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检索条件"任意字段=9th IEEE International Conference on Engineering of Complex Computer Systems"

共 16805 条记录，以下是111-120 订阅

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Security Dynamics of Blockchain-Enabled SDN systems: A Taxonomic Approach 9

Security Dynamics of Blockchain-Enabled SDN Systems: A Taxon...

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9th international conference on computer Science and engineering, UBMK 2024

作者： Dudukcu, Deniz Karakus, Murat Ankara University Artificial Intelligence Technologies Graduate Program Ankara Turkey Ankara University Department of Software Engineering Ankara Turkey

ISBN: (纸本)9798350365887

In the current landscape, security challenges extend to various domains, such as IoT networking, UAV communication, and VANETs, rendering underlying infrastructures increasingly susceptible to cyber-attacks. the taxonomies, targets, and purposes of these attacks are closely linked to the properties of the underlying infrastructures. With the elevated vulnerability risks and attacks targeting specific elements of Software Defined Networking (SDN), integrating blockchain technology into networking has emerged as a potential solution. However, this convergence of SDN and blockchain introduces new security risks. this position paper overviews blockchain-enhanced SDN systems focusing on security issues and reviews existing research and arguments. Notably, there is currently no agreed-upon established taxonomy for the integrated security of SDN and blockchain. As a contribution to the literature, this paper proposes a taxonomy for classifying security approaches within SDN and blockchain deployment scenarios. this taxonomy categorizes solutions based on defense mechanisms, defense planes, blockchain deployment types, and SDN deployment types. Additionally, we discuss the open issues and challenges at the intersection of SDN and blockchain technologies from a security perspective. © 2024 ieee.

关键词： Cyber attacks

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A New MRA and AA based Algorithm for the Higher Order Reduction Uncertain systems 9

A New MRA and AA based Algorithm for the Higher Order Reduct...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Shende, Vaishali Gajendra Raviraja, S. Kavitha, P. Aancy, H. Mickle Kumar, M. Jogendra Mittal, Varsha East Point College of Engineering And Technology Department of Information Science And Engineering Karnataka Bangalore560049 India University of Garden City Royal Research Foundation® Mysuru Department of Computer Studies Karnataka 570028 India R.MK. Engineerine College Department of Electrical And Electronics Eneineering Chennai Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Management Studies Tamil Nadu Chennai600123 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram India Graphic Era Deemed To Be University Department of Computer Science And Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350365092

the MRA and AA algorithms are utilized in this paper to present a novel approach for the reduction of high-order uncertain systems. the development of the program makes use of both of these mathematical approaches. As a result of this creative approach, the Continuous SISO system might be reduced. Measurement mistakes brought on by variations in the environment have the potential to render the system ambiguous, in contrast to other systems that have been reported in the literature. this strategy, in contrast to the methods that were used in the prior iteration literature, has the potential to build a stable reduced order (r-o) model from a stable initial high order uncertain system. By avoiding many of the constraints of interval arithmetic, affine arithmetic is used. One of the problems is that there is not a solution that is unbounded in many of the key scenarios. through the utilization of typical numerical examples derived from interval literature, the methodology was validated. Among the topics that we investigate are uncertain systems, lower order models, affine arithmetic, and the modified Routh approximation approach. © 2024 ieee.

关键词： Uncertain systems

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. these systems need properly placed and connected electrical components for best performance and reliability. complexity and time restrictions are rendering manual methods ineffective. therefore, AI-based PCB insertion and routing automation methods have been developed. this research suggests using AI to optimize PCB component arrangement and routing. the system uses machine learning and optimization to improve design efficiency. the AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. this research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 ieee.

关键词： Reinforcement learning

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ResNet-50 Powered Masked Face Detection: A Deep Learning Perspective 9

ResNet-50 Powered Masked Face Detection: A Deep Learning Per...

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Choudhary, Nilam Rathore, Pramod Singh Kumar, Loveleen Rajaan, Rajesh Sharma, Aakriti Sinha, Dibakar Swami Keshvanand Institute of Technology Management & Gramothan Department of Computer Science & Engineering Jaipur India Manipal University Jaipur Department of Computer And Communication Engineering Jaipur India Manipal University Jaipur Department of Computer Science And Engineering Jaipur India

ISBN: (纸本)9798350394474

Due to the widespread use of face masks as a result of the COVID-19 pandemic, facial recognition technology, which is routinely employed for security screening in workplaces, is encountering substantial difficulties. these algorithms were commonly trained using photos of unmasked human faces, which posed challenges in recognising individuals wearing masks. In order to tackle this problem, this study introduces a deep learning model that is specifically tailored to properly identify individuals wearing masks. the model utilises the ResNet-50 architecture and has high proficiency in recognising individuals wearing face masks. the findings of this study can be incorporated into existing facial recognition software utilized for security screening purposes. © 2024 ieee.

关键词： Face recognition

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Advanced Convolutional Neural Networks for Plastic Classification in Recycling systems 9

Advanced Convolutional Neural Networks for Plastic Classific...

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9th international conference on computer Science and engineering, UBMK 2024

作者： Alimbekova, Nazym Zhumadillayeva, Ainur Aiymbay, Sunggat L.N. Gumilyov Eurasian National University Astana International University Faculty of Information Technology Astana Kazakhstan L.N. Gumilyov Eurasian National University Faculty of Information Technology Astana Kazakhstan Astana It University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798350365887

this research examines the utilization of sophisticated convolutional neural networks (CNN) for the automated categorization of plastic waste in order to augment recycling effectiveness. A comparative analysis of three CNN architectures - MobileNet, ResNet-34, and EfficientNet-B1 - was conducted, alongside the introduction of an innovative model, EfficientNet-SED, aimed at enhancing both accuracy and computational efficiency. Empirical evaluations utilizing a dataset comprised of plastic bottles revealed that EfficientNet-SED attained a classification accuracy of 99.1%, exceeding the performance metrics of the other models. these results underscore the potential of modern machine learning methodologies to refine waste sorting systems, thereby facilitating more efficient recycling practices and resource preservation. © 2024 ieee.

关键词： Convolutional neural networks

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YOLO-AM identifies cracking and bulging defects in air springs 9

YOLO-AM identifies cracking and bulging defects in air sprin...

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9th international conference on Image, Vision and Computing, ICIVC 2024

作者： Han, Kaiying Xu, Wenteng Qin, Jiayang Wang, Biliu Zhao, Duo Huang, Zhong SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China School of Electrical Engineering Southwest Jiaotong University Chengdu China State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration CRRC Zhuzhou Electric Locomotive Co. LTD Zhuzhou China

ISBN: (纸本)9798350385991

this paper proposes a novel computer vision-based monitoring solution based on YOLOv8 enhanced with the Geometric Attention Module (GAM) for detecting defects in air springs in high-speed trains. Utilizing enhanced data sets captured by line scan cameras, the complex challenge of accurately identifying subtle defects such as cracks and bulges under varying environmental conditions is solved. By capturing the grid features on the air spring, the model’s ability to accurately detect and classify defects is significantly enhanced, achieving high recall (0.889 for cracks and 1.000 for bulges) and precise identification. this study highlights the potential of advanced deep learning techniques combined with specialized attention mechanisms to provide a more reliable, efficient, and automated alternative to traditional inspection methods. © 2024 ieee.

关键词： Cracks

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A Survey on Conversion of Audio and Text to Sign Language and vice versa using Deep learning 9

A Survey on Conversion of Audio and Text to Sign Language an...

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9th ieee international conference on Smart Structures and systems, ICSSS 2023

作者： Betsy Angelin, S. Nithya, E. Devianugraga, M.S. Sri Sairam Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350384208

Millions of individuals throughout the world use sign language as their primary form of communication, particularly those who are deaf or hard of hearing. But because not everyone is proficient in sign language, it might be difficult to communicate and restrict access to information for sign language users. the communication gap between the users and non-users of sign language can be significantly closed by systems that can translate sign language into text and audio as well as audio into sign language. Deep learning models have been extremely effective in advancing the recognition and conversion of sign language during the past few decades. these tools translate sign language motions into written or spoken words by using computer vision and natural language processing techniques, and vice versa. But there are still many problems that need to be solved, including the need for more precise and durable deep learning models and the diversity of sign language dialects and cultural variations. the goal of this survey article is to present a summary of the state of the art in sign language conversion technologies, including deep learning models for sign language recognition, text and audio conversion from sign language, and audio to sign language conversion. © 2023 ieee.

关键词： computer vision

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Multimodal Steganography: A Comparative Analysis of LSB and DCT Methods for Image and Audio Data Concealment 9

Multimodal Steganography: A Comparative Analysis of LSB and ...

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Dharani, Bathula Achutha Yashaswini, Bhoomireddy Shyashyankha Reddy, Garimi Reddy Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Multimodal steganography is a technique that involves concealing information in multiple types of media to enhance data-hiding capabilities. In this proposed work, exploration and comparison of two distinct methods for multimodal steganography are performed: the LSB method and the DCT method for the modalities. the LSB method is straightforward to implement and has a high data capacity, but it is susceptible to detection and quality degradation. In contrast, the DCT method, while more complex, offers better robustness and perceptual quality, particularly for larger data payloads. the comparison considers factors such as capacity, robustness, perceptual quality, and implementation complexity. the findings suggest that the choice between the two methods depends on specific application requirements. LSB may be suitable for scenarios prioritizing higher data capacity, while DCT is recommended for applications requiring enhanced robustness and perceptual quality. the abstract concludes by proposing the potential use of a hybrid approach for optimal trade-offs between capacity and security. © 2024 ieee.

关键词： Steganography

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MBSE as an Enabler for Collaborative Offer Management for Individual Production systems 18

MBSE as an Enabler for Collaborative Offer Management for In...

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18th Annual ieee international systems conference (SysCon)

作者： Wilke, Daria Humpert, Lynn Seidenberg, Tobias Menne, Leon Grewe, Carolin Dumitrescu, Roman Fraunhofer Res Inst Mech Syst Design IEM Paderborn Germany

ISBN: (纸本)9798350358810;9798350358803

Given the importance of mechanical and plant engineering for European competitiveness, it is necessary to think beyond the current status quo. Mechanical and plant engineering is characterized by the development of large-scale customer-specific products. the development of such complex systems is a challenge for all disciplines, e.g. mechanics, electronics, computer science and control engineering, as they have to interact with each other. During the offer phase, an initial system concept is created. the offer phase is crucial for the company, as it is during this phase that the customer decides whether to buy the system. At the same time, since it is unclear whether the customer will accept the offer, it is a question of cost-benefit for the company. this paper examines the urgency of interdisciplinary approaches in mechanical and plant engineering, particularly in light of the increasing trend towards product customization and the global competitive landscape. the idea of a model-based approach for the offer phase in special machine construction is explicitly discussed. this approach can contribute to an efficient offer phase. the advantages of a model-based approach for the offer phase of mechanical and plant engineering are also presented in this paper. With the help of industry experts, different types of models are analyzed for their suitability for this phase.

关键词： Model-Based systems engineering complex systems offer management technical sales large-scale production systems special purpose machinery collaborative process

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Geopolitical Risk Analysis and its Impact on Market Volatility 9

Geopolitical Risk Analysis and its Impact on Market Volatili...

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Rathinavel, Keshav Indira Kumar, A.K. Sahoo, Anshuman Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering & Computer Science Malaysia

ISBN: (纸本)9798350394474

this study addresses the influence of geopolitical risks on financial markets, as noticed in recent events like the COVID-19 pandemic and the Russia-Ukraine war. this study showcases a model combining financial indices with the Geopolitical Risk Index (GPRI), employing Bi-LSTM networks with attention mechanisms to analyze the data. this approach captures the nuanced effects of geopolitical events on market volatility. Focused on the pandemic period, our model offers insights into market dynamics during times of geopolitical unrest and serves as a predictive tool for market behavior in response to global events. © 2024 ieee.

关键词： Risk analysis

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