Detection of Invasive Species (Siam Weed) Using Drone-Based Imaging and YOLO Deep Learning Model by Deepak Gautam, Zulfadli Mawardi, Louis Elliott, David Loewensteiner, Timothy Whiteside, Simon Brooks

“…We specifically examined the effects of input training images, solar illumination, and model complexity on the model’s detection performance and investigated the sources of false positives. …”
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An Improved Deep Learning Unsupervised Approach for MRI Tissue Segmentation for Alzheimer’s Disease Detection by Karan Kumar, Isha Suwalka, Adaora Uche-Ezennia, Celestine Iwendi, Cresantus N. Biamba

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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

“…To address the issue of high complexity in current pedestrian anomaly detection network models, which hinders real-world deployment, this paper proposes a lightweight anomaly detection network called LPCF-YOLO (Lightweight Parallel Cross-Fusion YOLO) based on the YOLOv8n model. …”
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Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards by Eyad Btoush, Xujuan Zhou, Raj Gururajan, Ka Ching Chan, Omar Alsodi

“…The rapid advancement of technology has increased the complexity of cyber fraud, presenting a growing challenge for the banking sector to efficiently detect fraudulent credit card transactions. …”
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Development of Dual ‘RT‐LAMP‐LFA’ Rapid Detection Technology With Gold Magnetic Nanoparticles for Influenza Virus by Haiyang Fan, Yonglong Gong, Mengying Chang, Juan Gao, Mengjia Li, Siyu Chen, Ruoyi Yang, Muxue Zhao, Yali Cui, Wenli Hui

“…However, current diagnostic tools often face limitations in speed, accuracy or complexity of result interpretation; there is a great need for more efficient detection technology for influenza virus, especially for use in resource‐limited settings or during large‐scale outbreaks. …”
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Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding by Wasim Khan, Mohammad Ishrat, Ahmad Neyaz Khan, Mohammad Arif, Anwar Ahamed Shaikh, Mousa Mohammed Khubrani, Shadab Alam, Mohammed Shuaib, Rajan John

“…Attributed networks are prevalent in the current information infrastructure, where node attributes enhance knowledge discovery. Anomaly detection in attributed networks is gaining attention for its potential uses in cybersecurity, finance, and healthcare. …”
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Continuous Flow Injection Analysis Method for the Determination of a Drug Diphenhydramine Hydrochloride by Using Phosphomolybdic Acid by Muntadhar M. Jabbar, Elham N. Mezaal

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Aerial image segmentation of embankment dams based on multispectral remote sensing: a case study in the Belo Monte Hydroelectric Complex, Pará, Brazil by Carlos André de Mattos Teixeira, Thabatta Moreira Alves de Araujo, Evelin Cardoso, Marcos Antonio Costantin Filho, João Weyl Costa, Carlos Renato Lisboa Frances

“…Recently, multispectral remote sensing data and machine learning techniques have been applied to develop methodologies that enable automatic vegetation analysis and anomaly detection based on computer vision. As a first step toward this automation, this study introduces a methodology for land cover segmentation of earth-rock embankment dam structures within the Belo Monte Hydroelectric Complex, located in the state of Pará, northern Brazil. …”
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RFAG-YOLO: A Receptive Field Attention-Guided YOLO Network for Small-Object Detection in UAV Images by Chengmeng Wei, Wenhong Wang

“…The YOLO series of object detection methods have achieved significant success in a wide range of computer vision tasks due to their efficiency and accuracy. …”
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AHN-YOLO: A Lightweight Tomato Detection Method for Dense Small-Sized Features Based on YOLO Architecture by Wenhui Zhang, Feng Jiang

“…When benchmarked against other lightweight models in the field, AHN-YOLO exhibits superior training efficiency and detection accuracy in complex, dense scenarios, demonstrating clear advantages.…”
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Deep Learning Method with Domain-Task Adaptation and Client-Specific Fine-Tuning YOLO11 Model for Counting Greenhouse Tomatoes by Igor Glukhikh, Dmitry Glukhikh, Anna Gubina, Tatiana Chernysheva

“…The large-scale implementation of computer vision systems in greenhouses requires approaches that reduce costs, time and complexity, particularly in creating training data and preparing neural network models. …”
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State-of-the-Art Deep Learning Algorithms for Internet of Things-Based Detection of Crop Pests and Diseases: A Comprehensive Review by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu

“…Moreover, the research discusses the advantages and limitations of these techniques, emphasizing their architecture design, efficiency and accuracy. The findings demonstrate that there is a tradeoff between robustness and complexity among existing techniques, and authors recommend future trends aimed at creating robust models with fewer parameters that are more accurate and easily implementable on small IoT-based and portable devices suitable for in-field and real-time applications. …”
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Basic Introduction of New Energy Vehicles Structure and Research Progress on Fault Detection Methods of New Energy Vehicles by Ling Hao

“…Future research should refine the display of fault detection results to enhance maintenance efficiency.…”
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Studying the performance of YOLOv11 incorporating DHSA BRA and PPA modules in railway track fasteners defect detection by Chengwei Zhang, Jiawei Zhu, Yihao Ma, Qingmei Huang

“…The model also demonstrates competitive performance compared to other popular object detection algorithms, highlighting its potential to improve both detection accuracy and efficiency.…”
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Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study by Yanqiong Ding, Baojiang Han, Hua Jiang, Hao Hu, Lei Xue, Jiasen Weng, Zhili Tang, Yuzhang Liu

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Detection of Crack Sealant in the Pretreatment Process of Hot In-Place Recycling of Asphalt Pavement via Deep Learning Method by Kai Zhao, Tianzhen Liu, Xu Xia, Yongli Zhao

“…They often appear as wide black patches that overlap with cracks and potholes, and complex background noise further complicates detection. …”
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MDFusion: Multi-Dimension Semantic–Spatial Feature Fusion for LiDAR–Camera 3D Object Detection by Renzhong Qiao, Hao Yuan, Zhenbo Guan, Wenbo Zhang

“…Extensive experiments on the KITTI and ONCE datasets demonstrate that our method achieves competitive performance in complex scenes, significantly improving the multi-modal fusion quality and detection accuracy while maintaining computational efficiency.…”
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HSF-YOLO: A Multi-Scale and Gradient-Aware Network for Small Object Detection in Remote Sensing Images by Fujun Wang, Xing Wang

“…These results confirm that HSF-YOLO is a unified and effective solution for small object detection in complex RSI scenarios, offering a good balance between accuracy and efficiency.…”
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Employing CNN mobileNetV2 and ensemble models in classifying drones forest fire detection images by Dima Suleiman, Ruba Obiedat, Rizik Al-Sayyed, Shadi Saleh, Wolfram Hardt, Yazan Al-Zain

“… In recent years, the adoption of advanced machine learning techniques has revolutionized approaches to solving complex problems, such as identifying occurrences of forest fires. …”
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TMBO-AOD: Transparent Mask Background Optimization for Accurate Object Detection in Large-Scale Remote-Sensing Images by Tianyi Fu, Hongbin Dong, Benyi Yang, Baosong Deng

“…Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. …”
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