With the development and implementation of the 5G systems worldwide, the shortages and imperfections that limit the functions and services of 5G networks were made apparent. This generation should enable its users to access unseen services and applications and a better quality of service in comparison to its predecessors. However, aside from the apparent importance of 6G networks, the possibilities, use cases, applications, and services that this technology would provide are yet to be explored. Our intention with this paper is to research the papers and compare the differences in requirements, capabilities, services, and use cases between 5G and 6G.

Interaction channels are special opportunities to improve customer satisfaction by offering a consistent problem-solving experience. Contact center employees are the link between the company and the customer. They are responsible for maintaining an appropriate relationship between the company and the customer. So, they are personally responsible for the customer experience. In this paper, we present an objective evaluation method for evaluating customer-agent interaction, i.e., evaluating the effectiveness of the realization of customer requests from calls. The evaluation method is automatic and does not depend on the relationship between the call center manager and the employees. The motivation for evaluating calls stems from the key performance characteristics of a contact center, of which we particularly emphasize service time, first call resolution, handling time, and others.

The Internet of Things (IoT) connects everyone in the smart world, so the energy consumption of IoT technology is a challenging and attractive research area. The development of technology in the field of IoT has changed the way of life and enriched society with its benefits, but we must not ignore the fact that IoT consumes energy, contributes to toxic pollution, and generates electrical waste. To increase the benefits and reduce the harmfulness of IoT, there are increasing tendencies to move towards green IoT (G-IoT). The G-IoT is considered the future environmentally friendly IoT. Greening ICT technology plays a key role in G-IoT and promises many benefits to society such as efficient production, and reducing the energy used to design and distribute ICT devices and equipment. This paper will present a comprehensive overview of G-IoT technologies and strategies that demonstrate work and efforts to build a green and smart world, contributing to a safe and healthy environment, smart and high quality of life based on enabling technologies, reducing pollution, and reducing energy consumption. ICT technologies that enable G-IoT include Green RFID, Green Wireless Sensor Network (GWSN), Green Cloud Computing (GCC), Green M2M (G-M2M), and Green Data Center (GDC). The paper will also present an analysis of the importance of environmental technology processes in sustainable development, exploring the principles and roles of G-IoT in the progress of society through examining its potential for improving quality of life, environment, economic growth, and green global modernization.

Software process improvement implies a set of complex and systematic activities of software engineering. It requires theory and models established in management, technical and social sciences. The improvement is based on the assumption that the organization if it owns mature and capable processes, would be able to deliver quality software on time and in line with predicted costs. The maturity models are initially aimed for implementation in enterprise software organizations, government organizations and within the military industry. Their complexity and the size make them difficult to use in small software organizations and companies. In such organizations the interest for use and the efforts to make an efficient and effective organization is always presented, though. In this paper, the basic and derived capability maturity models are described and cases from their implementation are analyzed, along with assessment of results of such projects in business practices. The problem of the software process improvement in small organizations is described, extracting the risks and recommendations for its enhancement. These recommendations are provided in order to set up a foundation for implementation of these models in a specific managerial and organizational environment characterized by small organizations.

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Methods: This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim: This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion: ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.

Routing in multidomain and multilayer networks is the subject of constant theoretical research, with special emphasis on routing optimization algorithms based on several criteria. Such research results in new proposals. The basic task of the algorithm is to perform the given task in a finite and reasonable period of time and with reasonable resource requirements. When new solutions are compared with previous solutions, it is necessary to consider as much information as possible about the characteristics and differences between these algorithms, which ultimately determines the degree of success of the algorithm. Routing algorithms depend on the goals to be achieved and most often solve a certain group of problems with certain simplifications of the overall problem and to the detriment of performance that are not crucial for a given routing optimization problem. Therefore, it is necessary to have acceptable methods for efficiency-complexity evaluation methods of routing algorithms with certain, universally applicable, metrics. Several theoretical approaches, including graph theory, optimization theory, complexity theory, allow approaches to compare the algorithms and the results achieved with the help of these algorithms.

This paper focuses on the problem of 5G network cell planning. In addition, it presents an example of a rough estimation of the required number of cells or base stations in a certain area for arbitrary number of users who are provided with a certain bandwidth per user within these cells. The cell number estimation is the initial step and the essence of planning and implementation of 5G network in an area. It is helpful for the operators to create and take into the account business plans in order to fully implement the network as a function of number of users which have to be served. Considering that, knowing the rough number of 5G base stations per user is very important for pre-sale activities and eventually necessity for widening of the initial investments. Therefore, the paper presents four scenarios that include different network parameters. Depending on the network parameters, the required number of base stations in a certain area changes. Given scenarios are examples of one 5G network in virtual area per bandwidth per user.

A large number of researchers found their interest in addressing the issue of capacity scaling for wireless ad hoc networks. This paper aims to provide a comprehensive overview of the development of capacity scaling laws in wireless networks, highlighting the problem of scaling as one of the basic challenges in their research. The review began with the definition of the notion of bandwidth of random networks, which were taken as a reference model of consideration when determining more advanced strategies for improving throughput capacity. Based on these strategies, other factors that have an impact on capacity scaling laws have been identified and elaborated. Finally, the capacity of hybrid wireless networks, ie networks in which at least two types of nodes functionally exist (ad hoc nodes/infrastructure nodes / auxiliary nodes), was partially investigated.