The interest in human phenotypes has leveraged interdisciplinary efforts encouraging a better understanding of the broad spectrum of psychological and behavioral disorders. Moreover, the usage of mobile and wearable d...
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Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature o...
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The analysis of human mobility behavior through computational techniques finds applications in various domains and provides valuable insights for urban planning, transportation services, and a deeper understanding of ...
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The analysis of human mobility behavior through computational techniques finds applications in various domains and provides valuable insights for urban planning, transportation services, and a deeper understanding of human interactions in Smart Cities and Smart Environments. In this scenario, this study presents a Systematic Literature Review (SLR) with the following main question: How are computational techniques being used to analyse human mobility behavior in Smart Cities and Smart Environments? A total of 5989 articles were initially found and filtered, resulting in 56 articles reviewed. As the main contributions, this study provides responses to 19 research questions. A list of the challenges and the computational techniques identified is provided. The algorithms, machine learning techniques and data-sources used by the reviewed studies are also presented and organized through taxonomies. A comprehensive discussion of the identified techniques is conducted, finishing with a compilation of challenges, open issues and research opportunities. To the best of our knowledge, this is the first study that reviewed human mobility behavior covering a wide range of scenarios, including urban mobility, public transport, points and regions of interest, ridesharing, bike-sharing, traffic analysis, driving behavior, electric vehicle charging stations planning, mobility on demand, crowd analysis and others.
Sedimentary rocks are of great importance for the oil and gas industry. Spectral matching using known references made on hyperspectral images is one of the most rapid and cost effective alternatives for the detailed a...
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Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature o...
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Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts’ final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system’s effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance.
In software development, code autocomplete can be an essential tool in order to accelerate coding. However, many of these tools built into the IDEs are limited to suggesting only methods or arguments, often presenting...
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Despite the high growth of the Internet of Things and the multitude of applications that use the information generated, it is estimated that 90 % of these data are not yet fully used. This is because IoT systems are b...
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The Mean Tropospheric Temperature (Tm) is important for the sake of the conversion between Zenith Path Delay (ZPD) to Precipitable Water Vapor (PWV) because of the proportionality constant that takes Tm to be calculat...
The Mean Tropospheric Temperature (Tm) is important for the sake of the conversion between Zenith Path Delay (ZPD) to Precipitable Water Vapor (PWV) because of the proportionality constant that takes Tm to be calculated. In Brazil, Tm modeling’s been traditionally performed using Multiple Linear Regression Models (MLR). However, recent works suggest the use of Deep Learning methods to model Tm values. In this work, we propose models based on Convolutional Neural Networks (CNN) using radiosonde data from 1961 to 2010. The results show that CNN models can outperform the traditional methods considering different statistic metrics like R, standard deviation, and RMSE.
This paper presents a methodology for the indirect calibration of hyperspectral images using radiometric measurements from field samples. While hyperspectral imaging (HSI) is essential in remote sensing, its accuracy ...
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ISBN:
(数字)9798331513139
ISBN:
(纸本)9798331513146
This paper presents a methodology for the indirect calibration of hyperspectral images using radiometric measurements from field samples. While hyperspectral imaging (HSI) is essential in remote sensing, its accuracy depends on precise calibration, which is often challenging in the field. Most approaches rely on a single standard panel used as absolute reference and the empirical line method. This often leads to mistakes and uncertainties in the analysis. In this paper we propose an alternative that utilizes radiometric data from rock samples with known spectral properties to indirectly calibrate hyperspectral images. The results show specific improvements in accuracy, particularly in the shape of the curves, compared to standard calibration method. The calibrated images offer better material differentiation and more reliable spectral signatures, which are crucial for geological mapping and mineral exploration. This approach can enhance the reliability of hyperspectral data analysis in geological and environmental studies, especially in situations where time and logistics are challenging.
Sedimentary rocks are of great importance for the oil and gas industry. Spectral matching using known references made on hyperspectral images is one of the most rapid and cost effective alternatives for the detailed a...
详细信息
ISBN:
(数字)9798331513139
ISBN:
(纸本)9798331513146
Sedimentary rocks are of great importance for the oil and gas industry. Spectral matching using known references made on hyperspectral images is one of the most rapid and cost effective alternatives for the detailed analysis in these areas, since they can adequately catch the specific absorption features of the multiple minerals available. In many initiatives, scientists run the methods with standard spectral references, usually from spectral libraries or samples from other places. However, for the success of their application, they must be used along with spectral references that accurately describe the actual constituents of the surface. In this paper we scrutinize three different ways for mineralogical mapping performed by spectral matching techniques. The results showed that using reference spectra from samples collected directly on the surveyed environment can considerably improve the results, reaching high scores, pointing out that many places share common substances, but each specific material has particular differences that cannot be generalized.
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