways. …”
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