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Fundus Image Translation with Scale-Aware Registration and Gradient-Guided GAN

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SSRN

SSRN 2024年

作者： Kim, JuChan Le, Duc-Tai Bum, Junghyun Son, Chang-Hwan Lee, Eun Jung Han, Jong Chul Choo, Hyunseung Department of Artificial Intelligence Systems Engineering Sungkyunkwan University Korea Republic of College of Computing and Informatics Sungkyunkwan University Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Korea Republic of Software Science and Engineering Major Kunsan National University Korea Republic of Department of Ophthalmology Samsung Medical Center Korea Republic of School of Medicine Sungkyunkwan University Korea Republic of Department of Medical Device Management and Research SAIHST Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Two major fundus imaging types, conventional fundus (CF) imaging and ultrawidefield fundus (UF) imaging, are widely used in ophthalmology. In a typical clinical setting, both imaging methods are used simultaneously to diagnose diseases depending on the case. This study introduced an enhanced method of modality transformation to boost the diagnostic support capabilities of deep-learning models in ophthalmology. The method proposed in this study to translate UF images (UFIs) to CF images (CFIs) can replace the common clinical approach of both imaging methods, thereby reducing financial and temporal burdens on patients. This study learns and transforms the relationship between these modalities using UFI–CFI image pairs obtained from the same patient on the same day to minimize distorted information and accurately convert between different modalities. Our proposed model uses scale-aware registration and gradient-guided translation methods to align the UFI–CFI pairs, The generated CFIs not only enhance the quality of images and better represent the retinal area compared to existing methods but also effectively preserve the disease-related details in UFIs, aiding in the diagnosis. In addition, we optimize the image transformation performance, resulting in an 22% improvement in image quality assessment scales compared with previous methods. Furthermore, we acquired the observation that our results are nearly impossible to discern any differences between the real CFIs and the generated CFIs in the view of ophthalmologists. © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

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Genetic Algorithm-Based Feature Selection for Accurate Breas...

IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Raja Hashim Ali Muhammad Zeeshan Shabbir Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Muhammad Abu Bakar Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.

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A systematic literature review of blockchain cyber security

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Digital Communications and Networks 2020年第2期6卷 147-156页

作者： Paul J.Taylor Tooska Dargahi Ali Dehghantanha Reza M.Parizi Kim-Kwang Raymond Choo School of Computing Science&EngineeringUniversity of SalfordManchesterUK Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Software Engineering and Game Development Kennesaw State UniversityMariettaGA30060USA Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,trustless,peer-to-peer *** research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security *** findings show that the Internet of Things(IoT)lends itself well to novel blockchain applications,as do networks and machine visualization,public-key cryptography,web applications,certification schemes and the secure storage of Personally Identifiable Information(PII).This timely systematic review also sheds light on future directions of research,education and practices in the blockchain and cyber security space,such as security of blockchain in IoT,security of blockchain for AI data,and sidechain security.

关键词： Blockchain Smart contracts Cyber security Distributed ledger technology IoT Cryptocurrency Bitcoin

SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

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arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

Pattern recognition in vital signs using spectrograms

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arXiv 2021年

作者： Sribhashyam, Sidharth Srivatsav Salekin, Md Sirajus Goldgof, Dmitry Zamzmi, Ghada Last, Mark Sun, Yu Department of Computer Science and Engineering University of South Florida TampaFL United States Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

— Spectrograms visualize the frequency components of a given signal which may be an audio signal or even a time-series signal. Audio signals have higher sampling rate and high variability of frequency with time. Spectrograms can capture such variations well. But, vital signs which are time-series signals have less sampling frequency and low-frequency variability due to which, spectrograms fail to express variations and patterns. In this paper, we propose a novel solution to introduce frequency variability using frequency modulation on vital signs. Then we apply spectrograms on frequency modulated signals to capture the patterns. The proposed approach has been evaluated on 4 different medical datasets across both prediction and classification tasks. Significant results are found showing the efficacy of the approach for vital sign signals. The results from the proposed approach are promising with an accuracy of 91.55% and 91.67% in prediction and classification tasks respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal reconstruction

Compromised ACC vehicles can degrade current mixed-autonomy traffic performance while remaining stealthy against detection

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arXiv 2021年

作者： Gunter, George Li, Huichen Hojjati, Avesta Nice, Matthew Bunting, Matthew Gunter, Carl A. Li, Bo Sprinkle, Jonathan Work, Daniel The Department of Civil and Environmental Engineering The Institute for Software Integrated Systems Vanderbilt University TN United States The Department of Electrical and Computer Engineering The Institute for Software Integrated Systems Vanderbilt University TN United States The Department of Computer Science University of Illinois at Urbana-Champaign IL United States The Department of Computer and Electrical Engineering University of Arizona AZ United States

We demonstrate that a supply-chain level compromise of the adaptive cruise control (ACC) capability on equipped vehicles can be used to significantly degrade system level performance of current day mixed-autonomy freeway networks. Via a simple threat model which causes random deceleration attacks (RDAs), compromised vehicles create congestion waves in the traffic that decrease average speed and network throughput. We use a detailed and realistic traffic simulation environment to quantify the impacts of the attack on a model of a real high-volume freeway in the United States. We find that the effect of the attack depends both on the level of underlying traffic congestion, and what percentage of ACC vehicles can be compromised. In moderate congestion regimes the attack can degrade mean commuter speed by over 7%. In high density regimes overall network throughput can be reduced by up to 3%. And, in moderate to high congestion regimes, it can cost commuters on the network over 300 kmhrUSD. All of these results motivate that the proposed attack is able to significantly degrade performance of the traffic network. We also develop an anomaly detection technique that uses GPS traces on vehicles to identify malicious/compromised vehicles. We employ this technique on data from the simulation experiments and find that it is unable to identify compromised ACCs compared to benign/normal drivers. That is, these attacks are stealthy to detection. Stronger attacks can be accurately labeled as malicious, motivating that there is a limit to how impactful attacks can be before they are no longer stealthy. Finally, we experimentally execute the attack on a real and commercially available ACC vehicle, demonstrating the possible real world feasibility of an RDA. In particular, we test two well-known CAN bus detection techniques on the ensuing data and find that they fail to identify the malicious messages, suggesting that the attacks may be stealthy to detection at the vehicular level as

关键词： Anomaly detection

Interplay between Physical Layer Security and Blockchain Technology for 5G and Beyond: A Comprehensive Survey

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TechRxiv

TechRxiv 2022年

作者： Ghourab, Esraa M. Jaafar, Wael Bariah, Lina Naser, Shimaa Muhaidat, Sami Yanikomeroglu, Halim The KU Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates The Department of Software and Information Technology Engineering Ecole de Technologie Supérieure MontrealQCH3C 1K3 Canada Abu Dhabi9639 United Arab Emirates The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada The Department of Systems and Computer Engineering Carleton University OttawaON Canada

Fifth-generation and beyond (5G and xG) wireless networks are envisioned to meet the requirements of vertical applications like high traffic throughput, ultra-massive connectivity, extremely low latency, and high quality of service. Disruptive technologies, such as massive multiple-input multiple-output, millimeter wave, and multiple access are being deployed to meet these requirements. However, their deployment poses several challenges, including a lack of network transparency, management decentralization, and reliability. Moreover, the heterogeneity of future networks raises security concerns, e.g., confidentiality, privacy, and trustworthiness. Indeed, due to the emergence of novel paradigms, e.g., quantum computing, traditional security approaches are no longer sufficient to protect over-the-air communications. Hence, 5G/xG networks must consider smart security techniques to operate seamlessly and efficiently. Within this context, physical layer security (PLS) and blockchain represent promising solutions to complement existing methods. By exploiting the characteristics of wireless links, PLS can enhance the security of communications, while blockchain may enable networks’ decentralization, integrity, and trustworthiness. Motivated by these advancements, we provide an in-depth review of the existing PLS and blockchain literature. Then, for the first time in the literature, we present a framework to integrate PLS with blockchain in 5G/xG systems. We first provide a thorough discussion about the potential of PLS and blockchain for 5G/xG systems. Then, we present our vision of a cross-layer architecture that leverages PLS and blockchain. Through a case study, we demonstrate the high potential of cross-layer design to improve the security of vehicular networks. Finally, we identify related challenges and shed light on future research directions. © 2022, CC BY.

关键词： MIMO systems

Self-Healing Secure Blockchain Framework in Microgrids

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arXiv 2022年

作者： Rath, Suman Nguyen, Lam Duc Sahoo, Subham Popovski, Petar The Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Software Systems Research Group CSIRO's Data61 Sydney2015 Australia The Department of Energy Aalborg University Aalborg9220 Denmark The Department of Electronic Systems Aalborg University Aalborg9220 Denmark

Blockchain has recently been depicted as a secure protocol for information exchange in cyber-physical microgrids. However, it is still found vulnerable to consensus manipulation attacks. These stealth attacks are often difficult to detect as they use kernel-level access to mask their actions. In this paper, we firstly build a trusted and secured peer-to-peer network mechanism for physical DC microgrids' validation of transactions over Distributed Ledger. Secondly, we leverage from a physics-informed approach for detecting malware-infected nodes and then recovering from stealth attacks using a self-healing recovery scheme augmented into the microgrid Blockchain network. This scheme allows compromised nodes to adapt to a reconstructed trustworthy signal in a multi-hop manner using corresponding measurements from the reliable nodes in the network. Additionally, recognizing the possible threat of denial-of-service attacks and random time delays (where information sharing via communication channels is blocked), we also integrate a model-free predictive controller with the proposed system that can locally reconstruct an expected version of the attacked/delayed signals. This supplements the capabilities of Blockchain, enabling it to detect and mitigate consensus manipulation attempts, and network latencies. © 2022, CC BY.

关键词： Blockchain