Chit fund is a peer-to-peer saving and borrowing scheme operated among trusted groups of people. It is a reliable source of funds in emergencies with no guarantor and low-interest rate. Despite their enduring benefits...
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Recommender systems are an indispensable aspectof numerous contemporary online platforms, aiding users inuncovering new and pertinent content. However, both content-basedfiltering (CBF) and collaborative filtering (CF...
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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...
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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.
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights o...
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We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.
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 COVID-19 pandemic has resulted in a significant increase in the number of pneumonia cases, including those caused by the Coronavirus. To detect COVID pneumonia, RT-PCR is used as the primary detection tool for COV...
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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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Accurate and efficient multi-object localization and categorization is one of the key needs for applications of robotic vision, intelligent military surveillance systems, security, and ADAS. It is a significant and co...
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The rapid evolution of financial services has led to an increase in fraudulent activities, necessitating advanced detection methods. Traditional techniques often struggle with the volume and complexity of financial tr...
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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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