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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Bahareh Kalantar, Husam A. H. Al-Najjar, Biswajeet Pradhan, Vahideh Saeidi, Alfian Abdul Halin, Naonori Ueda and Seyed Amir Naghibi
Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods?Variance Inflation Fac...
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Veljko Marinkovic, Darko Dimitrovski, Vladimir Senic
Research question: The aim of the paper is to identify the statistically significant drivers for gold panning revisit intention at the River Pek in Serbia as one of the key factors in determining a positive effect on the destination?s economic sustainabi...
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Jose M. Bernal-de-Lázaro
Pág. 74 - 81
This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis syste...
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Hugo López-Fernández
Pág. 22 - 25
Mass spectrometry using matrix assisted laser desorption ionization coupled to time of flight analyzers (MALDI-TOF MS) has become popular during the last decade due to its high speed, sensitivity and robustness for detecting proteins and peptides. This a...
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Yi Zhao and Song-Kyoo Kim
This paper addresses the enhancement of modern security through the integration of electrocardiograms (ECGs) into biometric authentication systems. As technology advances, the demand for reliable identity authentication systems has grown, given the rise ...
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Angel E. Muñoz-Zavala, Jorge E. Macías-Díaz, Daniel Alba-Cuéllar and José A. Guerrero-Díaz-de-León
This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN arc...
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Konstantinos P. Fourkiotis and Athanasios Tsadiras
In today?s evolving global world, the pharmaceutical sector faces an emerging challenge, which is the rapid surge of the global population and the consequent growth in drug production demands. Recognizing this, our study explores the urgent need to stren...
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Maxim Kolomeets, Olga Tushkanova, Vasily Desnitsky, Lidia Vitkova and Andrey Chechulin
This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficult fo...
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Efrain Noa-Yarasca, Javier M. Osorio Leyton and Jay P. Angerer
Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap a...
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