Efficient DDoS attack detection and prevention scheme based on SDN in cloud environment by Heng HE, Yan HU, Lianghan ZHENG, Zhengyuan XUE

“…For addressing the problem of two typical types of distributed denial of service (DDoS) attacks in cloud environment,a DDoS attack detection and prevention scheme called SDCC based on software defined network (SDN) architecture was proposed.SDCC used a combination of bandwidth detection and data flow detection,utilized confidence-based filtering (CBF) method to calculate the CBF score of packets,judged the packet of CBF score below the threshold as an attacking packet,added its attribute information to the attack flow feature library,and sent the flow table to intercept it through SDN controller.Simulation results show that SDCC can detect and prevent different types of DDoS attacks effectively,and it has high detection efficiency,reduces the controller’s computation overhead,and achieves a low false positive rate.…”
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Auto forensic detecting algorithms of malicious code fragment based on TensorFlow by Binglong LI, Jinlong TONG, Yu ZHANG, Yifeng SUN, Qingxian WANG, Chaowen CHANG

“…In order to auto detect the underlying malicious code fragments in complex，heterogeneous and massive evidence data about digital forensic investigation, a framework for malicious code fragment detecting algorithm based on TensorFlow was proposed by analyzing TensorFlow model and its characteristics.Back-propagation training algorithm was designed through the training progress of deep learning.The underlying binary feature pre-processing algorithm of malicious code fragment was discussed and proposed to address the problem about different devices and heterogeneous evidence sources from storage media and such as AFF forensic containers.An algorithm which used to generate data set about code fragments was designed and implemented.The experimental results show that the comprehensive evaluation index F₁of the method can reach 0.922, and compared with CloudStrike, Comodo, FireEye antivirus engines, the algorithm has obvious advantage in dealing with the underlying code fragment data from heterogeneous storage media.…”
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A LiDAR - camera fusion detection method based on weight allocation by Kang Haotian, Wang Tianshu

“…In automatic driving target detection problem, the neural network is applied to two methods, vision, and LiDAR. …”
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Real-Time Human Ear Detection Based on the Joint of Yolo and RetinaFace by Huy Nguyen Quoc, Vinh Truong Hoang

“…With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. …”
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A novel pedestrian detection algorithm based on data fusion of face images by Jianhu Zheng, Jinshuan Peng

“…Face recognition is the most popular method to detect and track pedestrian movement. During the face recognition process, feature classification ability and reliability are determined by the feature extraction methods. …”
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A hybrid Bi-LSTM and RBM approach for advanced underwater object detection. by Manimurugan S, Karthikeyan P, Narmatha C, Majed M Aborokbah, Anand Paul, Subramaniam Ganesan, Rajendran T, Mohammad Ammad-Uddin

“…Additionally, this architecture handles variable-length sequences, mitigates the vanishing gradient problem, and achieves enhanced significance by capturing complex patterns in the data. …”
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A DR Detection Method Incorporating Multi-scale Techniques and Parallel Networks by CHEN Yu, XU Shibao

“… A PM-Net model ( Parallel Multi-scale Network) has been constructed to solve the problems of loss of key information and poor model robustness in the downsampling process of diabetic retinopathy ( DR) detection models. …”
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An edge detection method of pantograph carbon slide based on an improved Canny algorithm by YANG Jun, GUO Youmin, WANG Jianxin, ZHAO Hongliang

“…Aiming at the problems of inaccurate extraction, discontinuity and being susceptible to noise when the edge of a carbon slide is detected by using a traditional edge detection algorithm, an edge detection method of pantograph carbon slide based on an improved Canny algorithm was proposed in the paper. …”
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A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection by Xiang Li, Changchang Zeng, Yong Yao, Jide Qian, Haiding Zhang, Sen Zhang, Suixian Yang

“…However, existing methods fail to accurately read the display automatically due to incorrect labeling of the target box vertices, which arises from the vertex entanglement problem. To address these challenges, a novel Quadrilateral Contour Disentangled Detection Network (QCDNet) is proposed in this paper, which utilizes the quadrilateral disentanglement idea. …”
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Ensemble-based eye disease detection system utilizing fundus and vascular structures by Hongjie Yu, Xingbo Dong

“…Traditional diagnostic methods, relying on manually analyzing images, often have problems making an early detection and with their accuracy and efficiency, largely due to the subjectivity of human judgment and the time-consuming nature of the process. …”
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Small target detection of bearing surface defects based on improved YOLOv5s. by Haisong Xu, Xiaolin Shi, Han Zhang, Fan Yang, Yun Chen

“…However, traditional detection methods have problems such as insufficient feature extraction for small targets and sensitivity to background interference. …”
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A novel criterion for crack detection in beam structures by frequency response functions by Nguyen Tien Khiem, Tran Thanh Hai, Le Khanh Toan, Nguyen Thi Lan, Ho Quang Quyet

“…However, the complex feature of FRF has not been thoroughly employed for structural damage detection. …”
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Research on underwater target detection error compensation based on improved binocular vision by Wenhui Wang, Fumeng Ye, Yumin Peng, Shi Dong

“…A novel binocular vision error compensation method is proposed to solve the major error compensation problem caused by nonlinear distortion of camera and projector on underwater target detection. …”
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Detection Of Fire-Prone Areas In Attica Region Integrating Urban And Transport Aspect by Efthymia Kourmpa, Stefanos Tsigdinos

“…In Mediterranean climate areas, wildfires are one of the most critical problems bringing about several negative impacts (loss of human life, infrastructure damages, landscape transformation, natural environment deterioration, etc.). …”
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GCB‐YOLO: A Lightweight Algorithm for Wind Turbine Blade Defect Detection by Zhiming Zhang, Chaoyi Dong, Ze Wei, Xiaoyan Chen, Weidong Zan, Yao Xue

“…To address this problem, this paper introduces a lightweight wind turbine blade defect detection network, GCB‐YOLO, which attempts to maintain high detection accuracy and simultaneously achieve rapid detection speed. …”
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Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background by Junchen Ai, Yadong Li, Shengxiang Gao, Rongsheng Hu, Wengang Che

“…In order to solve the problems such as mutual occlusion of leaves, light disturbance, and small lesion area under complex background, YOLO-SSM, a tea disease detection model, was proposed in this paper. …”
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Cumulative confidence-driven task offloading for object detection in maritime Internet of Things by Yanglong Sun, Wenqian Luo, Weijian Xu, Qiang Mei, Haixia Peng, Linhai Wei

“…However, the dynamic marine network and environmental interference in feature extraction adversely affect detection accuracy and cause delay. …”
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Deep Learning and Its Application in the Field of Rail Transit by XIONG Qunfang, LIN Jun, LIU Yue, YUAN Hao, YOU Jun

“…It introduced the current state of deep learning and its application in the field of rail transit, including identification, driver fatigue detection, lane detection and vehicle recognition equipment fault detection. …”
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Tongue Color Analysis and Diseases Detection Based on a Computer Vision System by Abdulghafor Khudhaer Abdullah, Saleem Lateef Mohammed, Ali Al-Naji, Mohammed Sameer Alsabah

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MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer by Van-Nhan Tran, Hoanh-Su Le, Piljoo Choi, Suk-Hwan Lee, Ki-Ryong Kwon

“…With the rapid advances in deep generative models, the accessibility and sophistication of such manipulation technologies are increasing, making it more challenging to detect fake content. Different facial forgery techniques result in complex data distributions, and most existing deepfake detection approaches rely on convolutional neural networks (CNNs) that treat the task as a binary classification problem. …”
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