Deep architectures with convolution structure have been found highly effective and commonly used in computer vision. With the introduction of Graphics Processing Unit (GPU) for general purpose issues, there has been an increasing attention towards exploiting GPU processing power for deep learning algorithms. Also, large amount of data online has made possible to train deep neural networks efficiently. The aim of this paper is to perform a systematic mapping study, in order to investigate existing research about implementations of computer vision approaches based on deep learning algorithms and Convolutional Neural Networks (CNN). We selected a total of 119 papers, which were classified according to field of interest, network type, learning paradigm, research and contribution type. Our study demonstrates that this field is a promising area for research. We choose human pose estimation in video frames as a possible computer vision task to explore in our research. After careful studying we propose three different research direction related to: improving existing CNN implementations, using Recurrent Neural Networks (RNNs) for human pose estimation and finally relying on unsupervised learning paradigm to train NNs.

The integration of the Internet of Things (IoT) in education has fundamentally transformed personalized online learning (POL), enabling adaptive, context-aware environments that tailor learning based on real-time environmental data or device usage. This approach fosters learner-centered environments that customize content and pace according to individual needs and preferences. All are powered by interconnected devices and data analysis. This review systematically explores various technological and pedagogical approaches to integrate IoT in POL systems, including devices, communication technologies, context-aware mechanisms, integration frameworks, computing technologies, and implemented pedagogical methods. By examining 61 peer-reviewed studies, this research categorizes IoT technological and pedagogical approaches, evaluates their impacts on personalization, and identifies challenges such as data privacy and resource constraints. The key findings explore how IoT-enhanced POL significantly improves learner engagement, adaptive learning, and academic outcomes, assessed via performance analytics, tests, and questionnaires. However, gaps in interoperability standards, learner profiling, cognitive load modeling, and the ethical use of data highlight the need for a unified framework. In addition, it emphasizes the transformative potential of IoT in creating personalized and inclusive learning environments. The study concludes by proposing an IoT-based POL framework and recommends directions for future research and practical applications.

This article proposes a new concept of microservice-based architecture for the future of distributed systems. This architecture is a bridge between Internet-of-Things (IoT) devices and applications that are used to monitor cattle health in real time for the physical and health parameters of cattle, where microservice architecture is introduced that enables this form of monitoring. Within this architecture, machine-learning algorithms were used to predict cattle health and inform farmers about the health of each cattle in real time. Within this architecture, six microservices were proposed that had the tasks of receiving, processing, and sending data upon request. In addition, within the six microservices, a microservice was developed for the prediction of cattle health using algorithms from machine learning using the LightGBM algorithm. Through this algorithm, it is possible to determine the percentage value of the health of each head of cattle in the moment, based on the parameters that are sent from the mobile node. If health problems are identified in the cattle, the architecture notifies the farmer in real time about the problems that the cattle have. Based on the proposed solution, farmers will have 24 h online access to monitor the following parameters for each head of cattle: body temperature, heart rate, humidity, and position.

In today’s world, websites and web applications have become a necessity for all kinds of businesses and commercial enterprises. Software development is an important step of designing the software, ensuring that the designed software fulfills its practical needs and that it is free of bugs and errors. Software testing plays an important role in the software development life cycle because it contributes to the overall quality of the product. This guarantees the consistency and standard of the applications. It is important to be mindful of time and costs when evaluating a program. Therefore most testers have moved from a manual testing to automated testing to reduce time and cost. Then selecting a software-testing tool for automated testing that best fit a project is important yet a challenging task. The aim of this paper is evaluation and comparison of automation testing tools used for web application testing. In this paper will be analyzed and compared different features of two testing tools: The Katalon Studio and Selenium.

There is a complex problem regarding security of data in cloud, it becomes more critical when the data in question is highly sensitive. One of the main approaches to overcome this problem is the encryption data at rest, which comes with its own difficulties such as efficient key management, access permissions and similar. In this paper, we propose a new approach to security that is controlled by the IT Security Specialist (ITSS) of the company/organization. The approach is based on multiple strategies of file encryption, partitioning and distribution among multiple storage providers, resulting in increased confidentiality since a supposed attacker will need to first obtain parts of a file from different storage providers, know how to combine them, before any decryption attempt. All details of the strategy used for a particular file are stored on a separate file, which can be considered as a master key for the file contents. Also, we will present each strategy with the results and comments related to the realized measurements.