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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Flexible Adaptive Marine Predator Algorithm and Its Application in Fault Detection for Wind Turbines by WANG Wen, YI Jiabiao

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Real-Time Runway Detection Using Dual-Modal Fusion of Visible and Infrared Data by Lichun Yang, Jianghao Wu, Hongguang Li, Chunlei Liu, Shize Wei

“…This study proposes a salient object detection (SOD) method that integrates visible and infrared sensors for robust airport runway detection in complex environments. …”
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Detection of Apple Leaf Gray Spot Disease Based on Improved YOLOv8 Network by Siyi Zhou, Wenjie Yin, Yinghao He, Xu Kan, Xin Li

“…The details are as follows: (1) we introduce Dynamic Residual Blocks (DRBs) to boost the model’s ability to extract lesion features, thereby improving detection accuracy; (2) add a Self-Balancing Attention Mechanism (SBAY) to optimize the feature fusion and improve the ability to deal with complex backgrounds; and (3) incorporate an ultra-small detection head and simplify the computational model to reduce the complexity of the YOLOv8 network while maintaining the high precision of detection. …”
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MSDNet: A Multi-Scale Feature Representation Network Model for Tunnel Bolt Detection by Xiufeng Wu, Xueli Li, Guangxing Cao, Chao Guo, Wei Chang, Zhao Wang, Shuliang Liu

“…To address these issues, we introduces a novel multi-scale feature extraction detection network (MSDNet) designed to improve tunnel bolt maintenance by reducing false positives and missed detections. …”
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InvMOE: MOEs Based Invariant Representation Learning for Fault Detection in Converter Stations by Hao Sun, Shaosen Li, Hao Li, Jianxiang Huang, Zhuqiao Qiao, Jialei Wang, Xincui Tian

“…Converter stations are pivotal in high-voltage direct current (HVDC) systems, enabling power conversion between an alternating current (AC) and a direct current (DC) while ensuring efficient and stable energy transmission. Fault detection in converter stations is crucial for maintaining their reliability and operational safety. …”
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The Scalable Detection and Resolution of Data Clumps Using a Modular Pipeline with ChatGPT by Nils Baumgartner, Padma Iyenghar, Timo Schoemaker, Elke Pulvermüller

“…This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. …”
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YOLOv11-GSF: an optimized deep learning model for strawberry ripeness detection in agriculture by Haoran Ma, Qian Zhao, Runqing Zhang, Chunxu Hao, Wenhui Dong, Xiaoying Zhang, Fuzhong Li, Xiaoqin Xue, Gongqing Sun

“…The challenge of efficiently detecting ripe and unripe strawberries in complex environments like greenhouses, marked by dense clusters of strawberries, frequent occlusions, overlaps, and fluctuating lighting conditions, presents significant hurdles for existing detection methodologies. …”
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Enhancing image quality in circular-view photoacoustic tomography using randomized detection points by Soheil Hakakzadeh, Praveenbalaji Rajendran, Zahra Kavehvash, Manojit Pramanik

“…Circular-view (circular scan) photoacoustic computed tomography (PACT) with low-density detection points (DPs) is an efficient, high-speed, and inexpensive modality with numerous (pre-) clinical applications. …”
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Radio Frequency Interference Detection Using Swin Transformer Embedding U2-Net by Shengdi Chen, Bo Liang

“…The main encoder enhances the feature representation through the efficient multiscale attention (EMA) mechanism, reorganizes the channel information, captures pixel-level relationships, and improves the detection accuracy in complex backgrounds. …”
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YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model by Shuang Liu, Haobin Xu, Ying Deng, Yixin Cai, Yongjie Wu, Xiaohao Zhong, Jingyuan Zheng, Zhiqiang Lin, Miaohong Ruan, Jianqing Chen, Fengxiang Zhang, Huiying Li, Fenglin Zhong

“…Bitter melon, an important medicinal and edible economic crop, is often threatened by diseases such as downy mildew, powdery mildew, viral diseases, anthracnose, and blight during its growth. Efficient and accurate disease detection is of significant importance for achieving sustainable disease management in bitter melon cultivation. …”
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Comparative Analysis of Input Image Characteristics in Convolutional Neural Network-based Signature Detection by M. Adamec, M. Turcanik

“…The utilization of the NxN scalable format for machine code instruction representation results in enhanced accuracy, accelerated training, and a considerable reduction in pixel usage, indicating a promising avenue for optimizing the efficiency of malware detection.…”
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MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images by Huinan Guo, Congying Sun, Jing Zhang, Wuxia Zhang, Nengshuang Zhang

“…To overcome the limitations of existing fusion methods in terms of complexity and insufficient information utilization, we innovatively propose a Cosine Similarity-based Image Feature Fusion (CSIFF) module and integrate it into a dual-branch YOLOv8 network, constructing a lightweight and efficient target detection network called Multi-Modality YOLO Fusion Network (MMYFNet). …”
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E-DoH: elegantly detecting the depths of open DoH service on the internet by Cong Dong, Jiahai Yang, Yun Li, Haoran Jiao, Chenglong Li, Xia Yin, Yuling Liu

“…Through experiments, our approach demonstrates a remarkable 80% improvement in time efficiency and only requires 4–20% traffic volume to complete the detection task. …”
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