Support channels represent a unique opportunity to improve customer satisfaction by offering a consistent experience in resolving customer issues. Several surveys show that customers have raised their standards of customer support services. While only a few years ago customers willingly waited a long time to speak with one of the service agents and were patient for their problem to be resolved, today’s customers have very limited patience and want a solution to the problem immediately. Customers don’t want to settle for a mediocre support channel experience. Support channels must provide superior service capacities so that customers see that the company values their choice and time. Efficient management of support centers implies accurate modeling of customer behavior on hold. The subject of our research is the application of data research techniques for predicting customer behavior in support channels. In this paper, we apply machine learning methods to predict customer behavior. Based on historical data in the service system, we use classification algorithms to predict customer patience in service channels.

The Internet of Things (IoT) is considered a new paradigm that aims to connect a large number of devices. IoT is increasingly present in domains such as healthcare, transport, agriculture, and other industrial branches. An increasing number of IoT devices, as well as the amount of data, leads to increased energy consumption and a negative impact on the environment. Therefore, researchers are focusing on the concept of Green IoT that aims to increase energy efficiency and create a safe environment. The focus of this paper is on energy-efficient techniques within green data centers. Also, the performance evaluation of data centers was performed in the GreenCloud simulator for the optimal load of data centers in terms of energy efficiency and sustainability.

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.

Interaction channels are unique opportunities to improve customer satisfaction by offering a consistent problem-solving experience. The role of employees in the contact center is to maintain an appropriate relationship between the company and the customer, thus they are personally responsible for the customer experience. In this paper, an objective evaluation method for evaluating customer-agent interaction, i.e. evaluating calls is proposed. The motivation for evaluating calls stems from the key performance characteristics of a contact center.

The Internet of Things (IoT) is a paradigm that aims to connect billions of devices to each other, anywhere and anytime. As IoT will be a ubiquitous technology, its sustainability and environmental impact are very important. Green IoT is considered the ecological future of IoT and plays an important role in which it contributes to improving the quality of life and providing a safe and healthy environment and ecosystem. A systems approach, based on the application of systems knowledge, appropriate methods and tools, provides the basis for observing green IoT through several different aspects by looking at the whole life cycle. This paper focuses on research into green technologies, green applications as well as green IoT infrastructure. It should be noted that this research does not cover the details of the IoT network and the perspective of connectivity, but focuses on the green aspect of the IoT network related to the creation of a hierarchical framework for green IoT. The proposed framework represents a unique view on the implementation of different approaches within green IoT systems, which are focused on the conservation of natural resources, in a way to minimize the impact of technology on human health and the environment.

The development of information society and broadband Internet is key indicators of social and economic change. They transform the way companies, political systems, and citizens communicate with each other. Today, we talk about various regional and national initiatives to first stabilize and then improve the economies of countries through the development of the Internet and information society. The European Union has recognized information technology as a major factor influencing economic growth and innovation. Among the seven flagship initiatives of the Europe 2020 economic strategy is the Digital Agenda for Europe. This shows the importance that information technologies have in the development of the modern economy. In this paper, we analyze the current state of development of the information society and broadband Internet access in Bosnia and Herzegovina. We highlight the necessity of considering mechanisms for the development of broadband access. We analyzed the current situation and progress in the implementation of the Digital Agenda guidelines in EU countries. The aim of this research is to highlight the advantages of using EU strategic guidelines to improve and develop the current situation in the field of broadband Internet in Bosnia and Herzegovina

Urban mobility is one of the most significant factors in the successful development and sustainable future of large cities. The increasing demand for fast, safe, and eco-friendly transportation services is a trend in modern society. These requirements pose the challenge of finding corresponding solutions for efficient mobility of people in urban areas. However, many problems are caused by the increased traffic in cities, leading to high congestion, negative impacts on the environment, rising security challenges, etc. Therefore, the research community and other stakeholders have increased their focus on finding solutions for these issues. The Internet of Things (IoT) has enabled the development of efficient and cost-effective solutions to enhance urban mobility. Enabling IoT technologies has become a significant driver for smart mobility concept development. The continuous development of IoT has led to various applications focused on urban mobility improvement. This paper presents some IoT possibilities and potentials for developing solutions for smart urban mobility.

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.

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.

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.

Nowadays, companies face numerous challenges to operate successfully and remain competitive in the market. Globalization is increasing competition in the market, allowing many companies to operate in foreign markets. Increasing competition has made companies constantly trying to increase their productivity while reducing costs. To meet all the requirements, and at the same time function in the best possible way, companies must be organized adequately, taking into account the whole set of processes from the company's internal organization to the company's appearance on the market. It means, if the end-user is to be satisfied, the company must implement quality in all phases of business, ie business ethics and company culture, through the quality of technology, personnel, etc. In other words, it must be satisfied the system quality of the company, known in the literature as QMS (Quality Management System). The main purpose of this paper is to review the multiple understanding of the concept of QMS and its different underpinning theories in a business to customer context. The design of this research is based on mere documentary analysis and some observations.

Internet of Things (IoT) becomes an emerging network technology that expedites billions of devices to be connected via the Internet to provide real-time intelligent application services. The benefits of Software-Defined Networking (SDN) can be used to fulfill IoT requirements. Quality of Service provisioning is an on-going demand in software-defined IoT (SD-IoT), particularly for large scale environments. In this paper, we address this issue by proposing a seamless model of AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in distributed fragmentation optimized SDN-IoT. Firstly, we propose a Multi-Criterion based Deep Packet Inspection method for classifying the network traffic, which is held in Edge Routers (access points). Secondly, we construct a Partially Connected Network Topology using the ISOMAP algorithm for an effective rule placement and routing. We propose a Traffic Differentiated QoS Routing for forwarding data packets via the most suitable switches. We select the optimum route by Deep Alternative Neural Network (DANN). Based on the relationships among switches, the path is selected and flow rules are deployed. The poor QoS is often caused by load imbalance in controllers and switches. To overwhelm this issue, we propose a Dynamic Offloading scheme in SD-IoT. We offload the data packets from the overloaded controller to the underloaded controller using Hassanat Distance-based K-nearest neighbors (HDK-NN) algorithm. Similarly, we propose a Ranking-based Entropy function (R-Ef) to allow dynamic offloading among switches. Simulation is performed using the NS3.26 simulator and the results proved that our proposed AI-aided SD-IoT model provides superior QoS performance compared to previous approaches.

The implementation of information and communication technologies (ICT) is becoming an inevitable factor in optimization of transport processes. In that way, positioning of the vehicle represents an essential element for transport companies in their intention to improve fleet management. There are various technologies applicable for this purpose, but the one discussed in this paper and given as possible solution is using a platform of multiservice telecommunications system (IMS). This solution is represented in a form of Server-based IMS application whose performance results in fleet management control by transport companies. To enable this kind of control it is necessary to perform vehicle position tracking i.e. to define the route of movement. For vehicle location adequate calculations are used based on the vehicle position in relation to the existing infrastructure of mobile networks (global system for mobile communications (GSM), universal mobile telecommunications systems (UMTS)). The application based on this system allows its users to access the desired information from any place, at any time, receiving alarm in the form of short message service (SMS), multimedia messaging service (MMS), e-mail, and to be informed about the possible diversion of the pre-set route. For vehicle location here appropriate algorithm has been implemented on the standard database with input data that refer to the vehicle position obtained on the basis of information of the base stations and any types of standard maps, Google and the similar.