Tree Species Detection and Enhancing Semantic Segmentation Using Machine Learning Models with Integrated Multispectral Channels from PlanetScope and Digital Aerial Photogrammetry i... by Arun Gyawali, Mika Aalto, Tapio Ranta

“…Remote sensing technologies combined with machine learning techniques present an encouraging solution, offering a more efficient alternative to conventional field-based methods. …”
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Validation of a comprehensive long-read sequencing platform for broad clinical genetic diagnosis by Siddhartha Sen, Hillary P. Handler, Alec Victorsen, Zach Flaten, Aidan Ellison, Todd P. Knutson, Sarah A. Munro, Ryan J. Martinez, Charles John Billington, Jennifer J. Laffin, Sarah Bray, Pawel Mroz, Sophia Yohe, Andrew C. Nelson, Matthew Bower, Bharat Thyagarajan

“…The implementation of a unified comprehensive technique that can simultaneously detect a broad spectrum of genetic variation would substantially increase efficiency of the diagnostic process. …”
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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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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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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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Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions by Khaled Chahine

“…Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. …”
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Filtering airborne LiDAR data based on multi-view window and multi-resolution hierarchical cloth simulation by Shangshu Cai, Yong Pang, Sisi Yu, Xiangguo Lin, Xinlian Liang

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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 integrated model efficiently identifies the health state and predicts the failure time of digital microfluidic devices. …”
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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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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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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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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

“…The paper concludes with a synthesis of security challenges and threats of each identified category, along with their solutions, aiming to support decision-making during the design approach of IoT-based applications and to guide future research toward comprehensive and efficient IoT frameworks.…”
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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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Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

“…The rapid evolution of railway systems, driven by digitization and the proliferation of Internet-of-Things (IoT) devices, has resulted in an unprecedented volume of diverse and complex data. This railway big data offers immense opportunities for advancing safety, efficiency, and sustainability in transportation but presents significant analytical challenges due to its heterogeneity, high-dimensionality, and temporal dependencies. …”
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Edge-Based Dynamic Spatiotemporal Data Fusion on Smart Buoys for Intelligent Surveillance of Inland Waterways by Ruolan Zhang, Chenhui Zhao, Yu Liang, Jingfeng Hu, Mingyang Pan

“…Additionally, a novel regional division and a Kalman filter-based method for AIS and video data fusion were proposed, effectively resolving the issues of data sparsity and coordinate transformation robustness under complex waterway conditions. This approach substantially advances the precision and efficiency of waterway monitoring systems, providing a robust theoretical and practical framework for the intelligent supervision of inland waterways.…”
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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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YOLOv8n-WSE-Pest: A Lightweight Deep Learning Model Based on YOLOv8n for Pest Identification in Tea Gardens by Hongxu Li, Wenxia Yuan, Yuxin Xia, Zejun Wang, Junjie He, Qiaomei Wang, Shihao Zhang, Limei Li, Fang Yang, Baijuan Wang

“…The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. …”
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