Current Trends in Signal Processing
Volume 15, Issue 3 (2025)
Table of contents
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Digital Psychiatry: A Narrative Review on AI Positive Role in Mental Health
Abstract: 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. This narrative review explores AI’s potential to foster positive mental health through tools such as natural language processing, machine learning, deep learning and computer vision. These technologies promise earlier detection of mental disorders, customized treatment plans and responsive emotional support. Yet, alongside these …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 1–13 Read article
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Randomized Latent Vectors for Enhanced Reinforcement Learning Exploration
Abstract: 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). The core approach augments traditional reward functions with intrinsic rewards, calculated as the dot product between state features and periodically resampled latent vectors. The policy and value networks are …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 19–25 Read article
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A Study on Feature Subset Selection in Feature Streams of Dynamic Data
Abstract: 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. In datasets where not all features are available at the same time and we are unaware of the total number of features, and features arrive at different time stamps, for example, …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 26–32 Read article
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Algebraic Foundations of Generalized Signal Processing: A Unified Approach Across Domains
Abstract: 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. The notion is to use algebraic structures to explain signals, systems, and transformations such that ideas may be understood and generalized across many domains, including time, space, graph, or group. A unifying theoretical framework called ASP generalizes classical signal processing …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 33–44 Read article
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article