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.

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.