Current Trends in Signal Processing

This research paper presents a novel neuromorphic–quantum hybrid computing framework envisioned to advance intelligent systems toward artificial general intelligence.

As the use of real-time data with high dimensions continues to expand across various domains, selecting important features from the dataset is a key step to improve the predictive accuracy and time taken to build a machine learning model.

Using the techniques of algebra, notably polynomial algebras and modules, algebraic signal processing (ASP) is a contemporary, abstract framework that generalizes conventional signal processing— including Fourier analysis, filtering, and convolution.

This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting..

This paper investigates Random Latent Exploration (RLE), a novel reinforcement learning technique that enhances exploration using randomized latent vector conditioning. I evaluate RLE’s performance across various environments, including discrete control tasks (FourRoom), continuous control (IsaacLab), and complex visual domains (Atari games).

Artificial Intelligence has rapidly evolved into a formidable instrument within the domain of mental healthcare, fundamentally altering the way we understand awareness, diagnosis, intervention and emotional regulation.