Over the years, artificial intelligence has demonstrated its ability to overcome many challenges in our day-to-day life. The evolution of it inquired more studies about Machine Learning possible solutions for different domains, including health care. The increasing demand for artificial intelligence solutions has brought accessibility to loads of data, including clinical data. The availability of medical records facilitates new opportunities to explore Machine Learning models and their abilities to process a significant amount of data and to identify patterns with the purpose of solving a medical problem. Understanding the applicability of artificial intelligence on this type of data has to be a compelling aim for emergency medicine clinicians. This paper focuses on the general clinical problem of the complex correlation between medical records and later diagnosis and, especially, on the process of emergency department triage which uses the Emergency Severity Index (ESI) as triage protocol. This study presents a comparison between three different Machine Learning models, such as Logistic Regression, Random Forest Tree and NN-Sequentail, with the purpose of classifying patients with an emergency code. We conducted four experiments because of imbalanced data. A web-based application was developed to improve the triage process after our theoretical and exploratory results. Overall, in all experiments, the NN-Sequential model had better results, having, in the first experiment, a ROC-AUC score for each ESI emergency code of: 0.59%, 0.76%, 0.71%, 0.78% 0.64%. After applying methods to balance the data, the model yielded a ROC-AUC score for each emergency code of 0.72%, 0.75%, 0.69%, 0.74%, 0.78%. In the last experiment consisting of a three-class classification problem, the NN-Sequential and Random Forest Tree models had similar metric outcomes, and the NN-Sequential algorithm had a ROC-AUC score for each emergency code of: 0.76%, 0.72%, 0.84%. Without any doubt, our research results presented in this paper endorse this tremendous curiosity in Machine Learning applications to enrich aspects of emergency medical care by applying specific methods for processing both medical data and medical records.

Information technology has massively transformed the world of business over the past fifty years - first individual functional areas within companies (“first wave”), later increasingly also cross-divisional value-added processes and trade (“second wave”). Those companies that recognized the tremendous economic potential of these upheavals and consistently adapted, profited enormously - many others, however, fell dramatically. At the same time, innovative startups emerged that successfully created and occupied new markets. With the Internet of Things (IoT), the third digital wave is currently rolling up. Their impact will be enormous - both for our everyday lives and for many industries that have so far been largely spared the disruptive power of digital transformation. Accordingly, the challenges facing most companies today are: understanding more complex competition, acquiring new digital technologies, making existing offerings smart, developing new services, networking production, efficiently analyzing vast amounts of data, and building viable organizations to push all this forward. The IoT is a driver for digitization. By analyzing machine data, the use of sensors and the intelligent real-time processing of huge amounts of data in the cloud, new business models are created. With the information gained, companies are able to improve their value chain. However, one of the most difficult issues in this context for many companies is how they can further develop their existing business model or establish successful new business models that will be based on new technologies and IoT. To investigate resulting impacts, we draw on the existing business models and deduct specifics for the Internet of Things. Building on this, in order to reach the aims of the paper the authors will use a descriptive research method and a case study in order to present how new business models work with the IoT.

The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are affected by the underlying hardware components and networks. The overall speedup of the platforms, which are implemented using the new 5G networks, and the capabilities of new performant IoT devices may be wasted by an inadequate combination of authentication services and security mechanisms, by the architectural misplacing of the encryption services, or by the inappropriate subsystems scaling. Considering the emerging microservices platforms, the Spring Boot alternative is used to implement data generation services, IoT sensor reading services, IoT actuators control services, and authentication services, and ultimately assemble them into a secure microservices architecture. Furthermore, considering the designed architecture, relevant security aspects related to the medical and energy domains are analyzed and discussed. Based on the proposed architectural concept, it is shown that well-designed and orchestrated architectures that consider the proper security aspects and their functional influence can lead to stable and secure implementations of the end user’s software platforms.

Wireless Sensor Networks (WSN) are expected to revolutionize daily life by connecting everyday objects with sensing capabilities, offering numerous opportunities for a wide range of applications. To facilitate the integration and enable all these opportunities to be realized, it becomes a necessity for middleware architectures that: (a) perform well in non-well-defined infrastructures, (b) are able to deal with the large number of users and heterogeneous devices integrated into it (ultra scalable), and (c) enable autonomy of the system overall. This work introduces a bio-inspired middleware optimized for wireless sensor networks proposing a refinement, the regional network, in a work published earlier as a bio-inspired self-adaptive architecture for the Internet of Things, while providing a comparison of other similar middleware approaches and a discussion on the motivating health monitoring scenario.

The integrated collection of personal health data represents a relevant research topic, which is enhanced further by the development of next-generation mobile networks that can be used in order to transport the acquired medical data. The gathering of personal health data has become recently feasible using relevant wearable personal devices. Nevertheless, these devices do not possess sufficient computational power, and do not offer proper local data storage capabilities. This paper presents an integrated personal health metrics data management system, which considers a virtualized symmetric 5G data transportation system. The personal health data are acquired using a client application component, which is normally deployed on the user’s mobile device, regardless it is a smartphone, smartwatch, or another kind of personal mobile device. The collected data are securely transported to the cloud data processing components, using a virtualized 5G infrastructure and homomorphically encrypted data packages. The system has been comprehensively assessed through the consideration of a real-world use case, which is presented.

Some of the most challenging directions in recent theoretical and practical research concern both the web science and the web applications development. Any alive science and any alive programming language should increase its connection with the web domain. Apart from general advantages provided by the functional language programming Haskell , its specific support for the web applications design and implementation is highlighted in this paper. It is also our target to reveal here some mathematical grounds of the web as a modern and very young science. Appropriate examples will be selected from our students’ functional web applications. So, the web ecosystem and particularly those characteristics which are relevant for guiding our students to choose Haskell for developing performant maths-based web solutions are pointed out. All the mathematical evaluations involved in this approach are Haskell based.