Abstract: This research investigates the application of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, paired with gradient-based optimization techniques for ...
Abstract: Dense segmentation tasks, including semantic, instance, and panoptic segmentation, are essential for improving our comprehension of urban landscapes. This paper examines various deep ...
Abstract: The telecoms sector receives massive amounts of data every day from its vast population of customers. Here, acquiring additional customers is more expensive than retaining existing ones.
Abstract: Consumer segmentation is a very effective methodology that could enable organizations to gain a deeper comprehension of their consumer base and customize their tactics accordingly in order ...
Abstract: In the dynamic and fiercely competitive landscape of the telecommunications industry, customer churn prediction poses a significant challenge due to the continuously evolving customer ...
On-the-Fly Improving Segment Anything for Medical Image Segmentation Using Auxiliary Online Learning
Abstract: The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical ...
Abstract: Customer segmentation is a vital strategy for businesses seeking to enhance their marketing efforts and optimize resource allocation. This research focuses on segmenting customers using the ...
Abstract: Before implementing Autonomous Vehicles (AVs) in real-world settings, it is imperative to conduct thorough safety testing. Virtual simulation testing, known for its high fidelity, ...
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