Hand Washing Gesture Recognition Using Synthetic Dataset by Rüstem Özakar, Eyüp Gedikli

“…Using this dataset, four neural network models, Inception-V3, Yolo-8n, Yolo-8n segmentation and PointNet, were trained for gesture classification. …”
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A Review of Approaches for Rapid Data Clustering: Challenges, Opportunities, and Future Directions by Mahnoor, Imran Shafi, Mahnoor Chaudhry, Elizabeth Caro Montero, Eduardo Silva Alvarado, Isabel de la Torre Diez, Md Abdus Samad, Imran Ashraf

“…This review emphasizes ongoing efforts to address these issues through research and suggests exciting directions for future investigations. …”
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Quantitative infrared detection methods for debonding in concrete-filled steel tubes during the hydration heat phase by Haonan Cai, Chongsheng Cheng, Hong Zhang, Jianting Zhou

“…This study proposes a Discreteness-Based Image Preprocessing (DBIP) method, combined with Otsu’s and K-means image segmentation methods, to explore its effectiveness in detecting debonding in CFST during the hydration heat phase. …”
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Artificial Intelligence in Revolutionizing Kidney Care and Beyond: Kid-AI Revolution by Kounaina Khan, Farhan Zameer, Pratheek Jain, Ravi KR, Vidya Niranjan, Manoj S, Ravish H, Subrahmanya Padyana

“…This review provides a comprehensive overview of AI applications in renal pathology, focusing on computer vision algorithms for kidney structure segmentation, specific pathological changes, diagnosis, treatment, and prognosis prediction based on images along with the role of machine learning (ML) and deep learning (DL) in addressing global public health issues related to various nephrological conditions. …”
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Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination by Huiying Liu, Jiarong Wang, Weijun Zhong, Haitao Nie, Xiaotong Deng, Jiaqi Sun, Ming Zhu, Ming Wei

“…Current research is primarily based on idealized simulation datasets, resulting in a performance gap when applied to actual applications. To address these issues, firstly, we construct a simulation dataset tailored to the challenges of actual scenarios. …”
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GU-Net3+: A Global-Local Feature Fusion Algorithm for Building Extraction in Remote Sensing Images by Yali Liu, Cui Ni, Peng Wang, Dongqing Yang, Hexin Yuan, Chao Ma

“…Global features can help the model better recognize the overall structure of large buildings and provide contextual background information when segmenting small buildings to avoid mis-segmentation. …”
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«Flipped» class – innovative model of training by M. V. Voronina

“…The study showed that there is a lack of:– Qualitative research for a deeper understanding of the phenomenon of the “Inverted” training in specific contexts;– Scientifically based research that examines the various aspects of the tried and tested implementation of the “Inverted” training;– Working programs and training materials for implementing the “Inverted” training, based on the reasonable theoretical foundations and methods of evaluation;– Recommendations for researchers, practitioners and policy-makers of the “Inverted” training to develop action plans; Theoretical and practical contribution of the research:– The analysis of the existing studies on the “Flipped” class of domestic and foreign scientists is conducted;– The positive and negative aspects of the “Flipped” class model are revealed;– Based on the analysis of the students’ survey, the issues are considered, related to the segmentation of the field of education, namely, the effectiveness of the application of the “Inverted” learning.Conclusion. …”
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Research on Foreign Object Intrusion Detection for Railway Tracks Utilizing Risk Assessment and YOLO Detection by Shanping Ning, Rui Guo, Pengfei Guo, Lu Xiong, Bangbang Chen

“…The results demonstrate that the proposed detection model can effectively identify foreign object intrusions on tracks, mitigating issues such as missed and false alarms. It achieves a 3.7% improvement in mean average precision (mAP) compared to the baseline model. …”
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COph100: A comprehensive fundus image registration dataset from infants constituting the “RIDIRP” database by Yan Hu, Mingdao Gong, Zhongxi Qiu, Jiabao Liu, Hongli Shen, Mingzhen Yuan, Xiaoqing Zhang, Heng Li, Hai Lu, Jiang Liu

“…To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public “RIDIRP” database. …”
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Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection by Zirou Jiang, Jintao Fu, Tianchen Zeng, Renjie Liu, Peng Cong, Jichen Miao, Yuewen Sun

“…Defect detection in industrial computed tomography (CT) images remains challenging due to small defect sizes, low contrast, and noise interference. To address these issues, we propose Defect R-CNN, a novel detection framework designed to capture the structural characteristics of defects in CT images. …”
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A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors by Ping Li, Junfu Fan, Yujie Du, Kuan Li, Yuke Zhou

“…This eliminates the issue of manually setting parameter thresholds during the slope unit division process. …”
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Artificial intelligence and chordoma: A scoping review of the current landscape and future directions by Eddie Guo, Rafael D. Sanguinetti, Lyndon Boone, Jiawen Deng, Husain Shakil, Mehul Gupta

“…The studies addressed diverse clinical tasks: 7 focused on differentiating chordomas from other tumours or classifying subtypes, 6 on survival prediction, 2 on tumour segmentation, 2 on outcome prediction, and 4 miscellaneous tasks. …”
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Radiometric landscape: a new conceptual framework and operational approach for landscape characterisation and mapping by Louise Lemettais, Samuel Alleaume, Sandra Luque, Anne-Élisabeth Laques, Yonas Alim, Laurent Demagistri, Agnès Bégué

“…Landscape mapping has the potential to address some of the most pressing research issues of our time, including climate change, sustainable development, and human well-being. …”
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Comparative analysis of single-view and multiple-view data collection strategies for detecting partially-occluded grape bunches: Field trials by Mar Ariza-Sentís, Hilmy Baja, Sergio Vélez, Rick van Essen, João Valente

“…This study compares two data acquisition methodologies for grape bunch detection and tracking in a commercial vineyard where leaf removal was not performed: a traditional single-view approach and a multiple-viewing method designed to mitigate fruit occlusion issues. The PointTrack algorithm, trained and validated using MOTS annotations, was employed to evaluate detection and tracking performance through metrics of three trials. …”
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ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network by Yiming Chen, Hui Zhang, Chen Zhang, Yi Liu

“…More notably, it achieves almost lossless accuracy in semantic segmentation tasks.…”
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LC-TMNet: learned lossless medical image compression with tunable multi-scale network by Hengrui Liao, Yue Li

“…Additionally, the flexible tree-structured image segmentation mechanism enabled us to implement variable-speed compression. …”
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Extensive identification of landslide boundaries using remote sensing images and deep learning method by Chang-dong Li, Peng-fei Feng, Xi-hui Jiang, Shuang Zhang, Jie Meng, Bing-chen Li

“…The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features. …”
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USS-Net: A neural network-based model for assisting flight route scheduling. by Yinlei Cheng, Qingfu Li

“…This method utilizes a neural network-based semantic segmentation model to monitor aircraft and perform situational awareness of the surrounding environment, thereby assisting in multi-aircraft route scheduling. …”
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End-to-End Predictive Network for Accurate Early Crop Planting Area Estimation by Kedi Lu, Zhong Ma, Zhao He, Pengcheng Huo, Haochen Zhang, Jinfeng Tang

“…This method eliminates the impact of the intermediate process of image segmentation accuracy on area estimation. Additionally, multi-subimage technology is employed to solve the issue of inconsistent input sample size, and label distribution smoothing technology is used to tackle the problem of unbalanced sample distribution. …”
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Multi channel fusion diffusion models for brain tumor MRI data augmentation by Cuihua Zuo, Junhao Xue, Cao Yuan

“…In our experiments, we used a publicly available brain tumor dataset and compared the performance of image classification and segmentation tasks between the original data and the data enhanced by our method. …”
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