This paper presents an end-to-end architecture for smart waste management, leveraging real-time data, IoT, AI, and machine learning to optimize operational efficiency and decision-making processes. The architecture is designed for both near real-time and batch data processing, ensuring continuous optimization and adaptation of waste collection routes and resource allocation. Machine learning models are employed to predict possible bad adverse scenarios and optimize operational plans. Additionally, business intelligence is utilized for data analysis and reporting, providing actionable insights based on real-time and historical data. The presented system is implemented on a scalable Kubernetes infrastructure, supporting the increasing data volumes and processing demands while maintaining system responsiveness and efficiency. This integrated approach demonstrates significant improvements in resource utilization, operational efficiency, and service delivery, highlighting the potential for smarter and more sustainable waste management practices. This research addresses the gap in combining IT architectures with AI models and IoT, paving the way for future advancements in smart waste management systems.

Recently, the necessity of video testing at the point of reception has become a challenge for video distributors. This paper presents a new system framework for managing the quality of video degradation detection. The system is based on objective video quality assessment metrics and unsupervised machine learning techniques that use the dimensionality reduction of time series. It was demonstrated that it is possible to detect anomalies in the video during video streaming in soft real time. In addition, the model discovers degradations based on the visible correlation between adjacent images in the video sequence regardless the quick or slow change of a scene in the sequence. With additional hardware manipulations on the equipment on the user side, the proposed solution can be used in practical implementations where the need for monitoring possible degradations during video streaming exists.

This paper presents a model that enables the application of smart waste collection management using artificial intelligence to detect QR-codes on the video stream of surveillance cameras attached to waste collection trucks. A framework model proposal together with a detailed explanation of the key components of the system is shown. It also demonstrates the use of QR-code detection for identification of waste bins and its specific application in smart waste management system.

This article presents a simple software-developed model for calculating the relative frequency of individual symbols and the entropy of the Latin alphabet of a standardised language used by four South-Slavic origin ethnic groups in the Western Balkans in four countries. In addition, a method of applying the Shannon-Fano and Huffman source coding algorithms is presented, which takes into consideration the specificity of the observed alphabet in relation to the English one. The presented model is developed in the MATLAB programming language. The model is tested using an arbitrarily selected text.

This paper presents some results of analysis of using several types of common Ethernet cables (LAN cables) in last section of access networks. The main goal of the article is to answer the question whether (if so, under what conditions and to what extent) we should consider the type of specific Ethernet cable when using it in a FTTB environment. Four branded and two unbranded CAT5e Ethernet cables are used for measurements. Additionally, a DSL cable with diameter of 0.4 mm is used for comparison purposes. The results are collected and mutually compared under similar loop conditions (good loop). All of the results of measurements are collected in operating conditions.

The aim of the paper is the quality of video streaming analysis in cases of using different video codecs in the environment of distributed computer systems with different QoS (Quality of Service). For the purposes of the analysis, several scenarios were set up in which video encoded with different codecs is transmitted by a virtual video streaming server to virtual clients. For each of the scenarios, an environment with different QoS (packet losses, latency, jitter) was simulated and the quality of the received video stream was evaluated for each video codec. The quality of the received decoded video stream was calculated using SSIM (Structural Similarity) and VMAF (Video Multimethod Assessment Fusion) video objective metrics and compared to the original video stream.

The increased volume of initiatives and investments in the framework of smart cities, as well as strong investments in AI/ML technology together with the IoT industry, undoubtedly represent the future of practical activities and the implementation of new generation technological solutions in cities. By analyzing the previous literature and related works, in the field of smart waste management solutions, the focus of research was placed on isolated problems at lower levels of technology implementation on individual subsystems with individual environments. The purpose of this paper is to emphasize the need for a systematic integrated approach in the process of designing a model of practical application of technology to solve the problem of waste management in cities with a special aspect on business processes and the end user. The paper presents an analysis of related works and commercial solutions implemented so far and proposes a practical implementation model that, among other things, includes a component of a higher level of abstraction that should perform an additional iteration of the AI/ML process and make a final decision in the prediction process and controls in smart waste management solutions.

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.