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Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2–97.6% overall accuracy without PCA and 97.6–100% with PCA for positive cases identification, respectively.

Unmanned aerial vehicles (UAVs) are used to physically interact with humans and urgent data collection in a shared workspace. They are flying collaborative robots that can be remotely supervised through a software-controlled flight plans in their embedded systems, working in conjunction with built-in sensors and global positioning system modules. Drones are widely used in safety and critical intelligent transportation systems applications to enhance performance and well-being. UAVs can fly over the road to monitor and observe possible traffic destructions. Path planning (PP) process in UAVs gained a great interest in the literature. That is due to its significant effects at the application-level performance. PP of UAVs in the internet of things era can be achieved via several approaches. As the name implies, static-based PP is dependent on a static set of data collectors to relay and report data. For a particular engineering area, transportation engineering, the mathematical methods, and related algorithms are commonly applied to real cases. No sponsor

Grid-based deployments of Wireless Sensor Networks (WSNs) are widely used in a multiplicity of applications. However, practical factors such as communication irregularity and placement uncertainty have to be considered for more efficient deployments. In this paper, we examine connectivity properties of the 3D grid-based deployment when sensor placements are subject to random errors around their corresponding grid locations and hindrances to wireless communication channels exist. A generic approach is proposed to evaluate the average connectivity of the deployed network. This generic approach is independent of the grid-shape, random error distributions, and the environment wireless channel characteristics. The average connectivity is computed numerically and verified via extensive simulations. Based on the numerical results, quantified effects of positioning errors and grid edge length on the average connectivity are demonstrated. Furthermore, we discuss several ways of achieving efficient grid-based deployment planning for connectivity, and illustrate these approaches through numerical examples.

In this paper, we propose two schemes for sensor and relay node placement in environmental sensing applications. The first scheme aims at maximizing the network lifetime by reducing the total energy consumption. The second does so while maintaining fault-tolerance constraints. It guarantees a lower bound on the minimum required number of faulty nodes. Both schemes are based on a 3-D hierarchical architecture, in which nodes are placed on grid vertices to limit the search space. We divide the lifetime of the network into fixed-length rounds and find the placement which reserves more energy in each round to prolong the lifetime. These problems are formulated via Integer Linear Programs (ILPs). An ILP solver is used to find the optimal placement of nodes in addition to multi-hop routing from the sensors to the base-station in both schemes. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant scheme introduces a significant lifetime extension as compared to the first one under the same harsh operational conditions.

This chapter presents the existing studies related to the use of unmanned aerial vehicles in the internet of things era. It outlines problem description and the proposed framework. The chapter discusses the performance metrics, results, and findings of the study. It aims to monitor the intelligent transport system with drones by monitoring a specific target or area. The chapter proposes a cost-effective framework to minimize the total number of drones required to monitor the environment while providing the maximum visionary coverage for the target. Optimal drone placement problem and cost-effective target coverage of drones are extensively investigated in several studies. For example, the optimal placement of a group of drones is considered in, with the assumption that a large number of drones are available to cover a group of mobile targets. The chapter deals with a cost minimization problem related to the optimal placement of drones to monitor a set of static or dynamic targets. No sponsor

Smart tourism destination (STD) concept has taken serious attention as a result of the smart city initiatives. Technology connects all organizations, entities, activities, and elements. Tourism is a multidimensional service system covering different actors and organizations. When a tourism destination gets smarter, the tourists’ needs and demands are expected to be fulfilled more efficiently to create a better tourist experience. This paper aims to examine the content of smart tourism destination and its link with smart city addressing a model for Antalya as a candidate for a smart tourism destination. No sponsor

Emotional artificial neural network (EANN) is a cutting-edge artificial intelligence method that has been used by researchers in the engineering and medical sciences over the recent years. First introduced in the 1999s, EANN is the combination of physiological and neural sciences for investigation of complex processes. Rainfall-runoff is a complex hydrological process that may be modeled by EANN methods to attain information about the response of a catchment to a rainfall event. In practice, the response is surface runoff either in the form of streamflow or flood in the catchment of interest. Thus, a reliable rainfall-runoff model is an inevitable component of a watershed so that decision-makers may use it to reduce the relevant vulnerability against extreme rainfall events. Undoubtedly, one way to empower the capabilities of rainfall-runoff models is the integration of recent achievements in the Internet of Things (IoT) with robust modeling algorithms such as EANN. Relying on the huge amount of knowledge within IoT components, the hybrid IoT-EANN can yield in the high-resolution space-time estimations of runoff that is a practical way to mitigate potential hazards of flooding through real time or in advance actions. With this chapter, we provide a short overview of the state-of-the-art EANN and its application in rainfall-runoff modeling. In addition, a concise review of the applications of IoT in hydro-environmental issues is provided. The chapter reveals that integrations of IoT with hydro-environmental studies are in their infancy. Being a new class of investigation, there is no hybrid rainfall-runoff model within the literature coupling IoT technology with artificial intelligence. No sponsor