Deep learning for automated coral reef monitoring a novel system based on YOLOv8 detection and DeepSORT tracking by Younes Ouassine, Noël Conruyt, Mohsen Kayal, Philippe A. Martin, Lionel Bigot, Vignes Lebbe Regine, Hajar Moussanif, Jihad Zahir

“…By leveraging deep learning, our approach enables more efficient data collection, contributing to the protection of these vulnerable ecosystems in the face of increasing environmental pressures.…”
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RDW-YOLO: A Deep Learning Framework for Scalable Agricultural Pest Monitoring and Control by Jiaxin Song, Ke Cheng, Fei Chen, Xuecheng Hua

“…Due to target diversity, life-cycle variations, and complex backgrounds, traditional pest detection methods often struggle with accuracy and efficiency. …”
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Evaluating the Performance of Machine Learning Classifiers for Network Intrusion Detection: A Comparative Study Using the UNSW-NB15 Dataset by Iwan Handoyo Putro

“…It is because of their growing adoption and complexity of cyber-attacks. Therefore, protecting network infrastructures and identifying malicious behavior becomes a necessity. …”
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Enhancing UAV Object Detection in Low-Light Conditions with ELS-YOLO: A Lightweight Model Based on Improved YOLOv11 by Tianhang Weng, Xiaopeng Niu

“…ELS-YOLO features a re-parameterized backbone (ER-HGNetV2) with integrated Re-parameterized Convolution and Efficient Channel Attention mechanisms, a Lightweight Feature Selection Pyramid Network (LFSPN) for multi-scale object detection, and a Shared Convolution Separate Batch Normalization Head (SCSHead) to reduce computational complexity. …”
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The Influence and Compensation of Environmental Factors (pH, Temperature, and Conductivity) on the Detection of Chemical Oxygen Demand in Water by UV-Vis Spectroscopy by Jingwei Li, Yipei Ding, Yijing Lu, Jia Liu, Chenxuan Zhou, Zhiyu Shao

“…Considering the complexity of environmental factors, a data fusion method is proposed to compensate for the influence of three environmental factors simultaneously. …”
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RNA-BioLens: A Novel Raspberry Pi-Based Digital Microscope With Image Processing for Acute Lymphoblastic Leukemia Detection by Angeline Dwi Sanjaya, Rachmad Setiawan, Nada Fitrieyatul Hikmah

“…This application achieved a detection accuracy of 97.67% for the overall detection system, demonstrating its potential as a reliable tool for efficient and accurate diagnosis.…”
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Enhanced detection of surface deformations in LPBF using deep convolutional neural networks and transfer learning from a porosity model by Muhammad Ayub Ansari, Andrew Crampton, Samer Mohammed Jaber Mubarak

“…Our approach demonstrates the power of transfer learning in adapting a model known for porosity detection in LPBF to identify surface deformations with high accuracy (94%), matching the performance of the best existing models but with significantly less complexity. …”
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Automatic Segmentation and Characterization of Structure Planes From Borehole Images Based on Deep Learning by Shuangyuan Chen, Zengqiang Han, Yi Cheng, Chao Wang

“…By providing a reliable and efficient tool for structure plane segmentation and parameter characterization, this study enhances the accuracy and efficiency of rock mass structure detection and analysis.…”
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Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses by Can Wang, Xinhui Wu, Zhaoquan Wang, Han Shao, Dapeng Ye, Xiangzeng Kong

“…Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. …”
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Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning by Liang Wang, Chun Yang, Chao Yuan, Yanan Liu, Yanqing Chen

“…The sparse representation method is used to effectively encode the voiceprint signal and extract representative signal features; the compressed sensing technology is applied to efficiently reconstruct the signal using a small amount of sampled data, significantly reducing the data collection amount and storage requirements; deep feature learning and damage pattern classification based on convolutional neural network further improve the accuracy and intelligence level of detection.The research results show that the proposed method effectively reduces the computational complexity and greatly improves the detection accuracy. …”
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Hate Speech Detection and Online Public Opinion Regulation Using Support Vector Machine Algorithm: Application and Impact on Social Media by Siyuan Li, Zhi Li

“…Detecting hate speech in social media is challenging due to its rarity, high-dimensional complexity, and implicit expression via sarcasm or spelling variations, rendering linear models ineffective. …”
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A Wireless Sensor Network-Based Combustible Gas Detection System Using PSO-DBO-Optimized BP Neural Network by Min Zhou, Sen Wang, Jianming Li, Zhe Wei, Lingqiao Shui

“…The model exhibits strong robustness in handling nonlinear responses and cross-sensitivity effects across multiple sensors, demonstrating its effectiveness in complex detection scenarios under laboratory conditions within embedded wireless sensor networks.…”
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YOLO-WTB: Improved YOLOv12n Model for Detecting Small Damage of Wind Turbine Blades From Aerial Imagery by Phat T. Nguyen, Duy C. Huynh, Loc D. Ho, Matthew W. Dunnigan

“…In addition, in the backbone part, we also propose to remove a Convolution module and an Area Attention Concatenate-Convolution-Fusion module and add an improved SoftPool Feature Spatial Pyramid Pooling - Fast module to increase the feature extraction ability while maintaining the complexity of the model. The proposed model not only optimizes wind turbine maintenance efficiency but also contributes to advancements in the field of computer vision.…”
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MEL-YOLO: A Novel YOLO Network With Multi-Scale, Effective, and Lightweight Methods for Small Object Detection in Aerial Images by Yang Yang, Fangtao Feng, Guisuo Liu, Juxing Di

“…In recent years, deep learning has been extensively applied to small object detection, achieving significant advancements. Nevertheless, there remains substantial potential to improve both the effectiveness and efficiency of small object detection in Uncrewed aerial vehicle (UAV) images. …”
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Graphical Empirical Mode Decomposition–Convolutional Neural Network-Based Expert System for Early Corrosion Detection in Truss-Type Bridges by Alan G. Lujan-Olalde, Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Martin Valtierra-Rodriguez, Jose M. Machorro-Lopez, Juan P. Amezquita-Sanchez

“…To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. …”
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Rapid in vitro assessment of the immunogenicity potential of engineered antibody therapeutics through detection of CD4+ T cell interleukin-2 secretion by Yoshiyuki Arata, Shigeki Motoyama, Mariko Yano, Tatsuya Ikuno, Shunsuke Ito, Tomochika Matsushita, Akira Takeiri, Yukari Nishito, Nami Yabuki, Hideaki Mizuno, Zenjiro Sampei, Masayuki Mishima, Masaki Honda, Jumpei Kiyokawa, Hiromi Suzuki, Shuichi Chiba, Mitsuyasu Tabo, Chiyomi Kubo

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Linking Electrocardiogram and Echocardiogram: Comparing Classical Machine Learning and Deep Learning Neural Networks for the Detection of Regional Wall Motion Abnormalities by Shantanu M. Joshi, Hana R. Shaik, Shivam Rai Sharma, Philip Strong, Uma Srivatsa, Imo Ebong, Hyoyoung Jeong, Chen-Nee Chuah, Lihong Mo

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No increase is detected and modeled for the seasonal cycle amplitude of <i>δ</i>¹³C of atmospheric carbon dioxide by F. Joos, F. Joos, S. Lienert, S. Lienert, S. Zaehle

“…The good data–model agreement in the seasonal amplitude of <span class="inline-formula"><i>δ</i>¹³</span>C<span class="inline-formula">_a</span> and in its decadal trend provides implicit support for the regulation of stomatal conductance by C<span class="inline-formula">₃</span> plants towards intrinsic water use efficiency growing proportionally to atmospheric CO<span class="inline-formula">₂</span> over recent decades. …”
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FM‐YOLOv8:Lightweight gesture recognition algorithm by Fanghai Li, Xitai Na, Jinshuo Shi, Qingbin Sun

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Deep learning models for detection of explosive ordnance using autonomous robotic systems: trade-off between accuracy and real-time processing speed by Vadym Mishchuk, Herman Fesenko, Vyacheslav Kharchenko

“…The objectives are as follows: 1) conduct a comparative analysis of YOLOv8 and RT-DETR image processing models for explosive ordnance (EO) detection, focusing on accuracy and real-time processing speed;2) to explore the impact of different input image resolutions on model performance for identifying the optimal resolution for EO detection tasks;3) to analyze how object size (small, medium, large) affects detection efficiency for enhancing EO recognition accuracy; 4) to develop recommendations for EO detection model configurations; 5) to propose methods for enhancing EO detection model performance in complex environments. …”
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