Next-generation wireless network applications that combine the Internet of Things (IoT), intelligent edges, and connectivity technologies will benefit every area, including e-health and medical Internet of Things (M-IoT). The fifth generation (5G) mobile network technology cannot match the requirements of emerging mobile apps, which require extreme communication speed, network intelligence, ultra-low latency, comprehensive connectivity, and the capacity to manage diversely related usages. The sixth generation (6G) mobile network technology establishes new performance standards that the fifth generation (5G) mobile network technology could not satisfy. For incredibly immersive applications such as 3D communications and enormous virtual reality (VR)/ extended reality (XR) applications to be economically viable, 6G capabilities must be delivered at a large scale. Deploying several tiny cells to construct ultra-dense networks (UDN) is one option for tackling the extraordinary rise in capacity and coverage needs. The proposed work estimates that only the future 6G networks can deliver a high-performance connection with many connected devices, particularly in challenging situations such as diverse mobility, energetic environments, and extreme density. Accordingly, this article discusses the most current and forthcoming 6G network-compatible advancements to comprehensively review 6G mobile communication technologies in single survey research. At the outset, we thoroughly overview UDN and the 6G system's goals, motivations, requirements, architecture, and conceptual parts.

Real-time object detection with the YOLO family is now deployed in cloud data centres, edge servers, and tiny IoT devices, each operating under different constraints of latency, bandwidth, memory, energy, and cost. In this paper, a deployment-centric survey of YOLO is presented, where YOLO is treated as a scalable family of models embedded in distributed systems rather than a single benchmarked network. First, object detection paradigms and the evolution of YOLO are reviewed, and a macro–meso–micro view of cloud, edge, and IoT deployment is introduced. Then, lightweight architectures and compression techniques—such as tiny and nano variants, efficient backbones, pruning, quantisation, and distillation—are surveyed, and their effects on accuracy, latency, model size, and energy are analysed. On the system side, deployment patterns are summarised, including cloud-centric serving, edge and fog deployments, IoT and tiny-device pipelines, and collaborative hierarchical inference with federated learning. Finally, open challenges and future directions for hardware-aware, deployment-aware YOLO co-design are outlined, and the potential of sustainable YOLO deployments to support UN Sustainable Development Goals in smart cities, healthcare, and environmental monitoring is highlighted.

Companies are excited to maintain high-quality software while reducing the costs of production. DevOps is a contemporary software development life cycle paradigm in which development and operations teams join together during all stages of software development. Nonetheless, security is inadequately integrated inside DevOps. Although there have been attempts to amalgamate security with DevOps, resulting in the emergence of DevSecOps, considerable progress is necessary. The aim of Hybrid Intrusion Detection and Ensemble Learning System (HIDELS) is to present the incorporation of intrusion detection into the continuous monitoring phase of DevOps, hence enhancing DevSecOps. The integration comprises five machine learning (ML) models and assesses the performance of each model independently. Subsequently, the models are consolidated into an ensemble learning (EL) framework to improve overall robustness and provide more stable predictive outcomes. The results of the individual models show the decision tree (DT) classifier outperforming all the remaining models in terms of accuracy, precision, recall, and f1-score, with 99.5%, 99.5%, 99.7%, and 99.6% on average. Conversely, the EL model attained an average of 99.4%, 99.6%, 99.7%, and 99.7% for accuracy, precision, recall, and F1-score, respectively, exceeding the performance of all other individual ML models.

The rapid development of intelligent transportation systems and the Internet of Things (IoT) has increased the need for robust and efficient wireless communication in high-mobility environments, such as vehicle-to-everything networks. Orthogonal frequency division multiplexing is widely used due to its high data rates and resistance to multipath fading. However, in fast-moving scenarios, Doppler effects cause interference between subcarriers, reducing signal quality. This paper proposes a practical nonlinear equalization framework based on the conjugate gradient least squares (CGLS) method to tackle this problem. Two advanced designs—a CGLS-based block decision feedback equalizer and a regularized least squares CGLS sliding window equalizer—are introduced and tested. Simulation results show that the proposed methods significantly reduce bit error rates and achieve up to a 5 dB performance improvement over traditional approaches, while keeping computational costs low enough for real-time IoT applications. This work supports the development of safer, more reliable, and energy-efficient communication systems for smart and sustainable transportation. These contributions align with the United Nations Sustainable Development Goals (SDGs), specifically SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action) by supporting the development of resilient, energy-efficient, and scalable digital communication infrastructures for smart mobility and urban sustainability.

So far, the communication standard development requires specific parameters to achieve the requests of the desired application, most frequently, the connection speed rate. On the other hand, the term Beyond the Fifth Generation (B5G) symbolizes certain specifications required to succeed the future-proof of the Fifth Generation (5G), i.e., the predicted high-level parameters, such as ultra-reliable low-latency communications, massive machine-type communications, and improved mobile broadband, which are essential for the expected high-level future applications; consequently, 5G wireless (cellular) networks must be reconsidered precisely and in-depth to cope with the applications' required high-level standard parameters in B5G. Therefore, it is crucial to develop novel wireless access configurations and technologies that utilize additional spectrum. However, this alone is not sufficient for now. Incorporating technologies such as software-defined networking, cloud computing, machine learning, 3D networking, and network function virtualization into B5G networks is imperative due to raised concerns regarding decentralization, transparency, interoperability, privacy, and security. This page provides a comprehensive overview of B5G's design, functionality, and security, as well as its relationship to cloud computing. Furthermore, the proposed study examines the techniques employed for data transmission in B5G applications, such as Vehicle-to-Vehicle (V2V), Device-to-Device (D2D), and Machine to Machine (M2M) transmissions. Lastly, the proposed study focuses on essential technology based software services, such as healthcare, smart grid, tourism, and agricultural services. These services use the advantages of B5G communication networks and cloud computing. So, the proposed work collects all the necessary information for researches and developers in one article, supported by the most up-to-date references.

Medical visualization is a fundamental method that guarantees the accuracy of data analysis for the reliable and prompt diagnosis of potential diseases, resulting in improving public health. Among the instruments of medical visualization, the methods for delineating edges and identifying possible regions of interest are notable. The proposed study examines color medical images of blood smears exhibiting megaloblastic anemia. To perform the requisite analysis, the input image is enhanced using histogram equalization and segmented into multiple color channels of the RGB format. The wavelet theory of the db1 mother wavelet is employed to identify edges in picture objects. The evaluation of the acquired results relies on visual comparison and widely recognized metrics: niqe, brisque, and entropy. The results indicate that decomposing the original image into data corresponding to distinct color channels yields greater information. For example, the value of the entropy parameter for individual color channels exceeds the value of this parameter for the image as a whole, which enhances the findings, both within the context of each color channel and in comparison, to the grayscale format, which is crucial for employing wavelet theory in image processing. The acquired information can be utilized based on the issue formulation necessary for diagnosing the ailment and making educated decisions.

The internet of things (IoT) is a key element of the future internet, enabling the acquisition and transfer of data to improve efficiency. One challenge in IoT networks is managing the energy consumption of nodes. IoT innovation constantly evolves dynamically, contributing significantly to sustainable cities and economies. Clustering techniques can help conserve energy and extend the operational lifespan of network nodes. Cluster heads (CH) manage all cluster member (CM) nodes within their group, establishing intra-cluster and inter-cluster connections. Enhancing the CH selection process can further prolong the network lifespan. Various algorithms aim to extend the active duration of IoT nodes and the overall network lifespan. A comparison of the five algorithms shows that one algorithm is better than the others in some cases. This paper discusses how fusion techniques using the random forest (RF) algorithm can enhance energy efficiency in IoT networks. Five algorithms are compared using RF, a robust machine-learning algorithm renowned for its ensemble learning capabilities. It selects the best one based on active nodes per round, residual energy for each round, and the average end-to-end delay.

This paper presents a hybrid methodology for the thematic modeling of text corpora related to cybersecurity in educational environments. The proposed approach integrates informed priors for the latent Dirichlet distribution, which are derived from two independent sources: class-oriented c-TF-IDF profiles and sparse non-negative x matrix factorization bases. Furthermore, a coregularization mechanism for topic dictionaries, based on Jensen-Shannon divergence, has been introduced to ensure their stability. Visualization of the modeling results is accomplished by comparing the detected topics with the control domains of the ISO/IEC 27001 and NIST SP 800-53 standards. For practical application, visualizations based on t-SNE were developed to project topics, controls, and evidence into a two-dimensional space. Additionally, compact $\mathrm{C}-\mathrm{I}-\mathrm{A}$ glyphs were created to facilitate a rapid assessment of the topics' impact on confidentiality, integrity, and availability. The quality of the models is assessed through an analysis of coherence measures (NPMI/UMass), key vocabulary diversity, and stability metrics, specifically the adjusted Rand index and the silhouette coefficient. This methodology is expected to enhance the coherence and stability of the identified themes. The expectation is empirically supported by the values of NPMI, UMass, and the silhouette coefficient, which in turn could significantly streamline compliance auditing processes in educational environments that integrate artificial intelligence technologies.

Mitral Valve (MV) pathologies such as Mitral Valve Prolapses (MVP), Mitral Stenosis (MS), and type III regurgitation should be diagnosed as early as possible for the better management of the patients. This research work presents a method for the classification of MV diseases using echocardiographic images and texture analysis of the images using Gray Level Co-occurrence Matrix (GLCM) features in conjunction with Machine Learning (ML) classifiers. Initially, a Convolutional Neural Network (CNN) was employed to categorize echocardiographic images into two standard views: Apical Four-Chamber (A4C) and Parasternal Long-Axis (PLA). Next, the energy, contrast, correlation, and the entropy of GLCM-based texture features were obtained. The features were then fed into ML classifiers such as Random Forest (RF), Neural Networks (NN), Ensemble models to classify MV conditions. In the A4C view, the Neural Network Classifier (NNC) obtained an accuracy of 85% while in the Parasternal Long Axis (PLA) view, the accuracy was 84%. Some of the features of GLCM that were deemed important in the performance of the model were revealed. The results show that combining GLCM texture analysis with ML provides a potential way of improving the precision and reliability of MV disease diagnosis. The findings of this study contribute to advancements in cardiovascular disease detection by integrating machine learning techniques with echocardiographic analysis, ultimately supporting efforts to enhance public health and early disease diagnosis, in alignment with global healthcare initiatives.

In the context of digitalization and the growing dependence of business on electronic communications, data protection is becoming a key element of corporate security. The purpose of this article is to analyze modern cybersecurity threats in the field of business communication and to determine effective methods of information protection. The relevance of the study is due to the increase in the number of cyberattacks aimed at intercepting business correspondence, leaking confidential data and compromising corporate communication channels. The research methodology includes a systemic analysis of scientific literature, a comparative review of cybersecurity solutions, as well as a case analysis of the practices of leading companies. The analysis identified the most vulnerable points in business communications - email, instant messengers and cloud platforms. Encryption technologies, two-factor authentication and the use of secure communication protocols are considered. The results of the study confirm the need for an integrated approach to information security in business, including not only technical measures, but also personnel training. Practical application consists in the formation of recommendations for the development of a corporate cybersecurity strategy that ensures resistance to threats in the communication space.

The increasing complexity and proliferation of modern cyberattacks, such as modern Persistent Threats (APTs), ransomware, and encrypted threats, have rendered classic stateful firewalls obsolete. Next-Generation Firewalls (NGFWs) have become indispensable to protection, featuring deep packet inspection, intrusion prevention systems, application control, and threat intelligence. This survey provides an in-depth review of the evolution, capabilities, and effectiveness of NGFWs in combating modern cyber threats, which is needed for various applications, such as social protection system, and social determinants of health,. Our review involves support from a literature review spanning 2015 to 2025. We combine information from academic research and industry studies to evaluate how well technologies that support NGFWs work, focusing on their strengths in application-layer visibility and threat mitigation. The paper talks about big problems, like how advanced features like SSL/TLS inspection can slow down performance and how security protocols need to be adaptable and driven by AI. Finally, we end with suggestions for how to successfully deploy and ideas for future research, such as how to combine artificial intelligence and zero-trust architecture to make next-generation firewalls more effective against new threats.

In the context of the digital transformation of the economy, cost management is acquiring a new dimension, emphasizing the integration of automat-ed and analytical solutions into corporate decision-making. The purpose of this study is to develop a structured approach to evaluating the effectiveness of cost optimization through the application of digital technologies within business environments. The relevance of the topic stems from the growing need to enhance transparency, speed, and accuracy in managerial processes amid volatile markets and increasingly complex organizational structures. The need for the research arises from the lack of comprehensive and adaptable models for assessing the actual impact of digital tools on cost structures across various industries. The methodology involves the development of a system of key performance indicators (KPIs) that reflect the degree of reduction in direct and indirect expenses following the implementation of digital solutions. The study employs methods such as factor analysis, calculation of relative deviations, and comparative evaluation using “before-and-after implementation” models across different cost categories. The research perspectives include further adaptation of assessment models to specific industry contexts and the refinement of analytical indicators to support integrated digital cost transformation.

In the contemporary digital landscape, cybersecurity must be regarded not merely as a technological matter but as an issue with profound ethical implications. The article analyzes the various ethical concerns inherent in modern cybersecurity advancements, encompassing data privacy, mass surveillance, algorithmic discrimination, corporate equality, and cyber warfare. The study achieves its aim through an analytical examination of scholarly literature and by presenting case studies, including Facebook-Cambridge Analytica, the Stuxnet cyber weapon and more, to demonstrate how ethical lapses erode confidence, civil rights, and democratic principles. The study presents a more extensive valid tiered ethical cybersecurity model (TECM) than prior research due to its integrative perspective that connects normative ethical theories with practical regulatory methods, suggesting a multi-domain approach that has not been statistically assessed in earlier studies. The study clarifies the varying legal status of international guidelines, the mechanisms of consent, and the instruction of ethical principles to cybersecurity professionals, which will increase the applicability of recommendations across various legal and cultural contexts. The paper emphasizes the necessity of safeguarding the digital domain grounded in equity, responsibility, and human dignity. The findings underscore the necessity for practitioners and policymakers to integrate accountability, transparency, fairness, and proportionality at all stages of cybersecurity planning and implementation. The tiered approach can inform regulations, corporate governance principles, and incident response protocols. The paper provides pragmatic guidance for non-academic audiences on mitigating AI bias in cybersecurity solutions, ensuring corporate accountability in data management, and balancing privacy with national security.

Since melanoma is one of the most aggressive forms of skin cancer, early and accurate detection is essential to improving patient outcomes. This study proposes a deep learning framework based on a four-layer Convolutional Neural Network (CNN) to automatically detect melanoma from dermoscopic images. The model's 0.89 AUC-ROC score and 85.2% accuracy showed strong diagnostic capability. The model's robustness in high-recall scenarios was validated by the results of additional performance evaluations using precision, recall, and F1-score. CNN-based techniques outperformed traditional machine learning techniques in melanoma detection. The study also used explainable AI techniques to highlight important image regions that affect the model's decisions in order to address the issue of clinical trust. Despite these developments, it is still very difficult to identify melanoma in its early stages, which emphasizes the need for more study into fusing clinical knowledge with AI-powered systems.

Enhancing the performance of 5ph-IPMSM control plays a crucial role in advancing various innovative applications such as electric vehicles. This paper proposes a new reinforcement learning (RL) control algorithm based twin-delayed deep deterministic policy gradient (TD3) algorithm to tune two cascaded PI controllers in a five-phase interior permanent magnet synchronous motor (5ph-IPMSM) drive system based model predictive control (MPC). The main purpose of the control methodology is to optimize the 5ph-IPMSM speed response either in constant torque region or constant power region. The speed responses obtained using RL control algorithm are compared with those obtained using four of the most recent metaheuristic optimization techniques (MHOT) which are Transit Search (TS), Honey Badger Algorithm (HBA), Dwarf Mongoose (DM), and Dandelion-Optimizer (DO) optimization techniques. The speed response are compared in terms of the settling time, rise time, maximum time and maximum overshoot percentage. It is found that the suggested RL based TD3 give minimum settling time and relatively low values for the rise time, max time and overshoot percentage which makes the RL provide superior speed responses compared with those obtained from the four MHOT. The drive system speed responses are obtained in the constant torque region and constant power region using MATLAB SIMULINK package.

Medical image analysis is an important task in diagnosing various diseases, among which the study of megaloblastic anemia stands out, resulting in improving the public health. The peculiarity of processing the corresponding images is the selection of the edge for all objects of interest, including the details of the structure of megaloblastic anemia cells. It is shown that for these purposes it is advisable to use the ideology of wavelets. Based on this, the paper considers the issues of the influence of the wavelet type on the selection of the edge in images with megaloblastic anemia cells with changed contrast in the RGB and HSV color spaces. The wavelets considered in this paper include gaus1, haar, db2, and bior1.1. For the purpose of comparing the results, such quality assessments as niqe, brisque, and entropy are used, as well as a visual comparison of the obtained results. It is shown that for selecting the edge and potential areas of interest in images with megaloblastic anemia cells, it is advisable to use the RGB space. It is also noted that the gaus1 wavelet allows for efficient allocation of potential areas of interest, while the haar and bior1.1 wavelets allow for allocation of the edges of objects of interest. The results are presented in the form of various figures and tables.

Intelligent Reflecting Surfaces (IRSs) offer a revolutionary approach to wireless communication by dynamically controlling the properties of electromagnetic waves. By manipulating wavefronts without requiring radio frequency links, IRSs can enhance spectrum and energy efficiency, reduce costs, and improve overall system performance. Unmanned Aerial Vehicles (UAVs) have garnered significant attention due to their mobility and adaptability. However, real-time data transmission between UAVs can be hindered by obstacles and security risks. By combining IRSs and UAVs, we can create unparalleled opportunities in demanding environments. Through coordinated movements and intelligent wave reflection, both systems can optimize wireless signal propagation. This paper provides a comprehensive review of IRS-assisted UAV communications. We explore existing research, introduce novel technologies and applications, and highlight potential research directions for developing low-cost and energy-efficient wireless systems. Additionally, we address current challenges and outline promising areas for future study.

The research examines the potential for adaptive planning in the trajectory construction of a collaborative manipulator in the presence of humans, such as public health services or crowded workplaces . Mathematical models are proposed that integrate the gradient field of potential functions, adaptive weighting of social repulsion, and the change in the motion vector through fuzzy logic. The models provide safe and effective trajectories considering the operator’s proximity, obstructions, and the target. The proposed models are examined through their software implementation in the Python environment. These techniques facilitated the simulation of effector motion, the construction of a gradient field, and the assessment of system behavior under varying environmental configurations. The results indicate successful evasion of physical objects and the human comfort zone while achieving the intended objectives. The developed models possess the capability for incorporation into collaborative robotics systems in alignment with the specifications of Industry 5.0. Subsequent study may focus on incorporating reinforcement learning, extending to three-dimensional space, and conducting real hardware testing.

The rapid expansion of wireless communication across networks and devices, driven by advancements in the Internet of Things (IoT), highlights the need for compact, lightweight, cost-effective, and efficient antennas to meet market demands and support wireless systems. Patch and substrate-integrated waveguide (SIW) antennas have emerged to address these needs. In this study, a substrate-integrated waveguide resonator is used to construct an ultra-wideband (UWB) fork monopole antenna. The SIW resonator enhances antenna performance and bandwidth while reducing weight and dimensions. A prototype antenna was designed, manufactured, and evaluated through experimental tests. The impact of integrating array holes into the antenna on bandwidth characteristics is also examined. Significant performance improvements are demonstrated by the proposed SIW-based UWB fork monopole antenna. Simulations and experimental results show a bandwidth improvement of up to 10.1 GHz, an S11 value reduction to -47.5 dB with one array of holes, and a gain increase to 4.9642 dB. These metrics confirm the antenna's effectiveness in overcoming traditional bandwidth limitations and improving overall performance.

The research presents a comparative analysis of the selection of objects of interest in color medical cytology images. For these purposes, the individual edge detection operators studied are Canny, Roberts, and LoG. The comparison contrasts the standard method of assessing color photographs with a technique that incorporates supplementary information. Data from individual color channels, derived from the breakdown of a color image in RGB format, serve as supplementary information. The following individual cases for potential examination are emphasized: Image processing with a complete level of reuse of edge detection operators; with a partial level of reuse of edge detection operators. The results are displayed through an array of tables, graphs, and images. Metrics of visual perception of images are utilized for comparative analysis. Compared with traditional edge detection methods, the proposed approach yields better results, which can exceed the estimates of the classical approach by 20%.