A Novel Supervoxel-Based NE-PC Model for Separating Wood and Leaf Components from Terrestrial Laser Scanning Data by Shengqin Gong, Xin Shen, Lin Cao

“…However, previous studies on wood–leaf separation exhibited limitations in unsupervised adaptability and robustness to complex tree architectures, while demonstrating inadequate performance in fine branch detection. …”
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Ultrahigh Resolution X‐Ray Imaging With Thin‐Film Scintillators Based on Aggregation‐Induced Delayed Fluorescence Luminogens by Hongyun Wang, Qisheng Sun, Fangxu Yang, Zheyang Li, Lingjie Sun, Xiaotao Zhang, Zujin Zhao, Wenping Hu

“…ABSTRACT Flexible thin‐film scintillators based on organic semiconductors offer transformative potential for X‐ray imaging, enabling conformity to nonplanar objects and compatibility with complex structural applications. However, challenges in synergizing high solid‐state luminescence, X‐ray absorption, and efficient exciton utilization have become the bottleneck limiting their application in high‐resolution imaging. …”
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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…It can effectively improve the efficiency and quality of fruit picking. Automatic picking equipment combined with computer vision often uses object detection algorithms to identify objects, and object detection algorithms can be divided into both traditional algorithms and deep learning-based object detection algorithms. …”
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MRA-YOLOv8: A Network Enhancing Feature Extraction Ability for Photovoltaic Cell Defects by Nannan Wang, Siqi Huang, Xiangpeng Liu, Zhining Wang, Yi Liu, Zhe Gao

“…To address the challenges posed by complex backgrounds and the low occurrence in photovoltaic cell images captured by industrial sensors, we propose a novel defect detection method: MRA-YOLOv8. …”
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Enhancing Crack Segmentation Network with Multiple Selective Fusion Mechanisms by Yang Chen, Tao Yang, Shuai Dong, Like Wang, Bida Pei, Yunlong Wang

“…Furthermore, the proposed MSF-CrackNet also significantly reduces computational complexity, with only 2.39 million parameters and 8.58 GFLOPs, making it a practical and efficient solution for real-world crack detection tasks, especially in scenarios with limited computational resources.…”
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A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories by Hannelore Sebestyen, Daniela Elena Popescu, Rodica Doina Zmaranda

“…Given the diversity of such systems, selecting the most appropriate solutions and technologies to address the challenges is a complex activity. This paper provides an exhaustive evaluation of existing security challenges related to the IoT domain, analysing studies published between 2021 and 2025. …”
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Wind Turbine Blade Defect Recognition Method Based on Large-Vision-Model Transfer Learning by Xin Li, Jinghe Tian, Xinfu Pang, Li Shen, Haibo Li, Zedong Zheng

“…Timely and accurate detection of wind turbine blade surface defects is crucial for ensuring operational safety and improving maintenance efficiency with respect to large-scale wind farms. …”
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Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…Addressing these concerns is critical for enhancing the overall safety and efficiency of autonomous systems navigating complex traffic environments. …”
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A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Conclusion Using the largest known sample size and the most complex mixed cohort, we have successfully devised efficient screening models for prostate cancer, namely PCa4miR and PCaSS. …”
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Abnormality-aware multimodal learning for WSI classification by Thao M. Dang, Qifeng Zhou, Yuzhi Guo, Hehuan Ma, Saiyang Na, Thao Bich Dang, Jean Gao, Junzhou Huang

“…By combining abnormal detection with multimodal feature integration, our framework offers an efficient and scalable solution for advancing computational pathology.…”
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Machine Learning in Information and Communications Technology: A Survey by Elias Dritsas, Maria Trigka

“…Machine learning (ML) has emerged as a powerful tool, enabling more adaptive, efficient, and scalable systems in this field. This article presents a comprehensive survey on the application of ML techniques in ICT, covering key areas such as network optimization, resource allocation, anomaly detection, and security. …”
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Optical Frequency Comb-Based 256-QAM WDM Coherent System with Digital Signal Processing Algorithm by Babar Ali, Ghulam Murtaza, Hafiz Muhammad Bilal, Tariq Mahmood, Muhammad Rashid, Zaib Ullah

“…Digital signal processing (DSP) techniques, including digital filtering, detection, and impairment compensation, contribute to high transmission and spectral efficiency (SE). …”
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Enhancing the Distinguishability of Minor Fluctuations in Time Series Classification Using Graph Representation: The MFSI-TSC Framework by He Nai, Chunlei Zhang, Xianjun Hu

“…Compared with ten benchmark methods, MFSI-TSC exhibits both high accuracy and computational efficiency, making it more suitable for deployment in sensor systems to complete incipient fault detection tasks.…”
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Spectrum Sensing Using Fast Slepian Transform for Cognitive Radio Networks by Aman Kumar, Indrasen Singh, Dilip Kumar Choudhary

“…Additionally, we introduce a computational efficiency metric based on floating-point operations (FLOPs) to evaluate the energy cost of detection. …”
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Optimization of Sorghum Spike Recognition Algorithm and Yield Estimation by Mengyao Han, Jian Gao, Cuiqing Wu, Qingliang Cui, Xiangyang Yuan, Shujin Qiu

“…By optimizing the full detection–tracking–yield pipeline, this solution overcomes challenges in small object missed detections, ID switches under occlusion, and real-time processing in complex scenarios. …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…The advanced WIoUv3 loss function further boosted the model's performance, achieving a mAP@0.5 of 84.5% and an F1 score of 83%, marking an approximate 3.4% improvement over the baseline, and showcasing a favorable balance between detection accuracy and model efficiency. Comparative experiments indicated that AGW-YOLO outperformed several mainstream object detection algorithms, including Faster R-CNN, YOLOv8n, YOLOv9-tiny, YOLOv10n, RT-DETR-R50, and TPH-YOLO, across most evaluation metrics, offering high recognition accuracy with lower computational complexity. …”
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YOLOv11-RDTNet: A Lightweight Model for Citrus Pest and Disease Identification Based on an Improved YOLOv11n by Qiufang Dai, Shiyao Liang, Zhen Li, Shilei Lyu, Xiuyun Xue, Shuran Song, Ying Huang, Shaoyu Zhang, Jiaheng Fu

“…However, existing object detection models face limitations in complex backgrounds, target occlusion, and small target recognition, and they struggle to be efficiently deployed on resource-constrained devices. …”
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MLK-TR: a Multi-branch Large Kernel TRansformer for UAV-based images by Xun Li, Yuzhen Zhao, Yang Zhao, Zhun Guo, Jianjing Gao, Baoxi Yuan

“…These results confirm the effectiveness of MLK-TR in addressing the complexities of UAV object detection.…”
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Attention-based BiLSTM-XGBoost model for reliability assessment and lifetime prediction of digital microfluidic systems by Lifeng He, Qili Yang, Junxi Chen, Wenjing Liu, Zhijie Luo

“…This often leads to evaluation lag and limits their application, especially for complex droplets. To address these issues, this study proposes a novel prediction model for digital microfluidic (DMF) devices. …”
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A Semi-Supervised Object Detector Based on Adaptive Weighted Active Learning and Orthogonal Data Augmentation by Meng Wang, Xiao Xu, Haipeng Liu

“…To efficiently utilize limited resources, this paper proposes a semi-supervised object detection (SSOD) approach based on novel adaptive weighted active learning (AWAL) and orthogonal data augmentation (ODA). …”
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