2024
Authors
Canedo, D; Hipólito, J; Fonte, J; Dias, R; do Pereiro, T; Georgieva, P; Gonçalves Seco, L; Vázquez, M; Pires, N; Fábrega Alvarez, P; Menéndez Marsh, F; Neves, AJR;
Publication
REMOTE SENSING
Abstract
The increasing relevance of remote sensing and artificial intelligence (AI) for archaeological research and cultural heritage management is undeniable. However, there is a critical gap in this field. Many studies conclude with identifying hundreds or even thousands of potential sites, but very few follow through with crucial fieldwork validation to confirm their existence. This research addresses this gap by proposing and implementing a fieldwork validation pipeline. In northern Portugal's Alto Minho region, we employed this pipeline to verify 237 potential burial mounds identified by an AI-powered algorithm. Fieldwork provided valuable information on the optimal conditions for burial mounds and the specific factors that led the algorithm to err. Based on these insights, we implemented two key improvements to the algorithm. First, we incorporated a slope map derived from LiDAR-generated terrain models to eliminate potential burial mound inferences in areas with high slopes. Second, we trained a Vision Transformer model using digital orthophotos of both confirmed burial mounds and previously identified False Positives. This further refines the algorithm's ability to distinguish genuine sites. The improved algorithm was then tested in two areas: the original Alto Minho validation region and the Barbanza region in Spain, where the location of burial mounds was well established through prior field work.
2024
Authors
Silva, NA; Rocha, VV; Ferreira, TD;
Publication
MACHINE LEARNING IN PHOTONICS
Abstract
This communication explores an optical extreme learning architecture to unravel the impact of using a nonlinear optical media as an activation layer. Our analysis encloses the evaluation of multiple parameters, with special emphasis on the efficiency of the training process, the dimensionality of the output space, and computing performance across tasks associated with the classification in low-dimensionality input feature spaces. The results enclosed provide evidence of the importance of the nonlinear media as a building block of an optical extreme learning machine, effectively increasing the size of the output space, the accuracy, and the generalization performances. These findings may constitute a step to support future research on the field, specifically targeting the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions.
2024
Authors
Bonci, EA; Kaidar Person, O; Antunes, M; Ciani, O; Cruz, H; Di Micco, R; Gentilini, OD; Heil, J; Kabata, P; Romariz, M; Gonçalves, T; Martins, H; Borsoi, L; Mika, M; Pfob, A; Romem, N; Schinkoethe, T; Silva, G; Bobowicz, M; Cardoso, MJ;
Publication
JOURNAL OF CLINICAL ONCOLOGY
Abstract
2024
Authors
Pinheiro, MR; Fernandes, LE; Carneiro, IC; Carvalho, SD; Henrique, RM; Tuchin, VV; Oliveira, HP; Oliveira, LM;
Publication
JOURNAL OF BIOPHOTONICS
Abstract
With the objective of developing new methods to acquire diagnostic information, the reconstruction of the broadband absorption coefficient spectra (mu a[lambda]) of healthy and chromophobe renal cell carcinoma kidney tissues was performed. By performing a weighted sum of the absorption spectra of proteins, DNA, oxygenated, and deoxygenated hemoglobin, lipids, water, melanin, and lipofuscin, it was possible to obtain a good match of the experimental mu a(lambda) of both kidney conditions. The weights used in those reconstructions were estimated using the least squares method, and assuming a total water content of 77% in both kidney tissues, it was possible to calculate the concentrations of the other tissue components. It has been shown that with the development of cancer, the concentrations of proteins, DNA, oxygenated hemoglobin, lipids, and lipofuscin increase, and the concentration of melanin decreases. Future studies based on minimally invasive spectral measurements will allow cancer diagnosis using the proposed approach.
2024
Authors
Pajón-Sanmartín, A; de Arriba-Pérez, F; García-Méndez, S; Burguillo, JC; Leal, F; Malheiro, B;
Publication
GOOD PRACTICES AND NEW PERSPECTIVES IN INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 1, WORLDCIST 2024
Abstract
This work applies Natural Language Processing (NLP) techniques, specifically transformer models, for the emotional evaluation of open-ended responses. Today's powerful advances in transformer architecture, such as ChatGPT, make it possible to capture complex emotional patterns in language. The proposed transformer-based system identifies the emotional features of various texts. The research employs an innovative approach, using prompt engineering and existing context, to enhance the emotional expressiveness of the model. It also investigates spaCy's capabilities for linguistic analysis and the synergy between transformer models and this technology. The results show a significant improvement in emotional detection compared to traditional methods and tools, highlighting the potential of transformer models in this domain. The method can be implemented in various areas, such as emotional research or mental health monitoring, creating a much richer and complete user profile.
2024
Authors
Branquinho, R; Briga-Sá, A; Ramos, S; Serôdio, C; Pinto, T;
Publication
ELECTRONICS
Abstract
Agriculture being an essential activity sector for the survival and prosperity of humanity, it is fundamental to use sustainable technologies in this field. With this in mind, some statistical data are analyzed regarding the food price rise and sustainable development indicators, with a special focus on the Portugal region. It is determined that one of the main factors that influences agriculture's success is the soil's characteristics, namely in terms of moisture and nutrients. In this regard, irrigation processes have become indispensable, and their technological management brings countless economic advantages. Like other branches of agriculture, the wine sector needs an adequate concentration of nutrients and moisture in the soil to provide the most efficient results, considering the appropriate and intelligent use of available water and energy resources. Given these facts, the use of renewable energies is a very important aspect of this study, which also synthesizes the main irrigation methods and examines the importance of evaluating the evapotranspiration of crops. Furthermore, the control of irrigation processes and the implementation of optimization and resource management models are of utmost importance to allow maximum efficiency and sustainability in this field.
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