Malicious Traffic Detection on Tofino Using Graph Attention Model by Xichang Gao, Lizhuang Tan, Shengpeng Chen, Peiying Zhang, Jian Wang

“…With the surge of malicious traffic in networks, existing detection methods struggle to balance real-time performance and efficiency. …”
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Kneeliverse: A universal knee-detection library for performance curves by Mário Antunes, Tyler Estro, Pranav Bhandari, Anshul Gandhi, Geoff Kuenning, Yifei Liu, Carl Waldspurger, Avani Wildani, Erez Zadok

“…Additionally, Kneeliverse extends these algorithms to detect multiple knees and elbows in complex curves, employing a recursive approach. …”
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Research on Surface Defect Detection of Rare-Earth Magnetic Materials Based on Improved SSD by Bin Zhang, Shuqi Fang, Zhixi Li

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Unsupervised selective labeling for semi-supervised industrial defect detection by Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen

“…This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce annotation costs. This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions. …”
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Enhanced YOLO and Scanning Portal System for Vehicle Component Detection by Feng Ye, Mingzhe Yuan, Chen Luo, Shuo Li, Duotao Pan, Wenhong Wang, Feidao Cao, Diwen Chen

“…In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. …”
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Lightweight highland barley detection based on improved YOLOv5 by Minghui Cai, Hui Deng, Jianwei Cai, Weipeng Guo, Zhipeng Hu, Dongzheng Yu, Houxi Zhang

“…In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. …”
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DVCW-YOLO for Printed Circuit Board Surface Defect Detection by Pei Shi, Yuyang Zhang, Yunqin Cao, Jiadong Sun, Deji Chen, Liang Kuang

“…The accurate and efficient detection of printed circuit board (PCB) surface defects is crucial to the electronic information manufacturing industry. …”
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Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security by Hanadi Hakami, Muhammad Faheem, Majid Bashir Ahmad

“…We compared several ML and DL techniques to detect and address the most efficient one for our pipeline. …”
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An Improved Framework for Content- and Link-Based Web-Spam Detection: A Combined Approach by Asim Shahzad, Nazri Mohd Nawi, Muhammad Zubair Rehman, Abdullah Khan

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Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR by Salma, Maham Saeed, Rauf ur Rahim, Muhammad Gufran Khan, Adil Zulfiqar, Muhammad Tahir Bhatti

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Deep learning algorithms for detecting fractured instruments in root canals by Ekin Deniz Çatmabacak, İrem Çetinkaya

“…Conclusions DenseNet201’s superior performance highlights its clinical potential for FEI detection, while ResNet-18 offers a balance between accuracy and computational efficiency. …”
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A Method for Detecting Tomato Maturity Based on Deep Learning by Song Wang, Jianxia Xiang, Daqing Chen, Cong Zhang

“…Therefore, in this study, an improved YOLOv8 algorithm is proposed to address the problem of tomato fruit ripeness detection in complex scenarios, which is difficult to carry out accurately. …”
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DWS-YOLO: A Lightweight Detector for Blood Cell Detection by Yihai Mao, Hongyi Zhang, Wanqing Wu, Xingen Gao, Zhibin Lin, Juqiang Lin

“…Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. …”
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Multitask semantic change detection guided by spatiotemporal semantic interaction by Yinqing Wang, Liangjun Zhao, Yueming Hu, Hui Dai, Yuanyang Zhang

“…Abstract Semantic Change Detection (SCD) aims to accurately identify the change areas and their categories in dual-time images, which is more complex and challenging than traditional binary change detection tasks. …”
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CUSE-TD Algorithm for Precision Detection of Coherent and Uncorrelated Signals by Rohini Dakulagi, Ravi Raushan, Soham Dutta, Mohammed Theeb Alosaimi, Miguel Villagomez-Galindo

“…The usefulness of the CUSE-TD algorithm lies in its applicability CUSE -TD to real-world MIMO systems, particularly in complex environments such as radar, wireless communications, and surveillance systems, where accurate source detection is critical for operational effectiveness. …”
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Robust UAV Target Tracking Algorithm Based on Saliency Detection by Hanqing Wu, Weihua Wang, Gao Chen, Xin Li

“…In response to this problem, this paper proposes a robust UAV target tracking algorithm based on saliency detection (SDBCF). Using saliency detection methods, the DCF tracker is optimized in three aspects to enhance the robustness of the tracker in complex scenes: feature fusion, filter-model construct, and scale-estimation methods improve. …”
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Multi-granularity Android malware fast detection based on opcode by Xuetao ZHANG, Meng SUN, Jinshuang WANG

“…The detection method based on opcode is widely used in Android malware detection,but it still contains some problems such as complex feature extraction method and low efficiency.In order to solve these problems,a multi-granularity fast detection method based on opcode for Android malware was proposed.Multi-granularity refers to the feature based on the bag of words model,and with the function as basic unit to extract features.By step-by-level aggregation feature,the APK multi-level information is obtained.The log length characterizes the scale of the function.And feature can be compressed and mapped to improve the efficiency and construct the corresponding classification model based on the semantic similarity of the Dalvik instruction set.Tests show that the proposed method has obvious advantages in performance and efficiency.…”
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Method for Detecting Tiny Defects on Machined Surfaces of Mechanical Parts Based on Object Recognition by Haotian Li, Zhen Wang, Lipeng Qiu, Xichu Wei

“…Practical results demonstrate that this method outperforms traditional approaches in terms of missed detection rates and detection efficiency, effectively addressing the challenge of detecting complex machining surface defects, and providing a high-precision, high-efficiency defect detection solution for the mechanical part machining field.…”
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Improving Threat Detection in Wazuh Using Machine Learning Techniques by Samir Achraf Chamkar, Mounia Zaydi, Yassine Maleh, Noreddine Gherabi

“…The increasing complexity and sophistication of cyber threats underscore the critical need for advanced threat detection mechanisms within Security Operations Centers (SOCs) to effectively mitigate risks and enhance cybersecurity resilience. …”
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