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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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Aristeidis Karras, Anastasios Giannaros, Christos Karras, Leonidas Theodorakopoulos, Constantinos S. Mammassis, George A. Krimpas and Spyros Sioutas
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces ...
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Lingzhi Yin and Yafei Wang
Delving into the spatiotemporal evolution of the railway network in different periods can provide guidance and reference for the planning and layout of the railway network. However, most of the existing studies tended to model the railway data separately...
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Marko Jevtic, Sa?a Mladenovic and Andrina Granic
Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry?s needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to ...
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Yao Ding, Yin Wang, Shuming Yang, Xiaolong Zhao, Lili Ouyang and Chengyue Lai
The current standards used for nitrogen pollution evaluation are lacking, and scientific classification methods are needed for nitrogen pollution to improve water quality management capabilities. This study addresses the important issue of assessing surf...
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Dhan Lord B. Fortela, Alyssa M. DeLattre, Wayne W. Sharp, Emmanuel D. Revellame and Mark E. Zappi
Microalgae are multi-purpose microbial agents due to their capability to efficiently sequester carbon dioxide and produce valuable biomass such as protein and single-cell oils. Formulation and tuning of microalgae kinetics models can significantly contri...
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D. Criado-Ramón, L. G. B. Ruiz and M. C. Pegalajar
Pattern sequence-based models are a type of forecasting algorithm that utilizes clustering and other techniques to produce easily interpretable predictions faster than traditional machine learning models. This research focuses on their application in ene...
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Xiaoyu Huang, Mingming Liu, Samuel T. Turvey, Mingli Lin and Songhai Li
Marine mammals are a diverse group of aquatic animals that exhibit wide variation in body size, living conditions, breeding habitat, social behaviour and phylogeny. Although case studies about prenatal investment in cetaceans and pinnipeds have been inve...
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Zuxuan Song, Fangmei Liu, Wenbo Lv and Jianwu Yan
Exploring the transformation process of urban agricultural functions and its interaction with carbon effects based on regional differences is of great positive significance for achieving a low-carbon sustainable development of agriculture in metropolitan...
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Dhan Lord B. Fortela, Ashton C. Fremin, Wayne Sharp, Ashley P. Mikolajczyk, Emmanuel Revellame, William Holmes, Rafael Hernandez and Mark Zappi
This work focused on demonstrating the capability of unsupervised machine learning techniques in detecting impending anomalies by extracting hidden trends in the datasets of fuel economy and emissions of light-duty vehicles (LDVs), which consist of cars ...
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