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Topological Coherence Preserving Retinal Vessel Segmentation 13

Topological Coherence Preserving Retinal Vessel Segmentation

引用

13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Mao, Meiyan Lin, Jiawen Liu, Shilin College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350319156

Retinal vessel segmentation is a critical step in computer-aided diagnosis of retinal images, providing rich information of vascular morphology. However, existing works primarily focus on achieving high overall segmentation accuracy, often overlooking the errors in the topological structure that lead to disconnected vessels. In order to address these issues, a topological coherence preserving retinal vessel segmentation method is proposed. We adopt a two-stage model by cascading a refinement step after the typical segmentation network to enhance retinal vessel connectivity. A topology-preserving loss is then introduced to guide the refinement network to pay more attention to key regions with topological differences, allowing it to learn the correct topology structure and maximize the preservation of retinal vessel tree connectivity. Experimental results on the DRIVE, STARE, and CHASE_DB1 datasets demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Topology

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Uneven Hybrid Clickstream Generation Based on Multi-Layer Perceptron 11

Uneven Hybrid Clickstream Generation Based on Multi-Layer Pe...

引用

11th International Conference on Advanced Cloud and Big data, CBD 2023

作者： Liu, Shen Yu, Zhiyong Huang, Fangwan Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350345346

Online social networking is prevalent around the world, meanwhile, social bots damage users' experience by stealing user privacy, sending spam, spreading malicious links, launching DDoS attacks, et al. In this paper, we use social bots to automatically manipulate social accounts based on generated clickstreams to demonstrate the vulnerability of social networking sites and the capability of social bots. Clickstreams are the sequential click events of users or bots browsing a website. Existing research on clickstream generation assumes that the intervals between all click events are equal, which makes it difficult to fully capture the relationship between uneven intervals. To this end, we propose an uneven hybrid clickstream generation algorithm based on multi-layer perceptron (UHCG-MLP), which can generate click events and intervals simultaneously by dealing with uneven intervals. UHCG-MLP firstly transforms each clickstream in the training set into a sequence of discrete elements separated by coded interval, and uses multi-layer perceptron to mine user behavior patterns from these sequences. Then it generates hybrid clickstreams according to the prediction of the trained model. Experiments on the Twitter platform using the clickstreams generated by the proposed model and the other five benchmark models show that UHCG-MLP achieves the best execution effect. © 2023 IEEE.

关键词： Denial-of-service attack

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Joint Task Assignment and Migration in Cloud-Edge-End Collaborative Computing Based on DRL

Joint Task Assignment and Migration in Cloud-Edge-End Collab...

引用

2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Zhang, Xiaoqi Cheng, Hongju Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350357677

By effectively assigning and migrating tasks based on service requirements, the success rate of task execution in cloud-edge-end collaborative computing can be significantly enhanced, thereby ensuring the provision of high-quality services for users. The majority of conventional cloud-edge-end task offloading approaches primarily focus on static scenarios, posing challenges in ensuring the success rate of task execution in mobile scenarios. It is imperative to address the problem of constructing a joint optimization scheme for task allocation and migration that is suitable for mobile scenarios. This paper re-define the latency, energy, and migration model for task processing in mobile scenarios. Furthermore, we propose a Deep Reinforcement learning (DRL)-based Task allocation and Migration optimization algorithm (DRTM) to enhance the efficiency of task completion and minimize the total cost. DRTM introduces the traditional Actor-Critic with a mirror deep deterministic policy gradient (DDPG) and establishes a duel Q-network to update parameters on respective gradients for optimal policy acquisition. DRTM incorporates two target networks to effectively improve stability and convergence speed during training while reducing computational complexity. The experimental results demonstrate that DRTM can offer a high-performance task assignment and migration scheme in mobile scenarios, thereby significantly reducing the total cost of the task execution life cycle. © 2023 IEEE.

关键词： Reinforcement learning

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MSSCE-GAN: Multi-Scale Structural and Color Enhanced Generative Adversarial Network for Unpaired Underwater Image Enhancement 5

MSSCE-GAN: Multi-Scale Structural and Color Enhanced Generat...

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5th International Conference on Frontiers Technology of Information and computer, ICFTIC 2023

作者： Zhang, Lingxin Chen, Youkun Lan, Jie Niu, Yuzhen Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350309034

Enhancing underwater images is pivotal for applications like underwater exploration. While most existing methods require paired underwater and reference images for training, acquiring such reference images is difficult. Current techniques often suffer from information loss, resulting in blurred details and lack generalizability. This paper introduces an unpaired underwater image enhancement method using the Multi-Scale Structural and Color Enhanced Generative Adversarial Network (MSSCE-GAN). We also introduce a detail feature recovery module and an attention enhancement module, targeting multiple distortions in underwater imagery. Importantly, our method can produce superior enhanced images without paired training data. Experimental evaluations demonstrate our approach outperforms existing techniques in both effectiveness and generalizability on multiple underwater image datasets. © 2023 IEEE.

关键词： component deep learning Generative Adversarial Network unpaired underwater image enhancement

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An Improved Deep Reinforcement Learning for Robot Navigation 3

An Improved Deep Reinforcement Learning for Robot Navigation

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3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Zheng, Qifeng Huang, Xiaogang Dong, Chen Liu, Yuting Cheng, Dong College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9781510664814

Collision-free navigation is an important research direction for multi-robot systems, in which the two core problems are navigating to the target point and avoiding other robots. Many researchers use deep reinforcement learning as navigation strategy to realize multi-robot collision avoidance navigation. However, most of them use raw sensor information or global state information of the agent as the neural network input, which is not conducive to extending the navigation strategy to a larger space. This paper proposes an improved deep reinforcement learning navigation strategy, which enables robots to learn navigation and collision avoidance strategies more accurately. This strategy converts the interactive environment state from the global coordinate representation to the relative vector representation, and attenuates the influence of the rear irrelevant agents on the collision avoidance strategy. Experimental results show that the proposed method outperforms existing learning-based methods in three indicators: success rate, additional time to reach the target, and model convergence speed. © 2023 SPIE.

关键词： Multipurpose robots

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Mixed Attention Interleaved Execution Cascade Architecture for Fittings Instance Segmentation in Overhead Transmission Line Images 4

Mixed Attention Interleaved Execution Cascade Architecture f...

引用

4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Yang, Yilong Zhong, Shangping Chen, Kaizhi Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350324303

Recognition and localization of the fittings in overhead transmission line (OTL) images are the basis of fittings status detection and fault diagnosis. Most of the current recognition and localization of the fittings are based on object detection methods, which cannot accurately locate the fittings, and existing instance segmentation methods have limited accuracy for instance segmentation of fittings in complex OTL scenarios. To solve these problems, in the interleaved execution part, we inherit the idea of Hybrid Task Cascade (HTC) and add a direct information path to the same-stage box and mask branches to reinforce the coupled relationship between detection and segmentation;in the mask branch part, we sequentially apply the efficient channel attention (ECA) module and the dilated spatial attention (DSA) module and then insert them into the mask branch to improve the cross-stage information communication in the cascade architecture and mask prediction. Combining them results in Mixed Attention Interleaved Execution Cascade (MAIEC), a new cascade architecture for instance segmentation. Extensive experiments on the OTL fittings dataset reveal the effectiveness of the proposed method. The proposed MAIEC improves the AP of the box and mask predictions by respectively 2.0% and 1.5% compared to the strong HTC baseline. © 2023 IEEE.

关键词： Object detection

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An Intelligent Analysis Method for Multi-Stage Web Attack Forensics 20

An Intelligent Analysis Method for Multi-Stage Web Attack Fo...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Liu, Yanhua Xu, Yijie Han, Yuting Liu, Zhihuang College of Computer and Data Science Fuzhou University Fuzhou China College of Computer National University of Defense Technology Fuzhou China

ISBN: (纸本)9798350356328

In this paper, we propose IVMWAF, a framework for intelligent analysis of multi-stage web attacks which uses artificial intelligence and visualization approaches. The IVMWAF framework is designed to provide a general and practical forensic process for investigating multi-stage web attacks. Using IVMWAF, digital forensics analysts can have a flexible and intuitive understanding of the overall situation of complex web security incidents. Specifically, in order to extract all web security incident related anomalies, we first propose a multi-level anomaly detection method, by dividing different levels based on different stages of the web attack chain. We also design methods for visualizing and making human-computer interactions for different levels of anomaly detection, to assist analysts in comprehending and providing feedback on the decisions made by intelligent methods. For forensic analysis of web attack incidents, we integrate multi-level anomaly detection and visualization for correlation analysis, and propose a method for constructing multi-level web attack scenarios. Finally, we developed a prototype system and validated the usability and superiority of the proposed IVMWAF with experimental results and expert evaluations on a dataset during a real enterprise security incident. © 2024 IEEE.

关键词： Cyber attacks

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Capturing Spatial-Temporal Correlations with Attention Based Graph Convolutional Networks for Network Traffic Prediction 15

Capturing Spatial-Temporal Correlations with Attention Based...

引用

15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Wan, Xia Peng, Yufei Hao, Run Guo, Yingya College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350329360

Network traffic prediction is essential and significant to network management and network security. Existing prediction methods cannot well capture the temporal-spatial correlations hidden in the network traffic and suffer from a prediction accuracy degradation for two reasons. First, existing approaches concentrate on capturing the spatial relations and dependence in an explicit network topology graph, which fails to reflect the inherent relations in network traffic. Second, the common recurrent neural network models which are leveraged to learn the temporal relations in network traffic exhibit a poor performance in long-term prediction for its limited receptive field. To tackle these two problems, we propose an Attention-based Graph Convolutional Network model (AGCN) for capturing both the spatial and temporal correlations in network traffic. To catch the hidden spatial dependencies in network traffic, we combine graph attention network with graph convolutional network to mine the spatial relationships of network traffic. To efficiently learn the temporal long-term relations embedded in network traffic, we design a dilated convolution module to enable an exponentially growing receptive field for handling long sequences. Experimental results on three network traffic datasets show that AGCN has excellent performance in terms of prediction accuracy and inference time compared to current mainstream methods. © 2023 IEEE.

关键词： Convolution

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Offline Signature Forgery Detection Based on Geometric Measures Using Tensorflow Model 2

Offline Signature Forgery Detection Based on Geometric Measu...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Lakshmi, A. Anagha Reddy, G. Siddarth Reddy, M. Sowmya Kathirisetty, Nikhila Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The most popular method for identifying people from past signatures is through signatures. By using a TensorFlow model which is a deep learning algorithm, we created a new system to verify signatures on bank checks and other important documents. The main point is that a signature plays a crucial role in banking because it is necessary for money withdrawals. In banks there are no efficient systems to check if a signature is real or fake as it is the most frequently used bio-metric method to verify a person's identity. This can lead to bank fraud. The Project will make it easier to tell whether a signature is genuine or not. Online and offline verification are the two methods of verification. We're going to use various geometric measurements to accomplish offline verification. Python libraries were employed in this case. In the testing phase the model's performance is evaluated. To protect the integrity and legitimacy of handwritten signatures, signature forgery detection is an essential responsi-bility. The capacity to recognize forged signatures with accuracy has grown more important as a result of the rise in digital transactions and the growing reliance on electronic documents. The act of duplicating or copying another person's signature with the purpose to deceive or gain illegal access is known as signature forging. Manually spotting these forgeries can be difficult because expert forgers are adept at closely imitating the visual style and traits of real signatures. Therefore, there has been a lot of interest in the development of automated methods and algorithms to identify fake signatures. © 2024 IEEE.

关键词： data mining

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Chaotic Resilience: Enhancing IoT Security Through Dynamic data Encryption 8th

Chaotic Resilience: Enhancing IoT Security Through Dynamic D...

引用

8th International Symposium on Intelligent Informatics, ISI 2023

作者： Francis, E. Geo Sheeja, S. Depertment of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Data Science Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (纸本)9789819721467

In the ever-expanding landscape of the Internet of Things (IoT), safeguarding the security and privacy of data transmissions among IoT devices has become increasingly critical. This research introduces an innovative approach known as "Chaotic Resilience" to bolster the security of data exchanged between IoT devices. While traditional encryption methods are effective, they often rely on fixed cryptographic keys, making them susceptible to attacks over time. Chaotic Resilience introduces an element of dynamic unpredictability into the encryption process, making it considerably more resistant to brute-force attacks and key extraction techniques. The proposed technique involves the integration of chaotic systems, such as the 4D chaotic map, into the encryption process. These chaotic systems generate an ongoing sequence of unpredictable values, which are then combined with the plaintext data through a dynamic encryption algorithm. The result is ciphertext that becomes highly sensitive to initial conditions and practically immune to standard cryptographic attacks. Moreover, the dynamic nature of the encryption process ensures that even if an attacker gains access to the encryption algorithm, they cannot predict future encryption keys. Through extensive simulations and experiments, we demonstrate the effectiveness of Chaotic Resilience in thwarting various attack scenarios, including brute-force attacks, chosen-plaintext attacks, and known-plaintext attacks. Additionally, Chaotic Resilience demonstrates a 20% increase in encryption speed and a 15% reduction in resource utilization, establishing it as an efficient and robust IoT security solution. Our results underscore that Chaotic Resilience significantly enhances the security and resilience of IoT data transmissions, offering a promising avenue for strengthening IoT security in an increasingly interconnected world. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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