Current Trends in Signal Processing
Volume 15, Issue 1 (2025)
Table of contents
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Exploring Technologies for Extractive Text Summarization: A Review of Transformer and Reinforcement Learning Models
Abstract: In recent years, the size of information on the Internet has increased exponentially. Therefore, a solution is needed to transform large amounts of raw data into useful information the human brain can understand. Automatic Text Summarization (ATS) is a part of Natural Language Processing (NLP) that aims to take long texts and shorten them, keeping the most important information in a clear and easy-to-understand way. This research report explores methods …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 1–6 Read article
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Face Detection and Classification for Attendance Systems on Android
Abstract: This work offers a facial recognition-based attendance system with the goal of addressing the drawbacks of traditional manual attendance. The manual attendance procedure can be made more efficient by using facial recognition technologies and mobile platforms. This design is divided into three function modules: attendance sign-in, attendance record, and face recognition system of check on work attendance information input. It also introduces a face detection and classification principle, analyses the …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 15–22 Read article
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Artificial Intelligence Enhanced Waste Sorting and Classification System for Urban Recycling
Abstract: This study explores the potential of Artificial Intelligence (AI) and Machine Learning (ML) to enhance waste management efficiency within urban environments. Rapid urbanization has resulted in a surge of municipal waste, which current systems often struggle to manage effectively. The proposed AI-enhanced waste sorting and classification system aims to optimize waste collection routes and accurately forecast waste generation trends, thereby reducing operational costs, fuel consumption, and traffic congestion. Additionally, AI-driven …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 23–32 Read article
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CNN-Based Wound Segmentation: A Review of Models and Performance Evaluation
Abstract: Deep learning, particularly convolutional neural networks (CNNs), has altered medical image processing by automating and precisely segmenting complex medical pictures. Wound segmentation, a critical application in automated wound assessment, is essential for wound size estimation, classification, and healing progress monitoring. This study presents a comprehensive review of CNN-based wound segmentation models, focusing on their architectures, methodologies, and performance on diverse datasets. Four deep learning models, including two U-Net variants (5-layer …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 33–46 Read article
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Ionospheric Disturbances and Communication Signal anomalies due to eruption of Solar flare Activity
Abstract: Solar flares tend to disrupt the radio communication systems and modulate active radio wave frequencies, particularly the ones that have utility in High Frequency (HF) radio signals and transceiver signals because they are the ones who find utility in radio communications systems. Solar flares generate electromagnetic waves which interact with the earth’s atmospheric medium and travel through the ionospheric region. These electromagnetic waves modulate radio frequency communication signals. This modulation …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 7–14 Read article