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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xu, Shengxiang Yao, Liang Huang, Chenyu Jiang, Guoxin Liu, Fan College of Computer and Software Hohai University Nanjing China Nanjing Research Institute of Electronic Engineering Nanjing China

ISBN: (纸本)9789819610709

The rapidly evolving biometric recognition technology has been widely applied in the detection and diagnosis of biological diseases. However, in the poultry domain, the scarcity of case information and the difficulty in data collection result in lower accuracy in disease diagnosis and treatment. Even though multi-modal large language models possess remarkable capabilities, they cannot be fully relied upon due to the hallucination problem. To address these issues, we propose PoultryAssistant, a novel approach that unifies LLMs with knowledge graphs to enhance diagnostic accuracy in poultry disease diagnosis. Specially, a poultry disease knowledge graph is constructed by utilizing entity-relation extraction algorithms. Then a strategy combining large language models and knowledge graphs is proposed to achieve diagnostic results through multi-source information interaction. Furthermore, extensive experiments on a poultry disease dataset demonstrate the effectiveness of our approach, marking a significant advancement in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Brain tumor detection with mRMR-based multimodal fusion of deep learning from MR images using Grad-CAM

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Iran Journal of computer Science 2023年第3期6卷 245-259页

作者： Özbay, Feyza Altunbey Özbay, Erdal Faculty of Engineering Software Engineering Firat University Elazig 23119 Turkey Faculty of Engineering Computer Engineering Firat University Elazig 23119 Turkey

The brain tumor is considered a deadly cancer disease that can be seen among people of almost all ages. Early diagnosis and treatment by radiologists are very important in terms of diagnosing an average of 14,000 people with brain tumors per year. Magnetic resonance imaging (MRI) is the best technical expert used for tumor detection via computer-aided systems. However, due to the complex tissue characteristics of a large number of images, manual examination with traditional methods may cause various errors. In this study, a hybrid convolutional neural network (CNN) method based on minimum-redundancy maximum-relevance (mRMR) is proposed, which aims to classify brain tumors over brain MRI with machine learning (ML) algorithms. According to the classification results, DarkNet53, EfficientNet-B0, and DenseNet201 pre-trained CNN architectures, which are three architectures that give the best results as feature extractors, were used as hybrids among 10 different deep learning (DL) architectures. By means of these CNN architectures, the features trained on the features obtained by Gradient-weighted Class Activation Mapping (Grad-CAM) are concatenated. The mRMR method has been used to optimize all concatenated features. Then, the optimized features have classified with SVM, KNN, and Ensemble algorithms. DarkNet53, EfficientNet-B0, and DenseNet201 were used as feature extractors and three different machine learning classifiers were used as classifiers. As a result, the success rate of classification of brain MR images using derived feature vectors has been revealed as 99.6% accuracy with the SVM classifier. According to the experimental results, the use of a combination of feature selection approaches and CNN models helped to successfully classify brain MR images. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Brain tumor Classification Deep learning Grad-CAM MRI mRMR

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On Site Personnel Access Monitoring and Early Warning in Photovoltaic Field Based on AI Image Recognition 16

On Site Personnel Access Monitoring and Early Warning in Pho...

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16th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2024

作者： Zheng, Wenjin Wang, Yajun Xu, Xiaochen Chu, Hao Jin, Danyang Fang, Shibiao Powerchina Huadong Engineering Corporation Limited Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350386127

The study aims to enhance the management and monitoring of photovoltaic (PV) construction sites, which are often located in remote areas with challenging conditions. Traditional monitoring methods are inefficient, costly, and prone to errors, necessitating the integration of advanced image recognition technology to improve site management. The research introduces image recognition technology, a core aspect of artificial intelligence (AI) and computer vision (CV), to automate the detection and monitoring of equipment, personnel behavior, materials, and construction progress at PV sites. The process involves real-time data collection via high-definition cameras, automated anomaly detection, reporting, and historical data analysis using advanced deep learning models like YOLO. The study developed an efficient and adaptive image recognition model tailored to the complexities of PV construction sites. The model successfully improves the accuracy of detecting safety hazards, optimizes resource utilization, and enhances the overall safety and efficiency of the construction process. The findings highlight the potential of image recognition technology to significantly improve the management of PV construction sites. As the technology advances and becomes more cost-effective, its application is expected to drive higher levels of efficiency, reliability, and safety in the renewable energy sector, supporting the industry's sustainable growth. © 2024 IEEE.

关键词： Deep learning

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Research on Intelligent Voice Broadcast System for Smart Water Conservancy 19

Research on Intelligent Voice Broadcast System for Smart Wat...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Huang, Zhenqian Chen, Lanxin College of Computer Science and Software Engineering Hohai University Nanjing China University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9798331507817

Smart water management imposes higher demands on the real-time capabilities and intelligence of watershed information monitoring systems. Therefore, we propose an intelligent voice broadcast system for monitoring water information, aimed at achieving efficient and accurate dissemination of warning information. This system can be deployed and integrated into existing water information monitoring systems to enable real-time voice broadcasting of water-related information. To ensure the system’s timeliness and effectiveness, we propose a Performance Optimization Mechanism for Data Processing, Priority Control for voice broadcasting, a Content Optimization Mechanism, and a Timeliness Control Mechanism. Comparative experiments demonstrate that the proposed intelligent voice broadcast system significantly enhance the efficiency and accuracy of water information dissemination, providing an innovative solution for real-time monitoring and early warning in water management. © 2025 IEEE.

关键词： Information management

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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(Information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Combatting the TrickBot Threat: Analysis, Impact, and Defensive Strategies in Cybersecurity 17

Combatting the TrickBot Threat: Analysis, Impact, and Defens...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Cao, Jintao Null, Allie Stewart, Marissa Bhunia, Suman Salman, Mohammad Miami University Department of Computer Science and Software Engineering OxfordOH United States University of Anbar College of Computer Anbar Iraq

ISBN: (纸本)9798350351637

The TrickBot Botnet, emerging in late 2016, has been a significant cybersecurity threat, leveraging sophisticated attack vectors such as phishing emails, network vulnerabilities, and secondary payloads. This paper provides a comprehensive analysis of TrickBot's evolution, attack methodologies, and widespread impact on individual users, businesses, and government entities. Despite some disruptions to its infrastructure, TrickBot has demonstrated remarkable resilience, continually adapting its tactics to evade detection and enhance its malicious capabilities. Our research offers detailed insights into the operational mechanisms of TrickBot, supported by extensive data on its propagation and impact. Furthermore, we evaluate various defense strategies, including advanced technical measures and human-centric approaches, to mitigate the ongoing threat posed by TrickBot and similar botnets. The findings underscore the critical need for continuous vigilance and innovation in cybersecurity practices to effectively counter such persistent threats. © 2024 IEEE.

关键词： Bot (Internet)

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Spam detection with fasttext based features

Spam detection with fasttext based features

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Karadeniz, Talha Tokdemir, Gül Maraş, H. Hakan Department of Software Engineering Çankaya University Ankara Turkey Department of Computer Engineering Çankaya University Ankara Turkey Department of Computer Programming Çankaya University Ankara Turkey

ISBN: (纸本)9798350379433

Fasttext is a powerful word representation method that creates word representations based on vectors of character n-grams. In this work, we propose a method that utilizes fasttext features for a novel feature engineering model for the spam detection problem. In the feature engineering method, the combination of average, mean of second derivative;mean peak and standard deviation of fasttext features are computed. Finally, tf-idf features are also considered for the modeling process. The success of each feature engineering technique is measured and reported. The combination of the five feature extraction methods, tested on two spam detection datasets, yielded promising results with an accuracy of 0.978 on e-mail spam detection and an accuracy of 0.986 on sms spam classification. © 2024 IEEE.

关键词： Support vector machines

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Boosting Write Performance of KV Stores: An NVM -Enabled Storage Collaboration Approach 40

Boosting Write Performance of KV Stores: An NVM -Enabled Sto...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Yi He, Jiajian Sun, Kaoyi Dong, Yunhao Chen, Jiaxian Ma, Chenlin Zhou, Amelie Chi Mao, Rui College of Computer Science and Software Engineering Shenzhen University China Hong Kong Baptist University Department of Computer Science Hong Kong

ISBN: (纸本)9798350317152

As the most common data structure for key-value stores, LogStructured Merge Tree (LSM-tree) can eliminate random write operations and keep acceptable read performance. However, write stall and write amplification introduced by the leveled compaction of LSM-tree significantly degrade the system performance. The emerging non-volatile memory (NVM) provides byte-addressable access and low-latency data persistence. Integrating DIMM-interface NVM in the design of the LSM-tree can potentially alleviate the write stall and write amplification issue, as the access speed of NVM is several orders of magnitude faster than hard disk drives or flash memory-based solid-state drives. This hybrid storage should be carefully designed, requiring new architectural and key-value structural support. This paper presents ZigZagDB, an NVM-enabled data man-agement scheme for LSM-tree-based key-value stores. ZigZagDB adds additional layers of key-value stores and uses non-volatile memory as the storage media to hold these additional layers of data. The newly designed key-value stores alternately access the data from either SSD or NVM. This 'ZigZag' shape of storage collaboration and synchronization can benefit write efficiency and space utilization. By utilizing the NVM with very limited capacity, the redesigned organization of LSM-tree can effectively solve the write stall and write amplification issue. We demonstrate the viability of the proposed ZigZagDB using a set of extensive experiments. Experimental results show that ZigZagDB can significantly reduce the write amplification and boost the throughput in comparison with representative schemes. © 2024 IEEE.

关键词： Flash memory

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Traffic sign detection algorithm based on improved YOLOv5 4

Traffic sign detection algorithm based on improved YOLOv5

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4th International Conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Ma, Chi Li, Qinrong Hu, Hui Li, Jingyan Guo, Qiang School of Computer Science and Engineering Huizhou University Huizhou516007 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510682528

This paper introduces an enhanced YOLOv5 algorithm tailored for real-world traffic sign detection applications. Through the incorporation of Coordinate Attention after the SPPF module of the YOLOv5 backbone, the YOLOv5 neck pays more attention to key areas in the image and preserves accurate positional information. In response to the fact that traffic signs are mostly small targets, the PAN and FPN in the original algorithm's neck network are upgraded by substituting BiFPN for the previous feature fusion method, improving the algorithm's ability to detect traffic signs of different sizes, specifically targeting improved detection accuracy for small-scale targets. To validate the effectiveness of these modifications, we compared our improved model with other object detection model on the TT100K dataset and conducted ablation experiments. The experiments result revealed that the enhanced algorithm achieved an mAP of 94.2%, surpassing the original YOLOv5 model by 6.9%. The detection speed was 49.2 FPS, meeting the real-time requirements. © 2024 SPIE.

关键词： Traffic signals

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Robust Vision Transformer Model Against Adversarial Attacks in Medical Image Classification 47

Robust Vision Transformer Model Against Adversarial Attacks ...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Kanca, Elif Gulsoy, Tolgahan Avas, Selen Kablan, Elif Baykal Karadeniz Technical University Department of Software Engineering Trabzon Turkey Karadeniz Technical University Department of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798350365597

Recent advancements in deep learning have signif-icantly enhanced the rapid and precise classification of medical images. Vision transformers, an advanced model, have started replacing CNN s in several medical image tasks. However, recent research indicates that these sophisticated models are susceptible to adversarial attacks generated by various methods. This paper proposes a ViT-based model to bolster the robustness of Vision Transformers against such attacks. Extensive experiments across diverse datasets demonstrate that the ViT model attains higher accuracy on clean images compared to the proposed model. However, the proposed classification model exhibits greater re-silience against adversarial attacks. Across all datasets and attack methods, the proposed model achieves the lowest accuracy of 66.67%, contrasting with 0% for the ViT model. These findings underscore the paramount importance of constructing robust models for medical image classification. © 2024 IEEE.

关键词： Deep learning

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