Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model by Lijun Gao, Tiantian Ran, Hua Zou, Huanhuan Wu

“…It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. …”
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Hybrid Optimization Method for Social Internet of Things Service Provision Based on Community Detection by Bahar Allakaram Tawfeeq, Amir Masoud Rahmani, Abbas Koochari, Nima Jafari Navimipour

“…The average response time of IGBSA‐CD presents that it is efficient in all three tasks is 0.04 s. It also has a consistently lower response time, even when the task is complex. …”
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LN-DETR: cross-scale feature fusion and re-weighting for lung nodule detection by Dibin Zhou, Honggang Xu, Wenhao Liu, Fuchang Liu

“…However, challenges remain in deploying these methods in complex real-world scenarios. This paper introduces an enhanced lung nodule detection algorithm base on RT-DETR, called LN-DETR. …”
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Trade-off between toxicity and signal detection orchestrated by frequency- and density-dependent genes. by Laury Arthaud, Selim Ben Rokia-Mille, Hussein Raad, Aviv Dombrovsky, Nicolas Prevost, Maria Capovilla, Alain Robichon

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Research on Target Detection Algorithm for Solder Joint Defects Based on the Improved YOLOv8 by Chengkai Zhang, Xiaocui Feng, Pujun Mao, Fuyan Lv

“…Aiming at the problem of low accuracy in solder joint defect detection caused by the complex background and difficult to extract defect features of circuit boards using through-hole technology (THT), an improved YOLOv8 solder joint defect target detection algorithm was proposed. …”
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LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks by Shaymaa E. Sorour, Mohammed Aljaafari, Amany M. Shaker, Ahmed E. Amin

“…This research introduces a novel framework that enhances intrusion detection in IoT systems through a decentralized approach. …”
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Detect+Track: robust and flexible software tools for improved tracking and behavioural analysis of fish by Abhishek Dutta, Natalia Pérez-Campanero, Graham K. Taylor, Andrew Zisserman, Cait Newport

“…The modular workflow is adaptable to new experimental designs, supports manual correction and retraining for new object classes and allows efficient large-scale batch processing. By addressing key limitations of existing tracking tools, Detect+Track provides a flexible and generalizable solution for researchers studying movement and decision-making in complex environments. …”
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Characterising Payload Entropy in Packet Flows—Baseline Entropy Analysis for Network Anomaly Detection by Anthony Kenyon, Lipika Deka, David Elizondo

“…A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. …”
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YOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences by Nada Alzahrani, Ouiem Bchir, Mohamed Maher Ben Ismail

“…This paper introduces YOLO-Act, a novel spatiotemporal action detection model that enhances the object detection capabilities of YOLOv8 to efficiently manage complex action dynamics within video sequences. …”
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CoroYOLO: a novel colorectal cancer detection method based on the Mamba framework by Wenfei Chen, Fengrui Hou, Yika Shen

“…However, CNNs have limitations in capturing global contextual information, and while transformers can handle long-range dependencies, their high computational complexity limits their efficiency in practical applications. …”
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Research on the detection of obstacles in front of unmanned vehicles in opencast mines based on binocular vision by Shunling RUAN, Huiguo ZHANG, Qinghua GU, Caiwu LU, Di LIU, Jing MAO

“…Driverless cars in the complex environment of the open pit mining area encountered falling rocks, puddles, pedestrians and other obstacles have great safety hazards, easy to cause vehicle rollover, stuck in the car, resulting in the risk of loss of property or pose a threat to the safety of personnel, therefore, the complex and changing terrain environment on the road of the open pit mine as an important problem solving of the open pit mine unmanned vehicles in the mining intelligence, the need to measure the depth of obstacles in front of the car of the while guaranteeing the accuracy and speed of obstacle detection. …”
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Unsupervised Hybrid VAE-Based Anomaly Detection for Vehicle Onboard LiDAR Sensors by Nourhen Sboui, Hakim Ghazzai, Mohamed Hadded, Mourad Elhadef, Gianluca Setti

“…The model strikes a good balance between complexity and efficiency, achieving a 10% accuracy improvement compared to existing models, with an accuracy of 95.1% and an F1-score of 82.6%. …”
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YOLO-WAS: A Lightweight Apple Target Detection Method Based on Improved YOLO11 by Xinwu Du, Xiaoxuan Zhang, Tingting Li, Xiangyu Chen, Xiufang Yu, Heng Wang

“…To overcome the limitations of existing apple target detection methods, including low recognition accuracy of multi-species apples in complex orchard environments and a complex network architecture that occupies large memory, a lightweight apple recognition model based on the improved YOLO11 model was proposed, named YOLO-WAS model. …”
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TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection by Rijun Wang, Rijun Wang, Yesheng Chen, Fulong Liang, Xiangwei Mou, Xiangwei Mou, Guanghao Zhang, Hao Jin

“…Current tomato leaf disease detection methods, however, encounter challenges in extracting multi-scale features, identifying small targets, and mitigating complex background interference. …”
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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…Our model’s architecture is not just a testament to the sophistication of modern deep learning techniques but also highlights the novelty of applying such complex structures to the challenges of agricultural disease detection. …”
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Leveraging FastViT based knowledge distillation with EfficientNet-B0 for diabetic retinopathy severity classification by Jyotirmayee Rautaray, Ali B.M. Ali, Meenakshi Kandpal, Pranati Mishra, Rzgar Farooq Rashid, Farzona Alimova, Mohamed Kallel, Nadia Batool

“…This study presents FastEffNet, a novel framework that leverages transformer-based knowledge distillation (KD) to enhance DR severity classification while reducing computational complexity. The proposed approach employs FastViT-MA26 as the teacher model and EfficientNet-B0 as the student model, striking the ideal mix between accuracy and computational efficiency. …”
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An Improved Convolutional Neural Network (CNN) for Disease Detection and Diagnosis for Multi-crop Plants by Florence Choong Chiao Mei, Bryan Ng Jan Hong

“…However, many detection methods are complex, require high computational power and time to perform the required analysis and focus only on a particular species or strain of the disease. …”
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A Non-Contact AI-Based Approach to Multi-Failure Detection in Avionic Systems by Chengxin Liu, Michele Ferlauto, Haiwen Yuan

“…The increasing electrification and integration of advanced controls in modern aircraft designs have significantly raised the number and complexity of installed printed circuit boards (PCBs), posing new challenges for efficient maintenance and rapid failure detection. …”
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Cyberattack Detection Systems in Industrial Internet of Things (IIoT) Networks in Big Data Environments by Abdullah Orman

“…Traditional intrusion detection systems (IDSs) often struggle to effectively identify and mitigate complex cyberthreats, such as denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks. …”
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Research on the lightweight detection method of rail internal damage based on improved YOLOv8 by Xiaochun Wu, Shuzhan Yu

“…This model offers efficient and reliable technical support for detecting and classifying internal rail damage in rail flaw detection tasks. …”
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