Unmanned aerial vehicles (UAVs) have mobility in harsh environments and the flexibility to modify their flight altitude to acquire information in an adaptive manner, so these vehicles can be leveraged to solve the inf...
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The current COVID-19 epidemic is responsible for causing a catastrophe on a global scale due to its risky spread. The community’s insecurity is growing as a result of a lack of appropriate remedial measures and immun...
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Column-oriented databases have emerged as effective solutions for handling massive amounts of data, and data compression plays a crucial role. Attribute columns are divided into blocks and stored in separate files, an...
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In maritime emergency response operations, autonomous underwater vehicles (AUVs) can perform underwater search and transmit emergency data in real-time. To collect the sensed data from AUVs across the water-air interf...
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We study the problem of selecting the contention window (CW) for age of information (AoI)-oriented IEEE 802.11 networks using deep reinforcement learning (DRL) techniques. AoI quantifies information freshness and is d...
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Federated Learning (FL) enables multiple clients to collaboratively train models without exposing their local data. FL is an effective approach to utilizing localized data while preserving clients' data privacy, b...
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Federated learning allows multiple parties to jointly train deep learning models without the need for any participants to reveal their private data to a centralized server. However, this form of privacy-preserving col...
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Dynamic constrained multi-objective optimization problems (DCMOPs) are characterized by time-varying objectives and constraints, requiring optimization algorithms that can rapidly track the changing Pareto-Optimal Set...
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Ensemble learning for big data has been successful in machine learning and has great advantages over other learning methods. The ensemble model based on Random Sample Partition (RSP) is a prominent method of it. Altho...
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In recent years, with the continuous development of deep learning, more and more network models have been proposed to solve practical problems. However, most models often need a large number of labeled samples to trai...
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