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Service Function Chain Description Scheme in Named Data Networking

Service Function Chain Description Scheme in Named Data Netw...

引用

IEEE International Workshop Technical Committee on Communications Quality and Reliability, CQR

作者： Haruto Kobayashi Hidenori Nakazato Dept. of Computer Science and Communications Engineering Waseda University Faculty of Science and Engineering Waseda University

ISBN: (数字)9798331540722

ISBN: (纸本)9798331540739

In recent years, IoT has been utilized in various locations, demanding the construction of more efficient and easily deployable IoT networks. Utilizing edge computing has become a common approach to enhance efficiency, and in this context, an implementation of service function chaining through Named Data Networking (NDN SFC) has been proposed. However, existing implementations of NDN SFC faces several challenges, such as limited expressiveness and difficulty in deployment, making them unsuitable for real-world IoT use cases. We propose and implement “NDN Function Chaining Workflow+,” a novel approach to NDN SFC that addresses these challenges by offering enhanced expressiveness and ease of deployment. Our evaluation demonstrates its potential effectiveness in real-world IoT scenarios.

关键词： Service function chaining Conferences Reliability Faces Edge computing

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AttUNet : Enhancing ResUNet with Attention Mechanism for Accurate Brain Tumor Segmentation

AttUNet : Enhancing ResUNet with Attention Mechanism for Acc...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Vignesh, S. Renukadevi, M.N. Dayananda Sagar University Dept of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Brain tumor segmentation is a critical and challenging task in medical image analysis, essential for enhancing patient outcomes through timely detection and treatment. Manual segmentation is both laborious and subjective, highlighting the need for efficient automated solutions. Deep learning, especially models like U-Net, has demonstrated significant potential in biomedical image segmentation. The diversity in brain tumor location, shape, and size adds complexity to automatic segmentation, which involves differentiating abnormal from normal tissues (WM, GM, CSF). Multimodal approaches (e.g., PET/CT, PET/MRI) improve segmentation accuracy by integrating data from various modalities. MRI remains the primary tool for detecting brain tumors, but the process of tumor identification and segmentation is complex and heavily reliant on the expertise of radiologists. Recent advancements in deep learning have markedly enhanced the feasibility of accurate, automated brain tumor segmentation, meeting a critical need in medical imaging. Our proposed model, trained on the TGCA dataset, demonstrated superior performance with DSC 0.94, IoU 0.94 and SI 0.94 compared to traditional models like DeepResnet and Znet. This new method, notable for its performance, success, and pre-processing structure, is highly adaptable for various applications. © 2025 IEEE.

关键词： Attention gate AttRes DSC IoU Res-UNet SI

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Quantifying the Impact of Non-Code Text Block on Java Code Quality: Multi-Model Framework

Quantifying the Impact of Non-Code Text Block on Java Code Q...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sriharsha VVS Pranav B Nair Akarsh S.V Nakka Narmada Peeta Basa Pati Dept. of Computer Science & Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Non-code elements in programming, such as comments, documentation, and whitespace, play a crucial role in improving code readability, maintainability, and collaboration. However, their inclusion introduces overhead affecting code processing tools such as compilers and interpreters and static analyzers. In essence, this study investigates the impact of non-code blocks on the performance metrics of time complexity, space complexity, and execution time. A regression model was used to analyze the proportion of non-code elements in Java code and their effect on the performance metrics. The impact is quantified with the aim of providing empirical insights into how the density of non-code affects performance. Such findings are intended to help Java developers make decisions regarding code structuring and documentation while balancing aspects of maintainability and efficiency. The model's performance is validated by achieving an R2 score of 0.67, which demonstrates an improvement over previous methods and highlights its potential for real-world educational applications.

关键词： Dimensionality reduction Java Codes Reviews Transfer learning Documentation Robustness Integrated circuit modeling Time complexity Testing

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Patient Engagement and Satisfaction in Ai-Enhanced Healthcare Management 1

Patient Engagement and Satisfaction in Ai-Enhanced Healthcar...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Rathore, Yogesh Mishra Chaturvedi, Vandana Sujay Madhukar, Khadilkar Karwande, Vijay Suresh Rokade, Anil Haribhau Nagargoje, Yogesh Shri Shankaracharya Institute of Professional Management and Technology Dept of Computer Science & Engineering Chhattisgarh Raipur India D y Patil Deemed to Be University Navi Mumbai400706 India KIT's Institute of Management Education & Research KITsIMER Kolhapur India Everest Educational Society Group of Institutions College of Engineering and Technology Department of Computer Science and Engineering Chatrapati Sambhaji nagar431008 India Sambhajinagar India Csmss Chh. Shahu College of Engineering Department of Computer Science and Engineering Chhatrapati Sambhajinagar Aurangabad India

ISBN: (纸本)9798350330915

This study looks into how patient involvement and satisfaction are affected by AI-enhanced healthcare administration. Using a descriptive methodology and secondary data gathered from reliable sources, the research takes an interpretive approach within a deductive paradigm. The findings of the investigation indicate that artificial intelligence (AI)-driven solutions, such as chatbots and virtual assistants, enhance patient engagement by providing easy access to healthcare knowledge and customized interactions. Additionally, patients are happy with the effectiveness and customized care that AI systems offer. Nonetheless, consumer happiness levels are influenced by the caliber of contacts and worries about the confidentiality of data. Transparency, dependability, data privacy, provider expertise, and education for patients all influence people's trust in AI technologies. For artificial intelligence integration to be successful, a technical assessment emphasizes the significance of algorithm efficiency, scalability, seamless integration, data protection, and continuous maintenance. Patient-centered design should be prioritized, stakeholders should be informed, and AI systems should be continuously monitored. Prospective investigations ought to concentrate on extended periods, tailored treatment schemes, psychological assistance, reducing bias, economies of scale, and innovative artificial intelligence approaches. © 2023 IEEE.

关键词： AI-enhanced h ealth care patient engagement patient satisfaction technical evaluation trust in AI

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Artificial Neural Network Based Heat Transfer Analysis of CoFe2O4/Water Nanofluids

Artificial Neural Network Based Heat Transfer Analysis of Co...

引用

2024 International Conference on Futuristic Technologies in Control Systems and Renewable Energy, ICFCR 2024

作者： Sundar, Lingala Syam Reddy, N. Abhignya Kumar, N. T. Ravi Prince Mohammad Bin Fahd University Dept. of Mechanical Engineering Al-Khobar31952 Saudi Arabia The University of Texas Department of Computer Science and Engineering Arlington United States Greenko Group Hyderabad India

ISBN: (纸本)9798331504601

The research describes experimentally determine the heat transfer coefficient, Nusselt number, pressure drop, and friction factor of CoFe2O4 /water nanofluids flow in a tube during turbulent flow and then predict them using an artificial neural networkscaled conjugate gradient technique. The nanoparticles are synthesized using chemical reaction method. The water mixed CoFe2O4 hybrid nanofluids were prepared under 0.25% to 1.25% volume loadings. The flow properties such as thermal conductivity, viscosity, density and specific heat were measured in the temperatures from 20 to 60 °C, and volume loadings from 0.25% to 1.25%, followed by heat transfer and friction factor studies. The obtained data is used for ANN-Scaled Conjugate Gradient Predictions. The experimental results are shown that, the Nusselt number and heat transfer coefficient are boosted by 28.26% and 46.79% at φ=1.25% at a Reynolds number of 19301, with a friction factor penalty of 19.04% compared to water data. The results also showed that the scaled conjugate gradient algorithm predicted remarkably accurate values, with correlation coefficients of 0.99383, 0.99528,0.98653, and 0.9826 for heat transfer, Nusselt number, pressure drop, and friction factor, respectively. New Nusselt number, and friction factor correlations are reported based on experimental data. © 2024 IEEE.

关键词： Nusselt number

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A Neural Network Model for Autonomous Vehicles Safety Check Using Camera-only/Camera-Lidar Images

A Neural Network Model for Autonomous Vehicles Safety Check ...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Dengyi Liu Honggang Wang Hua Fang Graduate Computer Science and Engineering Department Katz School of Health and Science Yeshiva University NY Dept. of Computer and Information Science University of Massachusetts Dartmouth MA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Camera and LiDAR sensors play a crucial role in vehicle perception systems, enabling accurate detection of obstacles and other vehicles in autonomous driving technologies. Despite their complementary advantages, ensuring the reliability of camera data under varying environmental conditions poses a significant challenge. This paper introduces a novel system for camera data consistency checking through two methods: (1) a camera-only consistency checking mechanism utilizing an effective point-to-point feature extraction neural network inspired by the D2-Net method, and (2) a camera-LiDAR fusion data consistency checking approach employing advanced sensor fusion techniques. By integrating a critical reliability check for camera data and fusing it with LiDAR information, our system leverages the strengths of both sensor types to enhance the robustness and efficiency of autonomous vehicle navigation. Experimental results demonstrate that the proposed methods significantly improve the safety and performance of autonomous driving by ensuring reliable sensor data fusion, enabling safe navigation even in challenging environments.

关键词： Deep learning Accuracy Neural networks Sensor fusion Cameras Feature extraction Sensor systems Sensors Safety Autonomous vehicles

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OpenVAS vs the Rest: Unveiling the Competitive Edge in Vulnerability Scanners

OpenVAS vs the Rest: Unveiling the Competitive Edge in Vulne...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Mayank Sharma Divyanshu Desai Aditya Ranganathan Arun Priya. L Narendran Rajagopalan Dept. of Computer Science and Engineering National Institute of Technology Puducherry

In an increasingly interconnected and digital world, robust vulnerability management is paramount to safeguarding critical information assets and maintaining the integrity of network infrastructures. This paper explores into the heart of vulnerability assessment by presenting a comprehensive analysis of OpenVAS, an open-source vulnerability scanning tool, and its distinct advantages over other competing solutions. OpenVAS has emerged as a standout contender in the realm of vulnerability scanners, owing to its unique features, exceptional performance, and a commitment to open-source principles. Our study begins by providing an overview of the vulnerability management landscape, highlighting the importance of timely and accurate vulnerability assessment. Key advantages examined in this paper include OpenVAS's extensive coverage of vulnerability databases, ease of customization, scalability, and its robust community support. Moreover, its open-source nature ensures transparency and flexibility, making it an attractive choice for both small businesses and large enterprises. This paper aims to serve as a valuable resource for cybersecurity practitioners, IT professionals, and decision-makers seeking a thorough understanding of vulnerability assessment tools. By shedding light on OpenVAS's unparalleled strengths, it provides insights that empower organizations to make informed choices for their cybersecurity posture. In an era where cyber threats evolve rapidly, OpenVAS emerges as the beacon of proactive vulnerability management, offering distinct advantages that redefine the standards for the industry. As cybersecurity assumes an increasingly critical role in the digital age, this paper underscores the significance of choosing a tool that not only detects vulnerabilities but also empowers organizations to address them effectively.

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A Real-Time Approach to Detecting API Abuses Based on Behavioral Patterns

A Real-Time Approach to Detecting API Abuses Based on Behavi...

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Cryptography, Security and Privacy (CSP), IEEE International Conference on

作者： Sameeraa Prinakaa Bavanika V Sanjana S Sneha Srinivasan Sarasvathi V Dept. of Computer Science Engineering PES University Bengaluru India

ISBN: (数字)9798350386325

ISBN: (纸本)9798350386332

Over the past two decades, the internet has undergone a remarkable expansion, giving rise to the development of web-based software solutions. This evolution has led to widespread web Application Programming Interface (API) usage, facilitating remote software communication. However, the increased frequency of API calls has unfortunately resulted in a corresponding surge in API misuse instances. Monitoring API traffic becomes imperative to ensure data security, service availability, and system integrity. This paper introduces an API abuse detection system within a Log Storage Application utilizing Spring Boot with OAuth2.0 and JSON Web Token (JWT) based authentication. The proposed system effectively safeguards against critical security threats, such as Economical Denial of Service (EDoS) attacks (OWASP top 4), Unauthorized Access attempts (OWASP top 3), Brute Force Broken User Authentication, and Behavioral API abuses, with alerts promptly sent to the server. The prevalence of API usage has created numerous attack surfaces, offering a rich landscape for further research opportunities.

关键词： Privacy Force Authentication Software Real-time systems Servers Surges

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Proposal for a Virtual Reality System to Practice Knife Handling in Cooking

Proposal for a Virtual Reality System to Practice Knife Hand...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yukitoshi Nakazato Toru Kano Dept. Computer Science and Engineering Ibaraki Univercity Ibaraki Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Knife handling is one of the most important skills in cooking. However, it is difficult to practice the handling of a kitchen knife repeatedly owing to limitations in resources. To address this, we propose a highly realistic system using a haptic device and CT (Computed Tomography) data. This system allows users to practice knife handling in a virtual environment, providing force feedback that reproduces real-life situations. Our goal is to enhance the efficiency and effectiveness of cooking training by enabling unlimited practice without the constraints of ingredients.

关键词： Training Computed tomography Force feedback Virtual environments Proposals Consumer electronics

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Design and Development of Expense App

Design and Development of Expense App

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Gunit Girdhar Sachin Kumar Anuj Bhardwaj Megha Sharma Dept. of Computer Science & Engineering Chandigarh University Mohali India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

With the help of Expense Tracker, you can easily and intuitively manage your revenue and costs while keeping track of your everyday, weekly, monthly, and annual spending. Users may choose the kind of expenditure, add location, upload more data, choose the amount of expense, and more. Additionally, the relational database will save this data. Users have the option to see and sort spending weekly, monthly, or annually. We may track the spending and minimize the amount of human computations for their costs by employing this. This allows a user to enter their income to get an estimate of their daily costs, and the results are saved for each individual user. Individuals who frequently go on excursions or to the theatre with friends might use this tracker to keep tabs on their spending. They will find it simple to split the bill using this tracker. This will show graphs according to the chosen perspective. Additionally, a user may write in this TR his monthly income or monthly expenditure cap. An integrated feature set is offered by this tracker system to assist you in managing your spending and cash flow.

关键词： Costs Relational databases Investment

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