The development speed of informationtechnology is remarkable. At first, informationtechnology only intervened a lot in the field of industry. Now, with the improvement of people's overall economic level and the ...
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Automatic road extraction from high-resolution images has gained research interest due to a wide range of applications like autonomous driving, map updates, and traffic management. Roads have different shapes and patt...
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Deep learning is a standard approach in remote sensing for building information extraction, but it faces challenges like sparse data, large obstacles, and complex light-shadow interactions. To address these, we propos...
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The study, 'Empowering Young Adults,' investigates the role of soft skills in ensuring students' confidence to excel in personal and professional life using advanced machine learning models. Initially, a t...
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An innovative approach for examining the complex interaction between diabetes and heart disease is presented in 'A Unified Approach to Diabetes and Heart Disease Analysis: An Ensemble of XAI-Enhanced Models'. ...
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The paper details an Ethereum blockchain platform for smart grid energy trading, employing smart contracts and security measures like access control. It separates front-end and back-end, supporting secure integration ...
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Technological advancements have drastically in-creased data generation, requiring immediate processing to extract insights. While cloud computing has transformed data handling, it faces issues like high latency and ba...
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Huge records and AI generation have the capacity to revolutionize the way records is retrieved and used, enabling greater green and powerful retrieval approaches. AI algorithms are increasingly getting used to optimiz...
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Adopting Digital Twin (DT) technology in vehicular edge computing (VEC) enables efficient capture of real-time state information of applications, thereby addressing complex task scheduling problems. Existing literatur...
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ISBN:
(纸本)9798350369458;9798350369441
Adopting Digital Twin (DT) technology in vehicular edge computing (VEC) enables efficient capture of real-time state information of applications, thereby addressing complex task scheduling problems. Existing literature studies considered only minimizing service latency for task offloading;however, there is room for exploring strategies to enhance user Quality of Experience (QoE) in timeliness and reliability domains. In this paper, we have developed an optimization framework using Mixed Integer Linear Programming (MILP), namely QuETOD, which minimizes service latency by allocating task execution responsibility to highly reliable and reputed vehicles in a DT-enabled VEC environment. The developed QuETOD framework clusters the vehicles based on the demand-supply theory of economics by considering computing resources and utilizing the multi-weighted subjective logic for getting the proper reputation update of the vehicles. The experimental results of the developed QuETOD system depict significant performance improvement in terms of QoE and reliability compared to the state-of-the-art works as high as 15% and 25%, respectively.
With the evolution of AI technology and to support the ecosystem of domestic operating systems, this paper designs and implements an intelligent document question-answering robot system based on the Deepin domestic op...
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