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.

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.

Air traffic has seen a steady increase in the number of operations in recent years. This increase was not accompanied by an expansion of the system’s capacity to continue operations without significant impacts on the performance and quality of transport services. It is very difficult to expand the airport infrastructure in today’s conditions, so it is necessary to optimize the existing operations to increase the capacity and efficiency of the system. The paper focused on determining the correlation of individual key indicators of airport performance and their impact on delays, which was observed as an output variable. Three machine learning regression methods were applied, Decision Tree, Random Forrest and Support Vector Regression to predict delays with respect to other characteristics of traffic and surrounding airspace. All three methods were able to predict a delay with an error of ±60s in at least 91% of the test set, which shows a clear potential for the use of machine learning in this domain.

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

In the last few years, we have been active participants in globalization and the rapid growth of online commerce. This trend directly impacts logistics, postal, and courier companies. Challenges are visible in the growth and development of these companies, as well as quality of task execution and the ability to adapt to market conditions. With the impact of the SARS-COV-19 virus on the market, and due to locks during the pandemic, shoppers were forced to buy products online. This effect accelerated the trend of purchases via e-commerce, which increased the number of parcels in the technological phases of pick-up and delivery. These companies are the bearers of the first and last technological phase of shipment transport. Due to the increase in the number of shipments, postal and logistics companies are under great pressure. This is because of certain restrictions introduced during the pandemic, but also because of costs, especially fixed costs in the first and last phase of transport. This paper proposes an outsourcing model for the pick-up and delivery to convert fixed costs into variable ones.

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.

The airport environment is an extremely complex system with a large number of interdependent factors. A large number of airports have limited infrastructure capacity. One of the solutions to increase the capacity and efficiency of the airport is spatial infrastructure expansion. However, a large number of airports are close to populated areas, and further expansion is not possible. When implementing operational infrastructure solutions, it is necessary to establish a correlation between the Key Performance Indicators (KPI) and the existing limiting factors of the airport and the airspace in the vicinity of the airport. This paper will describe the basic performance indicators important for single-runway airports. Moreover, it will present the scalable and modular aviation software solution. It is used for simulation, allocation, and optimization systems for airport airside. The possibilities of using the output of the software tool and the quality of the datasets thus collected for the application of different prediction algorithms will be investigated. In this way, an attempt would be made to achieve a synergy of simulation tools and advanced algorithms. It will help decision-makers to determine the actual complexity and limitations during the planning and future investments.

Contact centers are an operationally complex element of a company and play a major role in the experience of its customers. By offering relevant and quick responses to questions and prompt problem solving, a company can achieve a better customer experience. Contact centers generate huge amounts of very useful data, which are often underused, misused, or even not used at all. Our research aims to apply data research techniques to the problem of creating customer profiles in the contact center. Customer profiling mechanisms should provide an explicit set of information about the observed customer's preferences, interests, and behavior patterns. Based on the attributes contained in the customer profile, the system makes decisions in terms of choosing the right contact center agent by anticipating the needs of the observed customer. In our paper, the customer profile is based on the extraction of his properties from log information about used services, behavior patterns, and other general characteristics of each customer. The purpose of our research is to determine which attributes are the most relevant for creating a customer profile and how to evaluate them.

The rapid development of technology is directly affecting the growth and development of e-commerce shipments, especially in the Business to Customer segment. An increase in e-commerce shipments has a strong impact on the express delivery industry. In these conditions, a very significant challenge is how to organize a postal network. The problem that arises is how many postal centers, and at what locations, should be implemented in a specific geographical area in order to optimize the level of service for the users. Solving this challenge has latterly received increased attention in both industry and academia. The aim of this paper is to firstly provide a concise overview of current approaches in the process of determining the optimal location of postal centers. The second part of the paper will focus on proposing an approach that will rely on machine learning methods for clustering in defined conditions and specific geographical environment using appropriate geographic information tools for spatial data analysis and visualization.

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.