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This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.Language is the primordial element for cultural transfer in indigenous communities; if it is not practiced, there is a risk of losing it and with it, a large part of the history of a community. Ecuador is a multicultural and multiethnic country with 18 indigenous peoples. Currently, in this country, some native languages are at risk of disappearing due to factors such as racial discrimination, underestimation of the language, and, above all, the lack of interest and motivation of the new generations to learn this language. Information technologies have made it possible to create mobile applications such as games, dictionaries, and translators that promote the learning of the Kichwa language. However, the acceptance of technology has not been evaluated, nor the intention to involve mobile devices in the process of teaching this language. Subsequently the objective of this work is to explore the acceptance of technology and the use of mobile devices to motivate the learning of the Kichwa language. For this purpose, the mobile application "Otavalo Rimay" was used with several students of a Kichwa language learning center. The methodology used to verify the hypothesis of this work was Design Sciences Research (DSR) together with the theory of acceptance and use of technology (UTAUT). The instrument used for this evaluation was a survey carried out after the use of the mobile application. The statistical analysis of the results obtained indicates characteristics such as the utility and perceived ease of use, positively influence students to motivate the use of mobile devices in learning a language. The results also show the great technological acceptance by students for learning and confirm that currently, mobile learning is accepted for use in education.This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models' accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). Olprinone clinical trial We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper's text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography. For evaluation, a user study is conducted on selected papers from 1,200 documents (comprise about 16,000 references) of an online journal, Journal of Computer Science (J.UCS). The evaluation results indicate that in-text citation frequency has attained higher precision in finding relevant papers than other state-of-the-art techniques such as content, bibliographic coupling, and metadata-based techniques. The use of in-text citation may help in enhancing the quality of existing information systems and digital libraries. Further, more sophisticated measure may be redefined be considering the use of in-text citations.Skin lesions are one of the typical symptoms of many diseases in humans and indicative of many types of cancer worldwide. Increased risks caused by the effects of climate change and a high cost of treatment, highlight the importance of skin cancer prevention efforts like this. The methods used to detect these diseases vary from a visual inspection performed by dermatologists to computational methods, and the latter has widely used automatic image classification applying Convolutional Neural Networks (CNNs) in medical image analysis in the last few years. This article presents an approach that uses CNNs with a NASNet architecture to recognize in a more accurate way, without segmentation, eight skin diseases. The model was trained end-to-end on Keras with augmented skin diseases images from the International Skin Imaging Collaboration (ISIC). The CNN architectures were initialized with weight from ImageNet, fine-tuned in order to discriminate well among the different types of skin lesions, and then 10-fold cross-validation was applied. Finally, some evaluation metrics are calculated as accuracy, sensitivity, and specificity and compare with other CNN trained architectures. This comparison shows that the proposed system offers higher accuracy results, with a significant reduction on the training paraments. To the best of our knowledge and based in the state-of-art recompiling in this work, the application of the NASNet architecture training with skin image lesion from ISIC archive for multi-class classification and evaluated by cross-validation, represents a novel skin disease classification system.