Introduction: The use of mobile applications has increased in the last years. Most of them require the knowledge of the user location, either for their core service or for marketing purposes. Location-based services (LBS) offer context-based assistance to users based on their location. Although these applications ask the user for permission to use their location and even explain in detail how this information will be used in its terms and conditions, most users are not aware or even interested in the fact that their location information is stored in databases and monetized by selling it to third-party companies. Regarding this situation, we developed a study with the aim to assess perception, concerns and awareness from users about their location information. Methods: This work is based on an exploratory survey applied to the university community, mainly from the North Coast of Colombia, to measure the perception of location privacy of users with mobile devices. The questionnaire was applied using Google Forms. The survey has nineteen questions organized in three sections: personal information, identification of privacy and privacy management. These questions were designed to know the users’ perceptions of privacy concerns in LBS and any actions they take to preserve it. Results: The results show that, in general, the respondents do not have a real concern regarding the privacy of their geolocation data, and the majority is not willing to pay to protect their privacy. Conclusions: This type of surveys can generate awareness among participants about the use of their private information. The results expose in this paper can be used to create government policies and regulations by technology companies about the privacy management.
Hyperspectral Remote Sensing (HRS) is an emergent, multidisciplinary paradigm with several applications, which are developed on the basis of material spectroscopy, radiative transfer, and imaging spectroscopy. HRS plays a vital role in agriculture for crops type classification and soil prediction. The recently developed artificial intelligence techniques can be used for crops type classification using HRS. This study develops an Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crop Type Classification (ISCO-DTLCTC) model. The ISCO-DTLCTC technique comprises initial preprocessing step to extract the region of interest. The information gain-based feature reduction technique is employed to reduce the dimensionality of the original hyperspectral images. In addition, a fusion of 3 deep convolutional neural networks models namely, VGG16, SqueezeNet, and Dense-EfficientNet perform feature extraction process. Furthermore, sine cosine optimization (SCO) algorithm with Modified Elman Neural Network (MENN) model is applied for crops type classification. The design of SCO algorithm helps to proficiently select the parameters involved in the MENN model. The performance validation of the ISCO-DTLCTC model is carried out using benchmark datasets and the results are inspected under several measures. Extensive comparative results demonstrated the betterment of the ISCO-DTLCTC model over the state of art approaches with maximum accuracy of 99.99%. RÉSUMÉLa t el ed etection hyperspectrale (HRS) est une technologie emergente et multidisciplinaire ayant plusieurs applications d evelopp ees sur la base de la spectroscopie des mat eriaux, du transfert radiatif et de la spectroscopie des images. L'HRS joue un rôle essentiel en agriculture pour la classification des types de cultures et la pr evision des sols. Les techniques d'intelligence artificielle (IA) r ecemment d evelopp ees peuvent être utilis ees pour la classification des types de cultures a l'aide de HRS. Cette etude d eveloppe un mod ele intelligent d'optimisation du sinus-cosinus avec une classification des types de cultures bas ee sur l'apprentissage par transfert profond (ISCO-DTLCTC). La technique ISCO-DTLCTC comprend une etape initiale de pr etraitement pour extraire la r egion d'int erêt (RoI). La technique IGFR (Information Gain Based Feature Reduction) est utilis ee pour r eduire la dimensionnalit e des images hyperspectrales originales. Une fusion de trois mod eles DCNN (Deep Convolutional Neural Networks), a savoir VGG16, SqueezeNet et Dense-EfficientNet, effectue un processus d'extraction des principales caract eristiques. En outre, l'algorithme d'optimisation du sinuscosinus (SCO) avec le mod ele MENN (Modified Elman Neural Network) est appliqu e a la classification des types de cultures. La conception de l'algorithme SCO permet de s electionner efficacement les param etres impliqu es dans le mod ele MENN. La validation des performances du mod ele ISCO-DTLCTC est effectu ee a l'aide d'ensembles de donn ees de r ef erence et
In this paper, we propose a Four-Dimensional Variational (4D-Var) data assimilation framework for wind energy potential estimation. The framework is defined as follows: we choose a numerical model which can provide forecasts of wind speeds then, an ensemble of model realizations is employed to build control spaces at observation steps via a modified Cholesky decomposition. These control spaces are utilized to estimate initial analysis increments and to avoid the intrinsic use of adjoint models in the 4D-Var context. The initial analysis increments are mapped back onto the model domain from which we obtain an estimate of the initial analysis ensemble. This ensemble is propagated in time to approximate the optimal analysis trajectory. Wind components are post-processed to get wind speeds and to estimate wind energy capacities. A matrix-free analysis step is derived from avoiding the direct inversion of covariance matrices during assimilation cycles. Numerical simulations are employed to illustrate how our proposed framework can be employed in operational scenarios. A catalogue of twelve Wind Turbine Generators (WTGs) is utilized during the experiments. The results reveal that our proposed framework can properly estimate wind energy potential capacities for all wind turbines within reasonable accuracies (in terms of Root-Mean-Square-Error) and even more, these estimations are better than those of traditional 4D-Var ensemble-based methods. Moreover, large variability (variance of standard deviations) of errors are evidenced in forecasts of wind turbines with the largest rate-capacity while homogeneous variability can be seen in wind turbines with the lowest rate-capacity.
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