The SDN-based network architecture currently lacks a framework for the efficient development and deployment of machine learning (ML) functions within the data plane. This paper addresses this gap by proposing a unifie...
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In the landscape of next-generation cellular networks, a projected surge of over 12 billion subscriptions foreshadows a considerable upswing in the network's overall energy consumption. The proliferation of User E...
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The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. Howeve...
Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural *** works with traditional Reinforcement Learning(RL)methods often...
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Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural *** works with traditional Reinforcement Learning(RL)methods often falter under such extreme *** address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management *** the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather *** incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather *** CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe *** results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased *** pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.
Open Information Extraction (OIE) is a structured prediction (SP) task in Natural Language Processing (NLP) that aims to extract structured n-ary tuples - usually subject-relation-object triples - from free text. The ...
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With cutting-edge functionality, 3D Shopping (Avatar Retail) promises to revolutionize the online shopping experience by seamlessly integrating gamification, social media, and augmented reality features. By immersing ...
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The DeepFish exploration aims to develop a web-based application that leverages deep learning algorithms to accurately identify and provide detailed information about various fish species based on user-uploaded images...
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This research presents an innovative approach to phishing URL detection by integrating hybrid features with Deep Q-Networks and convolutional neural networks. This combined method leverages Deep Q-Networks to enhance ...
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Accurate traffic sign recognition is crucial for driver assistance systems, yet challenges persist due to environmental factors and camera distortions. This study introduces a novel framework for real-time traffic sig...
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The advent of sensor data extracted from the smartphone offers opportunities to accurately model the human’s physical activities for context-aware scenarios or maybe for human-centered computing, e.g., human-in-loop,...
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