A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n by Lei Yang, Wenhao Cui, Jingqian Li, Guotao Han, Qi Zhou, Yubin Lan, Jing Zhao, Yongliang Qiao

“…Deployed on a Jetson TX2 development board, the model achieves a recognition speed of 56 frames per second (FPS) and an mAP of 84.2%, confirming its suitability for real-time detection. Furthermore, the improved model effectively reduces instances of both false negatives and false positives for diseased cotton bolls while yielding higher detection confidence, thus providing robust technical support for intelligent cotton boll disease detection.…”
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Real-time Defense Against Cyber Threats: Analyzing Wazuh's Effectiveness in Server Monitoring by Alde Alanda, H.A Mooduto, Ronal Hadi

“…The study investigates Wazuh's effectiveness in real-time monitoring of three AWS EC2 instance-based cloud servers. Wazuh's capabilities such as log data collection, malware detection, active response automation, and Docker container monitoring, are examined. …”
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CLF3D: A Coarse-Labeling Framework to Facilitate 3D Object Detection in Point Clouds by Nuo Cheng, Chuanyu Luo, Han Li, Sikun Ma, Shengguang Lei, Pu Li

“…These refined pseudo-labels are then used to fine-tune the model, progressively improving its detection performance on unlabeled data. Through iterative refinement of pseudo-labels, the model parameters, and the instance bank, CLF3D continuously improves label quality and accuracy. …”
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A deep learning model for detecting and classifying multiple marine mammal species from passive acoustic data by Quentin Hamard, Minh-Tan Pham, Dorian Cazau, Karine Heerah

“…Yet, the large amount of audio recordings raises the need to automate the detection of acoustic events. For instance, the increasing number of Offshore Wind Farms (OWF) raises key environmental and societal issues relating to their impacts on wildlife. …”
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A framework for detecting and predicting highway traffic anomalies via multimodal fusion and heterogeneous graph neural networks. by Shaowei Sun, Mingzhou Liu

“…Experimental results demonstrate that the model performs well in various scenarios, showing significant improvement in accuracy and stability over existing models like AGC-LSTM and AttentionDeepST. For instance, the proposed MHGNN-CPLE model achieves superior accuracy and F1 score in static detection tasks while maintaining high accuracy under different noise levels in dynamic detection scenarios. …”
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AI-Based Breast Cancer Detection System: Deep Learning and Machine Learning Approaches for Ultrasound Image Analysis by Amro Moursi, Abdulrahman Aboumadi, Uvais Qidwai

“…Our preprocessing methods significantly improve input data quality, leading to promising detection accuracies. For instance, ResNet-18 achieved impressive results, outperforming other models. …”
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Large Scale Asset Detection Within Railway Scene Point Cloud Data From Mobile Laser Scanning by Bram Ton, Rick Akster

“…Using a custom open dataset, these two models are evaluated to detect masts, tension rods, signals, and relay cabinets. …”
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Intraindividual, intraspecific, and interspecific variation shapes natural selection and its detection in two convergently-evolved lizard species. by Simone Des Roches, Max R Lambert, Michaela S Brinkmeyer, Jacqueline M Howells, Andy Dettinger, Erica Bree Rosenblum

“…We found that our inferences into which traits were under selection depended on which measurement instance we used (first, last, or median measurement of all measurements of a given trait), and, therefore, the degree of intraindividual variation within each trait. …”
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A qPCR assay for the detection of Phytophthora abietivora, an emerging pathogen on fir species cultivated as Christmas trees. by Guillaume Charron, Marie-Krystel Gauthier, Hervé van der Heyden, Guillaume J Bilodeau, Philippe Tanguay

“…However, these methods are rarely adapted to larger scale monitoring such as in situ detection. For these applications, molecular detection of environmental DNA (eDNA) provides the fast and high-throughput results needed. …”
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RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection by Bin Wang, Kang Zhao, Tong Xiao, Pinle Qin, Jianchao Zeng

“…Remote sensing (RS) image change detection (CD) aims to identify areas of interest that have changed between bitemporal images. …”
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Benchmarking bias in embeddings of healthcare AI models: using SD-WEAT for detection and measurement across sensitive populations by Magnus Gray, Leihong Wu

“…In this study, we aimed to develop a new framework to detect and measure inherent medical biases based on SD-WEAT (Standard Deviation - Word Embedding Association Test). …”
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Machine Learning and Deep Learning Approaches for Fake News Detection: A Systematic Review of Techniques, Challenges, and Advancements by Omar Bashaddadh, Nazlia Omar, Masnizah Mohd, Mohd Nor Akmal Khalid

“…Our findings provide a foundation for researchers and practitioners to build scalable, trustworthy, and context-aware fake news detection systems for global use.…”
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Advances in Detection Technologies for Pesticide Residues and Heavy Metals in Rice: A Comprehensive Review of Spectroscopy, Chromatography, and Biosensors by Yu Han, Ye Tian, Qingqing Li, Tianle Yao, Jie Yao, Zhengmao Zhang, Long Wu

“…In addition, this review envisions future directions for the development of detection technologies, emphasizing the importance of miniaturization, multiplexed detection, integration with nanotechnology, and real-time monitoring systems. …”
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UAV-OVD: Open-Vocabulary Object Detection in UAV Imagery via Multi-Level Text-Guided Decoding by Lijie Tao, Guoting Wei, Zhuo Wang, Zhaoshuai Qi, Ying Li, Haokui Zhang

“…Experiments conducted on two widely used benchmark datasets demonstrate that our approach achieves significant improvements in both mean mAP and Recall. For instance, for Zero-Shot Detection on xView, UAV-OVD achieves 9.9 mAP and 67.3 Recall, 1.1 and 25.6 higher than that of YOLO-World. …”
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A Comparative Analysis of Deep Learning Methods for Ghaf Tree Detection and Segmentation from UAV-based Images by H. Shanableh, M. B. A. Gibril, A. Mansour, A. Dixit, R. Al-Ruzouq, R. Al-Ruzouq, N. Hammouri, F. Lamghari, S. M. Ahmed, R. Jena, T. Mohamed, M. A. Almarzouqi, N. S. Alafayfeh, S. Z. Ghebremeskel

“…Results demonstrated strong potential for the assessed instance segmentation architectures in mapping Ghaf trees, achieving mean average precision values of 80% to 84.2% for detection and 82.2% to 85.1% for segmentation, with F1-scores ranging from 83.55% to 88.3% for detection and 85.5% to 88.6% for segmentation. …”
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Highly accurate anomaly based intrusion detection through integration of the local outlier factor and convolutional neural network by Rahimullah Rabih, Hamed Vahdat-Nejad, Wathiq Mansoor, Javad Hassannataj Joloudari

“…Abstract Intrusion detection systems (IDS) are critical for safeguarding computer networks by identifying malicious activities. …”
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Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting by Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht, Ahmed El Hilali Alaoui, Yakub Kayode Saheed

“…Network Intrusion Detection Systems (NIDS) face significant challenges in identifying rare attack instances due to the inherent class imbalance and diversity in network traffic. …”
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A Dual-Strategy Framework for Cyber Threat Detection in Imbalanced, High-Dimensional Data Across Heterogeneous Networks by T. Saranya, S. Indra Priyadharshini

“…As cyber threats grow in complexity, ensuring robust network security has become increasingly critical. Intrusion Detection Systems (IDS) serve as a key defense mechanism, detecting potential threats and unauthorized activities that may evade traditional firewalls. …”
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Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study by Hongbo Chen, Myrtede Alfred, Eldan Cohen

“…ObjectiveWe aimed to investigate the efficacy of ICL in detecting stigmatizing language within EHRs under data-scarce conditions. …”
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A machine learning based authentication and intrusion detection scheme for IoT users anonymity preservation in fog environment. by Khondokar Oliullah, Md Whaiduzzaman, Md Julkar Nayeen Mahi, Tony Jan, Alistair Barros

“…Furthermore, we introduce a staked ensemble learning approach for intrusion detection that achieves 99.86% accuracy, 99.89% precision, 99.96% recall, and a 99.91% F1-score in detecting anomalous instances, with a support count of 50,376. …”
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