Security anomaly detection for enterprise management network based on attention mechanism by Zhaohan You, Yucai Zheng

“…It is an enterprise management network security anomaly detection method based on deep learning, aiming to achieve efficient and intelligent monitoring and management of security anomaly data in enterprise management network. …”
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LiteTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories by Wenshuai Liu, Qingzheng Liu, Wenyong Quan, Junli Wang, Xiaomin Yao, Qiang Liu, Yuxiang Tian

“…The proposed LiteTom-RTDETR model was shown to balance high tomato identification accuracy (88.2%) with an excellent real-time inference speed (52.2 frames per second) and computational efficiency (36.3 GFLOPs). Notably, the average detection accuracy of LiteTom-RTDETR was 0.6% higher, its detection speed was 15.5% faster, and its computational load and model size were 36.2% and 31.6% smaller, respectively, than those of the original RTDETR model. …”
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Insulator Defect Detection Algorithm Based on Improved YOLOv11n by Junmei Zhao, Shangxiao Miao, Rui Kang, Longkun Cao, Liping Zhang, Yifeng Ren

“…Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. …”
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Enhancing Cybersecurity: Hybrid Deep Learning Approaches to Smishing Attack Detection by Tanjim Mahmud, Md. Alif Hossen Prince, Md. Hasan Ali, Mohammad Shahadat Hossain, Karl Andersson

“…Experimental results demonstrate that the proposed CNN-Bi-GRU model outperforms existing approaches, achieving an overall highest accuracy of 99.82% in detecting SMS phishing messages. This study provides an empirical analysis of the effectiveness of hybrid deep learning techniques for SMS phishing detection, offering a more precise and efficient solution to enhance cybersecurity in mobile communications.…”
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Contrastive Learning Algorithm for Low-Resource Cryptographic Attack Event Detection by Peng Luo, Rangjia Cai, Yuanbo Guo

“…Additionally, assigning appropriate weights to keywords of different event types and employing clustering methods for effective classification reduce reliance on manually labeled data, thereby facilitating the training of an efficient cryptographic attack event detection model. …”
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Deep learning-assisted terahertz intelligent detection and identification of cancer tissue by Xingyu Wang, Yafei Xu, Rong Wang, Nuoman Tian, Zhengpeng Zhu, Shuting Fan, Liuyang Zhang, Ruqiang Yan, Xuefeng Chen

“…By combining the THz detection technique and artificial intelligence technique, here we propose a dense and efficient channel attention network (DECANet) framework-based THz diagnosis system for cancer prescreening. …”
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Anomaly detection in encrypted network traffic using self-supervised learning by Sadaf Sattar, Shumaila Khan, Muhammad Ismail Khan, Ainur Akhmediyarova, Orken Mamyrbayev, Dinara Kassymova, Dina Oralbekova, Janna Alimkulova

“…Compared with existing methods, ET-SSL does not rely on labeled data, scales better, and detects zero day attack in dynamic network environment more effectively, serving as a paradigm for private and energy efficient anomaly detection in encrypted traffic.…”
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Optimal resource allocation for two-stage connectionless access with collision detection by Xin JIAN, Fang WANG, Jian SONG, Shu FU, Xiaoheng TAN, Xiaoping ZENG

“…Connectionless access allows massive machine type communication (mMTC) devices to transmit small packets without establishment of radio bearers,significantly reducing device power consumption and control signaling overhead.Two-stage connectionless access (TSCLA) improves throughput and resource efficiency by optimally allocating resources between scheduling request (SR) phase and data transmission phase,which can be used for bigger packets and high traffic load.Based on this,a comprehensive theoretical analysis of one kind of TSCLA with collision detection was conducted to investigate its performance limit and devise its optimal resource allocation scheme.In addition,to avoid the complexity of user number estimation,a dynamic resource allocation algorithm with feedback control was proposed.Numerical results are provided to validate the effectiveness of aforementioned theoretical results and show that comparing with the genie aided algorithm known exactly the number of users,the performance loss of the proposed algorithm is within 4%.These works together provide good references for appropriate resource dimensioning for mMTC related protocols.…”
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A Novel Approach to Speed Up Hampel Filter for Outlier Detection by Mario Roos-Hoefgeest Toribio, Alejandro Garnung Menéndez, Sara Roos-Hoefgeest Toribio, Ignacio Álvarez García

“…This paper proposes a novel Hampel filter variant that replaces the MAD with an original estimator (mMAD) that retains statistical robustness but is computationally more efficient. This reduces the filter’s computational complexity from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>N</mi><mo>·</mo><mi>w</mi><mo form="prefix">log</mo><mi>w</mi><mo>)</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>N</mi><mo>·</mo><mi>w</mi><mo>)</mo></mrow></semantics></math></inline-formula>, where <i>N</i> is the data length and <i>w</i> the window size. …”
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A Small Target Pedestrian Detection Model Based on Autonomous Driving by Yang Zhang, Shuaifeng Zhang, Dongrong Xin, Dewang Chen

“…Finally, a cascaded autofocus query module is proposed to increase the inference speed of the feature pyramid network through focusing and querying, thus improving the performance and efficiency of small-target pedestrian detection. The experimental results show that the proposed model achieves better detection results than previous models.…”
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Driver Distraction Detection in Extreme Conditions Using Kolmogorov–Arnold Networks by János Hollósi, Gábor Kovács, Mykola Sysyn, Dmytro Kurhan, Szabolcs Fischer, Viktor Nagy

“…The results suggest that KANs can enhance driver distraction detection under challenging conditions, with improved resilience against adversarial attacks, particularly in low-complexity networks.…”
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Lightweight network for insulator fault detection based on improved YOLOv5 by Dehua Weng, Zhiliang Zhu, Zhengbing Yan, Moran Wu, Ziang Jiang, Nan Ye

“…To address these issues, we introduce a novel one-stage network that enables real-time detection of insulator faults on mobile devices. We designed a new module that optimises the computational complexity of networks and fused the module with the attention mechanism SimAM to solve the problem of low efficiency in detecting flashover faults. …”
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A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots by Fei Yuan, Jinpeng Wang, Wenqin Ding, Song Mei, Chenzhe Fang, Sunan Chen, Hongping Zhou

“…To enhance detection accuracy and satisfy the deployment constraints of edge devices, we propose YOLOv10n-CGD, a lightweight and efficient dragon fruit detection method designed for robotic harvesting applications. …”
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Weed detection in cornfields based on improved lightweight neural network model by Haicheng Wan, Shanping Wang

“…Based on the original YOLOv4, an improved lightweight weed detection model, YOLOv4-weeds, is proposed. By changing the main YOLOv4 feature extraction network into MobileNetV3-Small, combined with the depthwise separable convolution and inverse residual structure, and introducing a lightweight attention mechanism, this model reduces the image processing memory size and improves detection efficiency. …”
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Spectral Clustering-Guided News Environments Perception for Fake News Detection by Kun Wang, Yuzhen Yang, Xiaoyang Wang

“…Then, we introduce a shared parameter multitask learning framework that treats different news environments as independent tasks and architects GRU bootstrapping modules with attention mechanisms to help aggregate features from different environments efficiently and interpretably. Finally, we provide textual perspective and stylistic perspective approaches during the detection process and soften the loss terms in multiple environments to alleviate the strict constraints, thus making it more compatible with the fake news detection task. …”
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Enhancing credit card fraud detection: highly imbalanced data case by Dalia Breskuvienė, Gintautas Dzemyda

“…It has been specifically designed to efficiently process and analyze vast and complex datasets commonly encountered in the financial sector, showcasing adaptability to the dynamic nature of big data environments. …”
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LNT-YOLO: A Lightweight Nighttime Traffic Light Detection Model by Syahrul Munir, Huei-Yung Lin

“…A novel SEAM attention module is proposed to refine the features that represent both the spatial and channel information by leveraging the features from the Simple Attention Module (SimAM) and Efficient Channel Attention (ECA) mechanism. The HSM-EIoU loss function is also proposed to accurately detect a small traffic light by amplifying the loss for hard-sample objects. …”
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A Defect Detection Algorithm for Optoelectronic Detectors Utilizing GLV-YOLO by Xinfang Zhao, Qinghua Lyu, Hui Zeng, Zhuoyi Ling, Zhongsheng Zhai, Hui Lyu, Saffa Riffat, Benyuan Chen, Wanting Wang

“…Compared to other methods, our approach outperforms them in both performance and efficiency, fulfilling the real-time and precise defect detection needs of photodetectors.…”
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CGTS: graph transformer-based anomaly detection in controller area networks by Xue Zhou, Guihe Qin, Yanhua Liang, Jiaru Song, Wanning Liu, Qingxin Liu

“…These results highlight CGTS can effectively detect multiple injection attacks and significantly improve the CAN bus intrusion detection performance.…”
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Artificial Intelligence in Wind Turbine Fault Detection and Diagnosis: Advances and Perspectives by Nejad Alagha, Anis Salwa Mohd Khairuddin, Zineddine N. Haitaamar, Obada Al-Khatib, Jeevan Kanesan

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