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Journal of Computer Science and Technology
Scopus
ISSN 1666-6046 · Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata · 625 artikel di indeks
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Artikel Jurnal
A Comparison of Technologies for Layer 2 Network Extension over IP: EoIP vs. VxLAN
Technology and communication are fundamental in modern enterprise environments, making network performance evaluation crucial. To analyze their impact on enterprise infrastructure performance, this study compares EoIP and VxLAN, two technologies used for Layer 2 network extension over IP. The…
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Security Enhancement of FinTech Platforms with Artificial Intelligence and Blockchain: A Systematic Literature Review
The convergence of artificial intelligence (AI) and blockchain is transforming the Fintech sector by offering innovative solutions in security, regulatory compliance, and operational transparency. This systematic mapping study analyzed 667 studies published between 2010 and 2024, selecting 175…
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On the Use of the Novel Zorro Activation Functions in Long Short-Term Memory Neural Networks
Activation functions are fundamental components of modern neural networks, including Large Language Models (LLMs). Nonlinear activations regulate the flow of information in most models and determine how data is processed. However, training or even fine-tuning very large and complex models with such…
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House Price Prediction in Andalusia: A Comparative Study of Hedonic and Machine-Learning Approaches Using Online Data
This study explores housing price prediction in six major cities of Andalusia using a combination of traditional and machine learning models. Online property listings were compiled to build a detailed dataset incorporating structural, socioeconomic, and locational variables. A traditional hedonic…
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Predictive model using neural networks and multitechnique validation in digital environments with scarce data for sustainable WEEE management
The sustainable management of Waste Electrical and Electronic Equipment (WEEE) is a critical global challenge, particularly in contexts with limited data. This study proposes a predictive model based on artificial neural networks, developed from surveys and historical records in the city of…
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Automatic Text Summarization: A Review of Approaches, Challenges, and Future Directions
Automatic text summarization (ATS) is a critical area in natural language processing that aims to condense long documents into concise and meaningful summaries. Manual text summarization is a time-consuming, labor-intensive, and costly process. Researchers have been actively working to enhance ATS…
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Scalability in Microservices: A systematic literature review
The scalability of microservices architectures is crucial for modern software systems, yet it presents significant challenges due to their inherent complexities. This study aims to systematically review existing literature on the scalability of microservices, identifying key strategies, challenges…
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Machine Learning for Site Adaptation of Satellite-DerivedSolar Irradiance in Northwestern Argentina
Accurate estimation of global horizontal irradiance (GHI) is essential for solar energy resource assessment, particularly in regions with limited ground-based measurements. This study evaluates the performance of three machine learning models—Simple Linear Regression (SLR), Extreme Gradient…
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Evaluation of Approaches Based on the BERT Model for Opinion Mining about the Cachaça Beverage
Opinion mining is a natural language processing task that aims to classify user opinions expressed on e-commerce platforms, social networks and other media. It is an important tool for decision making, monitoring products/services, detecting trends, developing marketing strategies, among others…
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Explainable Artificial Intelligence: Analysis of Methodologies and Applications
The lack of transparency and explainability in machine learning models, often referred to as "black boxes," presents a significant challenge that undermines trust and decision-making in critical applications such as medicine, finance, and security. This study examines the necessity of improving…
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Exploring Low-Cost Platforms for Automatic Chess Digitization
Automatic digitization of chess games through computer vision poses a considerable technological challenge. This capability holds significant appeal for tournament organizers and both amateur and professional players, enabling them to broadcast over-the-board (OTB) games online or facilitate…
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Impact of ambient temperature on short-term maximum electrical demand through the performance of forecast models generated with Prophet
The maximum short-term electrical demand is affected by climatic factors, including ambient temperature. To incorporate it into the forecast models, it is necessary to generate an indicator that represents the ambient temperature of the area under study. The objective of this research is to…
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Artificial Intelligence Applied in Legal Information: A Systematic Mapping Study
In the context of the digital era, our lives have become more dynamic, and the development of advanced technologies is transforming the way civil law relationships are handled. Today, legal professionals commonly use software that accelerates daily processes through the application of artificial…
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Impact of the population pyramid on the ED
A country’s population pyramid can affect the quality and demand for emergency departments in several ways. Emergency departament (ED) may need specialized resources and equipment to meet the population’s needs. In this research, we present an analysis of scenarios through agent-based modeling and…
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Trajectory analysis using data mining techniques
This study presents an innovative method for identifying variability in vehicular flow, designed for dynamic urban environments with fluctuating traffic conditions. The proposed approach integrates real-time data flow processing with a two-level clustering strategy to detect and analyze vehicular…
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Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components
This paper presents an exploration of unsupervised methods for initializing and training filters in convolutional layers, aiming to reduce the dependency on labeled data and computational resources. We propose two unsupervised methods based on the distribution of input data and evaluate their…
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Trends in High Performance Computing and Quantum Computing
High Performance Computing (HPC) applies different techniques to complex or large-volume applications, relying on both parallel software and hardware, to reduce their execution time compared to running them on a simple computer. On the other hand, Quantum Computing (QC) emerges as a new paradigm…
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A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework
Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel…
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A Study on pose-based deep learning models for gloss-free Sign Language Translation
Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, different grammars and lack of data. Currently, many SLT models rely on intermediate gloss annotations as outputs or latent priors. Glosses can help models to correctly segment and align signs to better understand…