A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds by Kedi Shen, Jieyu Zhao, Min Xie

“…Moreover, it performs well in invariant tasks such as classification and category-level segmentation.…”
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Foundation Models in Agriculture: A Comprehensive Review by Shuolei Yin, Yejing Xi, Xun Zhang, Chengnuo Sun, Qirong Mao

“…The paper examines the diversity and applications of AFMs in areas like crop classification, pest detection, and crop image segmentation, and delves into specific use cases such as agricultural knowledge question-answering, image and video analysis, decision support, and robotics. …”
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Deep learning in defects detection of PV modules: A review by Katleho Masita, Ali Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…The importance of preprocessing steps such as image normalization, registration, and segmentation is emphasized to enhance detection accuracy. …”
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Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training by Kamelia Sepanloo, Daniel Shevelev, Young-Jun Son, Shravan Aras, Janine E. Hinton

“…The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. …”
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A Deep Learning Framework for Healthy Lifestyle Monitoring and Outdoor Localization by Mehrab Rafiq, Naif S. Alshammari, Haifa F. Alhasson, Dina Abdulaziz Alhammadi, Mohammed Alshehri, Ahmad Jalal, Hui Liu

“…The capabilities of smartphones and wearable technology have increased due to advancements in sensing technology. Inertial sensors like gyroscopes and accelerometers are now frequently seen in smartphones. …”
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Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning by Ilya Larin, Alexander Karabelsky

“…Recently, a number of works have shown the advantages of hyperbolic embeddings in vision tasks, including clustering and classification based on the Poincaré ball model. One area worth highlighting is unsupervised segmentation, which we believe is undervalued, particularly in the context of non-Euclidean spaces. …”
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A novel MRI-based deep learning–radiomics framework for evaluating cerebrospinal fluid signal in central nervous system infection by Ferhat Cüce, Gökalp Tulum, Muhammed Ikbal Isik, Marziye Jalili, Güven Girgin, Ömer Karadaş, Niray Baş, Berza Özcan, Ümit Savaşci, Sena Şakir, Akçay Övünç Karadaş, Eda Teomete, Onur Osman, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed

“…IntroductionAccurate and timely diagnosis of central nervous system infections (CNSIs) is critical, yet current gold-standard techniques like lumbar puncture (LP) remain invasive and prone to delay. …”
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A bird song detector for improving bird identification through deep learning: A case study from Doñana by Alba Márquez-Rodríguez, Miguel Ángel Mohedano-Munoz, Manuel J. Marín-Jiménez, Eduardo Santamaría-García, Giulia Bastianelli, Pedro Jordano, Irene Mendoza

“…Applying the Bird Song Detector before classification improved species identification, as all classification models performed better when analyzing only the segments where birds were detected. …”
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Classifying Dementia Severity Using MRI Radiomics Analysis of the Hippocampus and Machine Learning by Dong-Her Shih, Yi-Huei Wu, Ting-Wei Wu, Yi-Kai Wang, Ming-Hung Shih

“…Compared with other studies, our results show the highest accuracy among the three dementia severity classifications. Combining the radiomic features from segmented hippocampus in MRI with machine learning promises unprecedented accuracy in predicting dementia severity, potentially slowing progression and enhancing patients’ quality of life.…”
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Hadron Identification Prospects with Granular Calorimeters by Andrea De Vita, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, Jan Kieseler, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph Willmore

“…Two machine learning approaches, XGBoost and fully connected deep neural networks, were employed to assess the classification performance across particle pairs. The results indicate that fine segmentation improves particle discrimination, with higher granularity yielding more detailed characterization of energy showers. …”
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A two-stage multi-scale attention-based network for weakly supervised cataract fundus image enhancement by Xiaoyong Fang, Yue Wang, Xiangyu Li, Wanshu Fan, Dongsheng Zhou

“…Moreover, the enhanced images contribute to improved performance in downstream tasks such as vessel segmentation and disease classification.…”
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Real-Time Hand Gesture Recognition in Clinical Settings: A Low-Power FMCW Radar Integrated Sensor System with Multiple Feature Fusion by Haili Wang, Muye Zhang, Linghao Zhang, Xiaoxiao Zhu, Qixin Cao

“…The proposed system integrates velocity profiles, angular variations, and spatial-temporal features through a dual-stage processing architecture: an adaptive energy thresholding detector segments gestures, followed by an attention-enhanced neural classifier. …”
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THE CURRENT STATE OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY – A REVIEW OF THE BASIC CONCEPTS, APPLICATIONS, AND CHALLENGES by Mariana Yordanova

“…Deep learning, especially deep convolutional neural networks (CNNs), has become a prominent approach, mimicking brain functions to process images through multiple layers. CNNs excel in tasks like lesion detection and disease classification, aiding radiologists in diagnosing conditions more accurately. …”
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Bipolar Morphological Neural Networks: Gate-Efficient Architecture for Computer Vision by Elena E. Limonova, Daniil M. Alfonso, Dmitry P. Nikolaev, Vladimir V. Arlazarov

“…In addition, we summarize the current results on the use of the bipolar morphological model in typical tasks of technical vision: image classification and semantic segmentation. We consider simple LeNet-5-like neural networks, as well as deeper ResNet and UNet architectures. …”
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TRIDENT: Text-Free Data Augmentation Using Image Embedding Decomposition for Domain Generalization by Yoonyoung Choi, Geunhyeok Yu, Hyoseok Hwang

“…Deep learning has advanced vision tasks such as classification, segmentation, and detection. However, in real-world scenarios, models often encounter domains that differ from the ones seen during training, which can lead to substantial performance degradation. …”
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Enhanced vehicle localization with low-cost sensor fusion for urban 3D mapping. by Sheraz Shamim, Syed Riaz Un Nabi Jafri

“…The efficacy of the developed map has employed for extraction of road edges and associated road assets by establishing the lucrative classification technique of the point cloud using Split and Merge segmentation and Hough transformation. …”
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Recognition of Strawberry Powdery Mildew in Complex Backgrounds: A Comparative Study of Deep Learning Models by Jingzhi Wang, Jiayuan Li, Fanjia Meng

“…In this study, an HSV-based image segmentation method was employed to enhance the extraction of disease regions from complex backgrounds. …”
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Influence of road environmental factors on traffic accidents involving vulnerable road users through negative binomial models. by Ying Chen, Yi Tian, Zhaoheng Ouyang, Jiaxun Zhu

“…Overall, the findings offer robust empirical evidence to guide development of tailored interventions that consider the unique capacities and exposures of different pedestrian populations. The age-segmented analyses also contribute transportation equity insights for achieving Vision Zero goals through inclusive infrastructure design.…”
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Genome-wide identification and unveiling the role of MAP kinase cascade genes involved in sugarcane response to abiotic stressors by Ahmad Ali, Xue-Ting Zhao, Ji-Shan Lin, Ting-Ting Zhao, Cui-Lian Feng, Ling Li, Rui-Jie Wu, Qi-Xing Huang, Hong-Bo Liu, Jun-Gang Wang

“…Results This study identified 89 ScMAPK, 24 ScMAPKK, and 107 ScMAPKKK genes through genome-wide analysis. Phylogenetic classification revealed that four subgroups were present in each ScMAPK and ScMAPKK family, and three sub-families (ZIK-like, RAF-like, and MEKK-like) presented in the ScMAPKKK family. …”
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Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules by Tatsuki Komori, Hiroki Nishikawa, Keita Sasaki, Ittetsu Taniguchi, Takao Onoye

“…Traditional machine learning techniques approach the task of urine sediment particle detection as an image classification problem, wherein the particles are segmented based on features like edges or thresholds. …”
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