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Chronic kidney disease is a rising health issue that affects millions of people worldwide. Early detection and characterization of this disease is essential for effective management and control. This disease is associated with several serious health risks, such as cardiovascular disease, increased risk of stroke, and end-stage renal disease, which can be effectively prevented by early detection and treatment. Medical scientists rely on machine learning algorithms to diagnose the disease accurately at its outset. Recently, adding value to healthcare is being accomplished through the integration of machine learning algorithms into mobile health solution. Considering this, this paper proposes a predictive model of three machine learning classifiers, including Support Vector Machine, Decision Tree, and Multilayer Perceptron for chronic kidney disease prediction. The performance of the model was assessed using confusion matrix and executed in popular machine learning software tools such as WEKA and Rapid Minor. The study found that support vector machine yielded the highest accuracy rate of 98% in predicting chronic kidney disease in WEKA among other standard classifiers by using 10-fold cross validation. In addition, the proposed prediction model has been compared with existing models in terms of accuracy, sensitivity, and specificity. The experimental results indicate that the proposed predictive model shows promising results. These findings could integrate with the development of mobile health solution and other innovative approaches to prevent and treat this debilitating condition. [ABSTRACT FROM AUTHOR]

In this work we propose an integrated system to manage and enhance the museums visiting experience for the general public, providing a more comfortable, intelligent and interactive way to visit multiple museums without the need for several mobile applications, whilst giving a tool to museum curators that allows them to easily manage and extend the content displayed to each individual visitor. For that purpose, our approach was to develop a single mobile application, a module to an open-source Content Management System (CMS), namely Drupal 8, and incorporate location information inferred by the capture of BLE's signals (beacons). The proposed prototype features an Android application that will be used by the visitors where they can access the available information about several museums, and combined with BLE's, will display information about the museum and spaces of the museum where the visitor is (or is planning to visit). This application gives the option to choose from several languages that are provided and managed by each museum. [ABSTRACT FROM AUTHOR]

In this work we propose an integrated system to manage and enhance the museums visiting experience for the general public, providing a more comfortable, intelligent and interactive way to visit multiple museums without the need for several mobile applications, whilst giving a tool to museum curators that allows them to easily manage and extend the content displayed to each individual visitor. For that purpose, our approach was to develop a single mobile application, a module to an open-source Content Management System (CMS), namely Drupal 8, and incorporate location information inferred by the capture of BLE's signals (beacons). The proposed prototype features an Android application that will be used by the visitors where they can access the available information about several museums, and combined with BLE's, will display information about the museum and spaces of the museum where the visitor is (or is planning to visit). This application gives the option to choose from several languages that are provided and managed by each museum. [ABSTRACT FROM AUTHOR]

The pressure of traffic on modern cities keeps growing. More and more vehicles flow into the city draining the existing parking resources and increasing traffic congestions and fueling the pollution increase. In this paper we present a solution for a low-cost smart parking system and all the software and hardware components that were developed and integrated in a smart parking prototype. The developed prototype is based on Arduino for the sensor network and on the Raspberry Pi for the gateway device. This paper also describes some of the technology aspects used in the communication between the sensors and the gateway - based on the ZigBee protocol. In addition to the hardware part of the prototype, the used backend architecture and the mobile application, that enables the user to find quickly and efficiently a parking spot, are also analyzed in this paper. [ABSTRACT FROM AUTHOR]