Graph Neural Networks and Deep Reinforcement Learning-Based Resource Allocation for V2X Communications In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, cellular ...

Artificial Intelligence systems powered by deep learning are changing how we work, communicate, and make decisions. If we ...

In developing the sixth-generation (6G) system, integrated sensing and communication technology is becoming increasingly essential, especially for applications like autonomous driving. This paper ...

During training, deep learning demands large volumes of labeled data. However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled ...

Deep reinforcement learning is utilized to build an end-to-end learning framework for the joint optimization problem. In particular, by fixing the activated ports, we adopt a primal-dual based ...

Chennai: In a major push to build deep-tech capabilities in India’s workforce, IITM Pravartak Technologies Foundation, the ...

In today's rapidly changing world of technology, the curriculum alone is no longer sufficient to meet the growing demands of the tech industry. Real success belongs to institutions that move beyond ...

In today’s digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and ...

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms ...

In this work, we propose a ground-truth-free method for strong background noise suppression in DAS vertical seismic profiling (DAS-VSP) data. Compared to existing deep learning (DL) methods, the ...