Efficient and sustainable electrical grids are crucial for energy management in modern society and industry. Govern-ments recognize this and prioritize energy management in their plans, alongside significant progress made in theory and practice over the years. The complexity of power systems determines the unique nature of power communication networks, and most researches have been focusing on the dynamic nature of voltage stability, which led to the need for dynamic models of power systems. Control strategies based on stability assessments have become essential for managing grid stability, diverging from traditional methods and often leveraging advanced computational techniques based on deep learning algorithms and neural networks. This way, researchers can develop predictive models capable of forecasting voltage stability and detecting potential instability events in real-time, whereas neural networks can also optimize control strategies based on wide-area information and grid response, enabling more effective stability control measures, as well as detecting and classifying disturbances or faults in the grid. This paper explores the use of predictive models to assess smart grid stability, examining the benefits, risks, and comparing results to determine the most effective approach.

Deep learning techniques in computer vision (CV) tasks such as object detection, classification, and tracking can be facilitated using predefined markers on those objects. Selecting markers is an objective that can potentially affect the performance of the algorithms used for tracking as the algorithm might swap similar markers more frequently and, therefore, require more training data and training time. Still, the issue of marker selection has not been explored in the literature and seems to be glossed over throughout the process of designing CV solutions. This research considered the effects of symbol selection for 2D-printed markers on the neural network’s performance. The study assessed over 250 ALT code symbols readily available on most consumer PCs and provided a go-to selection for effectively tracking n-objects. To this end, a neural network was trained to classify all the symbols and their augmentations, after which the confusion matrix was analysed to extract the symbols that the network distinguished the most. The results showed that selecting symbols in this way performed better than the random selection and the selection of common symbols. Furthermore, the methodology presented in this paper can easily be applied to a different set of symbols and different neural network architectures.

The number of loan requests is rapidly growing worldwide representing a multi-billion-dollar business in the credit approval industry. Large data volumes extracted from the banking transactions that represent customers’ behavior are available, but processing loan applications is a complex and time-consuming task for banking institutions. In 2022, over 20 million Americans had open loans, totaling USD 178 billion in debt, although over 20% of loan applications were rejected. Numerous statistical methods have been deployed to estimate loan risks opening the field to estimate whether machine learning techniques can better predict the potential risks. To study the machine learning paradigm in this sector, the mental health dataset and loan approval dataset presenting survey results from 1991 individuals are used as inputs to experiment with the credit risk prediction ability of the chosen machine learning algorithms. Giving a comprehensive comparative analysis, this paper shows how the chosen machine learning algorithms can distinguish between normal and risky loan customers who might never pay their debts back. The results from the tested algorithms show that XGBoost achieves the highest accuracy of 84% in the first dataset, surpassing gradient boost (83%) and KNN (83%). In the second dataset, random forest achieved the highest accuracy of 85%, followed by decision tree and KNN with 83%. Alongside accuracy, the precision, recall, and overall performance of the algorithms were tested and a confusion matrix analysis was performed producing numerical results that emphasized the superior performance of XGBoost and random forest in the classification tasks in the first dataset, and XGBoost and decision tree in the second dataset. Researchers and practitioners can rely on these findings to form their model selection process and enhance the accuracy and precision of their classification models.

With the emerging Internet of Things (IoT) technologies, the smart city paradigm has become a reality. Wireless low-power communication technologies (LPWAN) are widely used for device connection in smart homes, smart lighting, mitering, and so on. This work suggests a new approach to a smart parking solution using the benefits of narrowband Internet of Things (NB-IoT) technology. NB-IoT is an LPWAN technology dedicated to sensor communication within 5G mobile networks. This paper proposes the integration of NB-IoT into the core IoT platform, enabling direct sensor data navigation to the IoT radio stations for processing, after which they are forwarded to the user application programming interface (API). Showcasing the results of our research and experiments, this work suggests the ability of NB-IoT technology to support geolocation and navigation services, as well as payment and reservation services for vehicle parking to make the smart parking solutions smarter.

Distributed Ledger Technologies are one of the pillars of future technologies, prognozing to have a great impact to many aspects of our lives, including social, economic, juristic, security and many others. Bitcoin is still the most popular blockchain currency, but the opportunities to use Distribute Ledger Technologies are much more wide, outperforming financial applications as most known and popular. Besides blockchains, there are also other architectures of Distributed Ledger Technologies. This paper observes and analyses one technology as a very strong alternative to blockchains: hashgraphs, which are promising to outperform blockchains, but also tangles. Basis of their architecture and functionality will be explained and directions and prognosis of the further development will be given. The main paper contribution is a comparison of a hashgraph technology to its concurrent architectures, i.e. blockchains and tangles, considering different segments and different properties that define a quality of Distributed Ledgers.

Connected devices in IoT as well as the smartwatch market are getting more and more popular every year. The main mode of communication in IoT is an easy-to-use MQTT protocol suitable for devices with limited resources and battery power. Tizen is used for platforms such as mobile devices, smartwatches, TVs and even Linux kernel-based IoT devices. In this paper, we explain how MQTT protocol, Tizen operating systems and their architecture work, and suggest one possible implementation of a MQTT protocol for Smartwatches based on the Tizen operating system. We list the types of Tizen applications, develop a native application, and suggest possible future upgrades and appliances in IoT.