Electronic voting, based on cryptographic protocols and blockchain technology, represents an advanced method for ensuring the security, transparency, and reliability of electoral processes. The scientific research presented in this article is devoted to the analysis of trends and advanced technologies in the field of electronic voting. The authors propose a systematic mapping of this area using bibliometric analysis. The research is based on the analysis of scientific articles extracted from the Web of Science and Scopus databases. Bibliometric analysis tools are used to automatically identify key topics and trends in the literature. The results of the study reveal the key directions of the development of electronic voting, including the integration of cryptographic protocols and blockchain technology. Specific aspects include improving privacy through the use of modern cryptographic protocols, creating an immutable register of votes using blockchain technology, ensuring trust through open-source code, and increasing resilience to cyber-attacks. These areas reflect the desire to develop efficient, transparent, and secure electronic voting systems. Based on the identified trends, recommendations are proposed for further research aimed at improving electronic electoral systems and their adaptation to the modern requirements of security and transparency in electoral processes.

The Internet of Things (IoT) technology development speed, along with its integration into smart city infrastructure, requires strong cybersecurity solutions to protect important systems. The research introduces an extensive framework to protect IoT systems and wireless networks through the integration of penetration testing method with anomaly detection and predictive modeling techniques. In this study, we collected real-time network traffic data as part of our methodology before performing penetration tests with Airmon-ng and Wireshark to create enriched attack scenario datasets. Anomalies were identified using an optimized Isolation Forest model, revealing patterns such as unusual activity involving the Tenda_476300 WiFi network. The Tenda_476300 network exhibited frequent authentication packet anomalies, along with other potential misconfiguration or vulnerability indicators. Predictive modeling utilized both logistic regression (LR) and support vector machines (SVM) for binary classification to identify benign from malicious traffic, resulting in high accuracy rates and precise results. XGBoost achieved better performance than Random Forest (RF) across all metrics when performing as a multiclass classifier to identify Denial of Service (DoS), Distributed Denial of Service (DDoS), and brute force attacks. The reliability and robustness of the constructed models were tested using precision, recall, F1 scores, ROC curves, and precision-recall curves during performance evaluation. The anomaly detection and predictive modeling analysis proves that real-time surveillance systems should incorporate these techniques for proactive security threat discovery and defense. The proposed framework delivers a flexible solution for protecting IoT and smart city wireless networks, which helps create safer, resilient urban environments.

Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV and Intel Real Sense libraries are used. The subsystem for features extracting and gestures classification is based on the modified VGG-16 by using the TensorFlow&Keras deep learning framework. Performance of the static gestures recognition system is evaluated using maching learning metrics. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy both at the training and testing stages.

This paper presents the methodology and outcomes of creating the Rail Vista dataset, designed for detecting defects on railway tracks using machine and deep learning techniques. The dataset comprises 200,000 high-resolution images categorized into 19 distinct classes covering various railway infrastructure defects. The data collection involved a meticulous process including complex image capture methods, distortion techniques for data enrichment, and secure storage in a data warehouse using efficient binary file formats. This structured dataset facilitates effective training of machine/deep learning models, enhancing automated defect detection systems in railway safety and maintenance applications. The study underscores the critical role of high-quality datasets in advancing machine learning applications within the railway domain, highlighting future prospects for improving safety and reliability through automated recognition technologies.