Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrate...
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Sea sky-line detection is crucial for unmanned vessel attitude estimation and maritime surveillance target detection to reduce computational complexity. Many existing seas sky-line detection algorithms mainly extract ...
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Meta-heuristic algorithms have become a popular approach for flexible job shop scheduling optimization problem. In this paper, an improved sand cat swarm optimization algorithm (ISCSO) is proposed for the flexible job...
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This paper reviews the recent research on network-on-chip (NoC) optimization techniques, which address the challenges of fault tolerance and congestion management in multicore systems. It reviews the innovative strate...
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Massive MIMO (Multiple-Input Multiple-Output) technology hugely enhances spectral and energy efficiency in wireless communication systems, but signal detection remains a key challenge due to its high computational com...
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In this paper, we address the transmission of local data on the uplink of wireless systems aided by reconfigurable intelligent surface (RIS) arrays using the concept of spatial modulation (SM), and we introduce new ac...
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The accurate classification of wildlife habitats is necessary as it provides necessary resources and condition of wildlife population. To improve this prediction task, the proposed research developed Machine Learning ...
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In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algo...
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The Optimized controller is established for controlling the reconfigurable UAV, which has been designed to enable various morphologies whilst keeping the performance of a standard quadrotor. These design adjustme...
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Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms, and social networks, to provide suggestions that users will most likely consume or connect, thus improvi...
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ISBN:
(纸本)9798400704369
Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms, and social networks, to provide suggestions that users will most likely consume or connect, thus improving the user experience [1]. This motivates people in industry and research organizations to focus on personalization and recommendation algorithms, resulting in many research papers [2, 3]. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment, including but not limited to business metric definition and evaluation, scalability, recommendation quality control, robustness, fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions to industrial recommendation systems. This workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue for industrial researchers to share practical insights and for academic researchers to become aware of the additional factors of algorithm adoption in real production systems.
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