Cloud Technology Research Articles

Business digitalization: definition and tools

As a result of the study, the concept of digitalization of a business organization is defined as a set of tools, the process of adding digital technologies to physical space and transferring information into a digital format to improve the efficiency and increase the profit of a business organization. Digitalization is a process and a tool, not the ultimate goal, and it should improve the interaction between people and technology. Digitalization has been classified into two types: process digitalization and product digitalization. Process digitalization is the classic understanding of this term in the context of business processes, and product digitalization is the additions of digital technologies to an existing product to improve it. Any type of digitalization should lead to an increase in the profit of a business organization directly or indirectly because this is one of the most important goals of any business. Digitalization tools have been defined as digital technologies and methods of their implementation in business processes or additions to products. Examples of such tools have been provided. It has been determined that one of the most important tools of digitization is digital data management. Digital data management includes digitization as one of the first stages and may include the use of CRM systems, cloud technologies and artificial intelligence. The use of these tools is possible not only in the context of data management, but as a method of creating a digital infrastructure or making decisions. The advantages of using various digitalization tools have been determined. Digital data management (including CRM systems, cloud technologies and artificial intelligence in this context) gives the ability to store, analyse and use large volumes of data, allows quick and easy access to information, reliability, and confidentiality, ensuring statistics, etc. Also, the use of digitalization tools allows improving interaction with consumers and creating a comfortable and efficient infrastructure both within the company and for consumers. Other digitalization tools have been listed: mobile technologies, ERP systems, automation, robots, IoT platforms, and GPS systems. It has been found that all these tools can be used to digitize the process, and only artificial intelligence GPS, and cloud technologies can be used to digitize the product.

Navigating The Cloud: Enhancing Human-Robot Collaboration In Industry 4.0 Through Digital Twins And Cloud-Based Systems

Industry 4.0 Is Geared Towards Establishing Sophisticated Industrial Ecosystems That Necessitate Secure And Efficient Collaboration Between Humans And Robots, Termed Human-Robot Collaboration (HRC). Within This Framework, HRC Leverages Cutting-Edge Collision Avoidance Technology Capable Of Detecting Objects And Foreseeing Possible Collisions, Thereby Calculating Alternative Pathways To Avert Any Direct Contact. Furthermore, The Concept Of Digital Twins Emerges As An Innovative Solution, Offering A Platform To Simultaneously Evaluate The Consequences Of Various Control Strategies Within A Digitally Simulated Environment. While Cloud Computing Infrastructure Can Furnish The Requisite Computational Prowess, It May Simultaneously Introduce Challenges Such As Increased Latency And Variability In Communication, Known As Jitter, Which Could Potentially Hinder System Performance. Cloud Technologies Are Renowned For Their Inventive Approaches To Ease The Workload For Software Developers And System Operators, Raising Questions About Their Integration With Digital Twin Methodologies And The Resilience Of Robotic Systems Against The Potential Delays Incurred From Cloud-Based Services. This Research Endeavors To Navigate These Complexities By Integrating A Blend Of Both Public And Private Cloud Services, Which Are Distinguished By Their Unique Parallel Processing Capabilities. This Study's Contributions Are Threefold. Firstly, It Introduces A Novel Method Designed To Gauge The Efficacy Of Diverse Strategies By Focusing On Their Associated Latency Impacts. Secondly, It Delineates A Tangible Application Of HRC, Illuminating Its Practical Benefits And Real-World Applicability. Lastly, It Defines A Crucial Performance Metric Intended To Assess The Efficiency Of These Systems Thoroughly. By Delving Into These Aspects, The Research Aims To Conduct A Comprehensive Analysis Of The Strengths And Weaknesses Inherent In Various Technological Methodologies And Their Consequential Effects On The Performance Of HRC Systems Within The Ambit Of Industry 4.0. Through This Detailed Examination, The Study Seeks To Provide Valuable Insights Into Optimizing Human-Robot Collaboration In Industrial Settings, Ensuring Both Security And Efficiency Are Upheld

Enhancing the internal auditors' effectiveness in jordanian companies: The impact of cloud-based accounting usage and the moderating role of digital proficiency

The main aim of this research was to explore how the digital proficiency of internal auditors impact the relationship between adopting cloud-based accounting and the digital efficiency of internal auditors in Jordanian-listed industrial firms. 98 questionnaires, gathered from internal audit managers in these companies, underwent analysis using Statistical Package for Social Sciences (SPSS) using descriptive-analytical methods. Results indicated that adopting cloud-based accounting significantly influences the effectiveness of internal auditors in these Jordanian-listed industrial companies. Moreover, the study found that the digital proficiency of internal auditors positively affects how much cloud-based accounting adoption impacts their effectiveness. These findings hold importance for regulators and decision-makers, offering insights to shape potential new regulations aimed at enhancing the use of cloud-based accounting in internal audit departments within Jordan's industrial sector. This could ultimately help internal auditors in overseeing public funds. Given the extensive impact of cloud technology on modern business, it's crucial to understand how these innovations can improve the precision and benefits of internal audit functions. Exploring the factors that drive the implementation of cloud technologies in internal audit is also essential. Notably, this study represents the initial attempt to examine existing research on the role of cloud-based accounting in enhancing internal audit effectiveness concerning the digital proficiency of auditors within Jordan's industrial sector.

Decoding the Cloud Giants: A Comparison of AWS, Azure and GCP

The adoption of cloud services by companies and organizations is increasingly becoming essential for enhancing competitive performance in today's business environment. Cloud services represent a relatively new technological advancement. However, selecting the optimal cloud service to meet specific requirements remains one of the most significant challenges. Major commercial entities, including Amazon AWS, Microsoft Azure, and Google Cloud, provide a variety of cloud services through dedicated, reliable, and cost-effective web applications. These cloud services attract a diverse range of users and organizations across various sectors, such as healthcare, business, and education. This article aims to introduce cloud customers to the most prominent cloud services. Additionally, we explore the concept of cloud computing, defined as the delivery of computing resources over the Internet. Leading cloud service providers such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) offer on-demand cloud solutions, including processing power, memory, and databases, alleviating the need for users to invest in, operate, and maintain physical computers, hardware, and servers. This study compares the performance and services of three major cloud platforms—Google Cloud Platform, Amazon AWS, and Microsoft Azure—as well as their architectures and types of cloud services. Cloud technology allows resources to be dynamically scaled without the need for significant changes in infrastructure, additional staffing, or new software development.

Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration

Amplifying the utilization of big data in healthcare analytics through cloud and Snowflake migration presents a significant opportunity to enhance data-driven insights and decision-making in the healthcare sector. This migration makes it easier to move large amounts of healthcare data to the cloud. Applications deployed in could are scalable for in-depth analysis in Health Care industry. The cloud is becoming more popular for storing data and running applications because it can easily grow with your needs, requires little to no management, improves security, and offers budget flexibility. The benefits of the cloud are obvious -- once you get there. Moving to the cloud requires planning, strategy, and the right tools for data migration. [1] By using Snowflake's advanced data warehousing tools, healthcare organizations can smoothly handle and analyze their complex and varied data. This helps them quickly uncover important insights and make better decisions. The shift to cloud technology and Snowflake has the potential to significantly enhance real-time analytics, personalized patient care, and evidence-based decision-making in healthcare. When healthcare organizations leverage big data in a cloud-based setting, they can discover valuable insights from their data, ultimately improving clinical outcomes, operational efficiency, and healthcare delivery. This study explores how the adoption of cloud and Snowflake in healthcare analytics can bring about transformative change and create new possibilities for leveraging data and generating insights in the healthcare sector.

Digital transformation with a lightweight on-premise PaaS

The rise of cloud computing has been enabled by advances in virtualization and containerization technology. Over the past decade, the use of cloud computing has grown rapidly and has had a significant impact on digital transformation with many enterprises migrating to public clouds. While convenient and cost efficient, such approaches are prone to certain data privacy, compliance and security risks. The ongoing democratization of cloud technologies represented by the increasing number of open source projects, has enabled certain enterprises to easily develop their on-premise cloud infrastructure. However, these open source projects are largely enterprise level and still too complex for small and micro enterprises and academic environments. To further decrease the on-premise infrastructure deployment and management barrier, we first provide an analysis of the existing on-premises PaaS workflows and solutions, along with the complexity of their deployment models, and identify the requirements for simple PaaS solutions for small environments with limited resources. We then introduce Kubitect as an enabler of on-premises PaaS democratization and expedite digital transformation. Kubitect is a lightweight single file declarative infrastructure configuration solution for on-premises cluster definition, instantiation and update. Our qualitative and quantitative evaluation shows the advantage of Kubitect for small environments where simplicity is more important than deploy time, assuming the latter is relatively comparable with alternative solutions.

Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization

This study focuses on examining the shift of an application system from a traditional monolithic architecture to a cloud-native microservice architecture (MSA), with a specific emphasis on the impact of this transition on resource efficiency and cost reduction. In order to evaluate whether artificial intelligence (AI) and application performance management (APM) tools can surpass traditional resource management methods in enhancing cost efficiency and operational performance, these advanced technologies are integrated. The research employs the refactor/rearchitect methodology to transition the system to a cloud-native framework, aiming to validate the enhanced capabilities of AI tools in optimizing cloud resources. The main objective of the study is to demonstrate how AI-driven strategies can facilitate more sustainable and economically efficient cloud computing environments, particularly in terms of managing and scaling resources. Moreover, the study aligns with model-based approaches that are prevalent in sustainable systems engineering by structuring cloud transformation through simulation-supported frameworks. It focuses on the synergy between endogenous AI integration within cloud management processes and the overarching goals of Industry 5.0, which emphasize sustainability and efficiency that not only benefit technological advancements but also enhance stakeholder engagement in a human-centric operational environment. This integration exemplifies how AI and cloud technology can contribute to more resilient and adaptive industrial and service systems, furthering the objectives of AI and sustainability initiatives.

Elements of Timed Pattern Matching

The rise of machine learning and cloud technologies has led to a remarkable influx of data within modern cyber-physical systems. However, extracting meaningful information from this data has become a significant challenge due to its volume and complexity. Timed pattern matching has emerged as a powerful specification-based runtime verification and temporal data analysis technique to address this challenge. In this paper, we provide a comprehensive tutorial on timed pattern matching that ranges from the underlying algebra and pattern specification languages to performance analyses and practical case studies. Analogous to textual pattern matching, timed pattern matching is the task of finding all time periods within temporal behaviors of cyber-physical systems that match a predefined pattern. Originally we introduced and solved several variants of the problem using the name of match sets, which has evolved into the concept of timed relations over the past decade. Here we first formalize and present the algebra of timed relations as a standalone mathematical tool to solve the pattern matching problem of timed pattern specifications. In particular, we show how to use the algebra of timed relations to solve the pattern matching problem for timed regular expressions and metric compass logic in a unified manner. We experimentally demonstrate that our timed pattern matching approach performs and scales well in practice. We further provide in-depth insights into the similarities and fundamental differences between monitoring and matching problems as well as regular expressions and temporal logic formulas. Finally, we illustrate the practical application of timed pattern matching through two case studies, which show how to extract structured information from temporal datasets obtained via simulations or real-world observations. These results and examples show that timed pattern matching is a rigorous and efficient technique in developing and analyzing cyber-physical systems.

“PRASHI” (PRANA RAKSHAK SHIRASTRANA)

This paper presents an advanced smart helmet system designed to detect accidents and provide alerts using an ESP32 microcontroller, alcohol sensor, IR sensor, buzzer, and accelerometer to monitor the x, y, z axes in the helmet. The bike section includes a DC motor, keypad, LCD display, ESP32, power supply, and GPS module. The system utilizes cloud technology to send and receive OTPs and GPS locations, ensuring timely assistance during accidents. This comprehensive approach enhances rider safety by integrating multiple sensors and real-time communication capabilities.

Training and education of specialists in the field of labour law

Objective/Aim: In the context of a constantly changing legislative environment and the necessity for professionals to develop their skills, the research focuses on identifying effective methods and tools that facilitate efficient learning and professional development in the field of labour law. This study aimed to propose a pedagogical technology for the preparation and training of specialists in the field of labour law and to assess the effectiveness of the training based on the specified technology. Method: The study involved 124 participants, with 63 in the experimental group and 61 in the control group. Statistical analysis was performed using Microsoft Excel. The student’s t-test indicated significant improvements in the experimental group’s training effectiveness, confirming the proposed pedagogical technology’s efficacy. Results: Consequently, implementing training and education technology for specialists in the labour law field was proposed to enhance the indicators. The criteria for the preparation of specialists in the field of labour law were delineated, including knowledge of labour legislation, consulting and support skills, analytical skills, communication skills, and continuous learning. According to the criteria above, levels of preparation for specialists in the field of labour law were established, namely high, medium, and essential. The proposed training and education technology for specialists in the field of labour encompasses the following tools: The utilisation of online platforms and educational resources, virtual classes and simulations, the incorporation of multimedia materials, the integration of adaptive learning technologies, the implementation of project- and problem-oriented teaching methodologies, the incorporation of interactive methodologies, the incorporation of cloud technologies and mobile applications, and the provision of assessment and feedback. Conclusion: The proposed pedagogical technology effectively enhances the training and education of labour law specialists. The experimental group’s significant improvement in learning outcomes confirms the technology’s efficacy. Implication: The findings of this research hold significant social implications. Improved training and education of labour law specialists leads to a more competent and effective legal workforce. This, in turn, ensures better protection of workers’ rights and fairer employer-employee relations, contributing to overall social stability.

DETECTION OF UNHEALTHY WEBSITES USING MACHINE LEARNING

In recent years, advancements in Internet and cloud technologies have led to a signicant increase in electronic trading in which consumers make online purchases and transactions. Accompanying this achievement are vices like unauthorized access to users' sensitive information and damages to enterprise resources. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. This study aims to develop an efcient machine-learning program to detect phishing websites with high accuracy. Most phishing webpages look identical to the actual web pages and various strategies for detecting phishing websites, such as blacklisting, and heuristics, among others have been suggested. Existing research works showed that the performance of the phishing detection system is limited and there is a demand for intelligent techniques to protect users from cyber-attacks. A Uniform resource locator (URL) detection technique based on a supervised machine learning approach – Naïve Bayes is employed and implemented in Python programming language. The efcacy of this approach was determined on a phishing dataset made up of 7900 malicious and 5800 legitimate sites, respectively. The results show that using the proposed methodology an accuracy of 96% can be achieved by using stacking, ltering along the Naïve Bayes and logistic regression. This study thoroughly investigates the use of machine laearning with features extracted from the URLs and was able to showcase common words for the identication of either phishing (unhealthy) or good websites and proffered a guide to end users against the recent approaches in malicious URLs detection.

Topological Evaluation of Cloud Computing Networks and Real-Time Scenario-Based Effective Usage

Cloud technology provides computing services over the internet, enabling entrepreneurs to access tools and services previously only available to large organizations, enhancing efficiency, business scaling, and competitiveness. With a step-by-step practical performance, the study builds real-time clouds using several lab scenarios. The research offers recommendations for cloud computing networks' performance, security, and awareness in this way. The study investigates and improves cloud computing networks in IoT and other network architectures using cheminformatics, a combination of chemistry, computer, and mathematics. It computes topological invariants, such as K-banhatti sombor (KBSO) invariants (KBSO), Dharwad Invariants, K-banhatti Redefined Zagreb (KBRZ), their different forms, and Quadratic-contra harmonic invariants (QCI), to explore and enhance their characteristics like scalability, efficiency, higher throughput, reduced latency, and best-fit topology. The main objective is to develop formulas to check the topology, and performance of certain cloud networks without experiments and produce mathematical modeling results with graphical results. It also gives the optimized ranges of the network with one optimized value. After these evaluations, the network graph also checks for irregularities if exist with the help of the Irregularity Sombor (ISO) index. The study also produced real-time scenario-based clouds and performance-based use. The results will help researchers construct and improve these networks with different physical characteristics.

Efficiency and Cost-Effectiveness in Agile DevOps with Cloud Computing

Abstract This article explores how Agile methodology, DevOps principles, and Cloud Computing fit into the software industry. The synergy study focuses on Axes Software, the oldest logistics software company in Romania. The effects of the association of these technologies during the development cycle of a software product are explored. Both their impact in the operational activities and the transformation of the development mode, as well as the financial impact, are evaluated. In the first part, theoretical aspects of Agile and DevOps ways of working and cloud technologies are explored. The amplification of the potential of the association of the three concepts is examined. With the basis formed, the paper continues with a detailed case study of Axes Software. It looks at the adoption of new technologies and way of working. Challenges and successes encountered are also considered. Key performance indicators such as release frequency, bug incidence (dropped from 72 to 4), and development velocity are examined to validate the effectiveness of this integration. The financial aspects, including initial investment and savings, are also analysed, leading to an assessment of the return on investment. Notably, deployments of new software versions now take two days instead of eleven, while the market witnessed a 275% increase in clients preferring the company’s cloud-hosted solutions over on-premises options. Finally, the effects of the association for software developers are distinguished, emphasizing continuous learning, leadership vision and supporting digital transformation.

Choice of architecture of video surveillance systems of State Border Guard of Ukraine based on cloud and fog technologies

Modern video surveillance systems have evolved from closed-circuit television systems to the level of complex systems that operate as part of complex integrated systems and solve the tasks of not only recording events in the sectors of view of video surveillance cameras but also analysing the received video information. The increase in the amount of information circulating in modern systems requires the creation of new concepts. In recent years, the attention of scientists has been drawn to distributed information processing technologies. The concept of cloud computing is developing rapidly, and the basic ideas that were laid down in its construction can no longer solve the problems faced by the technology. This has led to the emergence of so-called post-cloud architectures that extend and complement the capabilities of cloud computing. These architectures include Mist, Edge, Fog, etc. The purpose of the article is to study video surveillance systems based on the concept of cloud and fog computing and criteria for assessing their effectiveness. The research was conducted using the methods of analysis and generalisation, modelling, and experimentation. This approach made it possible to conduct a comparative study of video surveillance systems built on two architectures. The results obtained indicate that fog computing technology has the advantage of reducing latency, minimising the need for repeated requests to the cloud by performing calculations at the cloud edge. In a model based on the cloud computing concept, services that use cloud resources lead to an increase in network load. At the same time, fog computing technology allows you to relieve the network load by performing part of the computation by fog nodes. The results of the experimental study show the advantages of fog computing for networks that are sensitive to delays. However, if we consider a video surveillance system with the main task of recording events in the sectors of view of CCTV cameras and the ability to view video information in real time, it is obvious that fog nodes will not provide long-term storage of video information, and delays will not be critical. In the case of a video surveillance system with video analytics functions, fog nodes will be able to perform part of the video analytics algorithms, thus unloading the cloud. Therefore, an urgent task is to study the effectiveness of building video surveillance systems with video analytics functions based on fog architecture.

Using Cloud Healthcare to Improve Home Learning Efficacy in Congenital Heart Disease Infants

During the COVID-19 pandemic, visitation restrictions in line with infection control policies curtailed opportunities for family members to learn essential caregiving skills in the intensive care unit. This limitation decreased satisfaction among family members, possibly indicating their increased difficulties in care due to the lack of face-to-face guidance. Thus, increasing family member understanding of and ability to apply learning content without direct interaction presents a significant and urgent challenge. Moreover, because of lack of caregiving confidence, some family members may be reluctant to facilitate the discharge of critically ill patients, causing delays in discharge planning. These challenges underscore the obstacles faced by nursing health education during the pandemic. This study was designed to utilize cloud technology to enhance the knowledge and skills of families caring for infants with congenital heart disease at home and to assess their satisfaction with the associated homecare learning platform. Based on our hospital's cloud-based health education platform, a series of personalized instructional video materials was developed for families of infants with congenital heart disease. These materials cover comprehensively the entire treatment process, from diagnosis to post-discharge home care skills, for these patients. To facilitate autonomous learning, the videos in this series were made accessible to the families anytime, anywhere via personal devices such as smartphones and tablets. Concurrently, a chatbot tool was integrated to provide guidance on inpatient care for infants with congenital heart disease, including fundamental aspects of newborn care, with the aim of equipping parents and caregivers with the knowledge and skills necessary to provide basic post-discharge care. To ensure the families acquired personalized care skills, after completing the learning modules, practical bedside training sessions incorporating knowledge and skills assessments were organized for family members. After project implementation, the average knowledge score for family members increased significantly from 79.1 to 100 (perfect score). The proportion of family members proficient in executing caregiving techniques autonomously also rose impressively from 30% to 95%. Furthermore, average overall satisfaction with cloud-based technology-assisted caregiving learning among the family members rose 31.4% from 3.5 to 4.6. This project represents a viable solution to providing clinical nursing guidance independent of the constraints of time and location, and effectively enhances homecare-skill-related learning outcomes in family members, especially with regard to caring for infants with congenital heart disease.

"CANVAS" IN THE DEVELOPMENT OF COMPETENCIES OF WORKING WITH MULTIMEDIA TOOLS OF FUTURE INFORMATICS TEACHERS USING CLOUD TECHNOLOGIES

The modern process of informatization of education serves as a basis for the introduction of information and communication technologies into the educational process, where the trends of introducing cloud technologies to organize access to software implemented in various food activities are very relevant. The article highlights the problem of using one of the ICT tools, the open-source learning management system hosted by Instructure on the Canvas cloud, the advantages of using it in teaching and the tools it contains.

Research on the Application of Resource Virtualization Management Soft for Spaceborne Edge Computing Node

Due to the characteristics of multiple nodes, large load differences, and unbalanced network computing resource distribution, this paper explores efficient cloud technology for heterogeneous node resources, such as CPUs and GPUs, in spaceborne distributed edge nodes. A virtual resource pool with low overhead and real-time reconstruction is constructed to enable the unified encapsulation and management of on-orbit edge computing resources. This approach provides fine-grained supply of computing, storage, network, and other multi-dimensional resources, as well as an efficient cloud-edge collaboration mechanism. At the same time, based on the spaceborne edge AI computing platform, we carried out the application research and integration demonstration of the resource virtualization management soft of the spaceborne edge computing node. Through the design and research of the spaceborne edge computing node resource virtualization management system, the application of deployment of the system software on high-performance edge computing nodes is completed, and the ground simulation system is established to complete the design and implementation of integrated verification. In order to solve the problem of limited resource construction, this article provides a feasible technical approach for resource virtualization management of spaceborne edge nodes and cloud-edge collaboration.

On the implementation of digital technologies in accounting and financial analysis

Integration of digital technologies into accounting and financial analysis processes has become a necessity in today's economy. With the rapid growth of data volumes and the complexity of financial transactions, traditional accounting methods are becoming increasingly problematic. The integration of digital technologies offers ways to enhance efficiency, accuracy, and analytical capabilities in accounting and financial analysis. The purpose of the paper is to analyze the impact of digital tools such as artificial intelligence, machine learning, blockchain, and data analytics on the processes of accounting and financial analysis. It identifies the benefits, challenges, and best practices associated with their implementation. Based on an analysis of scholarly sources and specific research, this paper reveals significant advantages of implementing digital technologies in accounting and financial analysis. These include automation of routine tasks, real-time data processing, predictive analytics for scenario planning, improvement in fraud detection, and expanded decision-making capabilities. The results obtained indicate the transformative potential of digital technologies in the field of accounting and financial analysis, capable of effecting a range of revolutionary changes. At the same time, they highlight challenges such as data security issues, skill shortages among professionals, and the need to adapt regulatory frameworks to technological progress. Cloud technologies have completely changed the way accounting data is stored and accessed, while automation tools have streamlined routine tasks and freed up time for strategic activities. Simultaneously, artificial intelligence and machine learning technologies have enabled accountants to extract crucial information from financial data, enhancing decision-making processes and fostering business growth. Prospects for further research lie in exploring the scalability and interaction possibilities of digital solutions across various organizational structures, assessing the long-term impact on job roles and qualification requirements, studying the ethical implications of decision-making based on artificial intelligence, and developing frameworks for effective risk management in the digital age.

Will AI Become a Threat to Higher Education Sustainability? A Study of Students’ Views

Universities started to use artificial intelligence (AI) tools to improve the quality of higher education services. However, the rapid adoption of AI tools in higher education (HE) may lead to sustainability issues. On the one hand, there are prerequisites for using AI tools to achieve Sustainable Development Goal 4 (SDG 4). On the other hand, as consumers of educational services (stakeholders), students have their own opinions about using AI in the educational process. The purpose of this study was to explore students’ opinions on the use of artificial intelligence tools in higher education. The authors analyzed student responses to the question: “Do you think AI threatens higher education in the next five years?” The authors formulated this question based on the definition of “a safe learning environment”, which is associated with a “safe” learning environment (SDG 4.3). The authors made use of a literature review, a bibliometric analysis of 5000 sources, a survey of 1104 students from eight universities in Eastern Europe through cloud technologies to host a special electronic questionnaire, statistical processing of questionnaires, and testing of statistical hypotheses. The authors formulated and tested two pairs of competing statistical hypotheses. Finally, the authors obtained three new scientific facts based on the respondents’ answers. New scientific facts were obtained using a standard level of statistical hypothesis testing (α = 0.05). The main scientific fact is that 10.17% to 35.42% of students think that Artificial Intelligence threatens higher education. According to student opinions, AI may hurt the sustainability of higher education (SDG 4.3). The authors are confident that new scientific facts help conceptualize and promote didactic theory and practice. The study results are needed to predict, plan, and implement organizational, pedagogical, and methodological measures aimed at SDG 4.3 through a “safe” learning environment while further expanding the use of AI in higher education.

Application of Machine Learning to Detect Building Points in Photogrammetry-based Point Clouds

Different point cloud technologies such as Terrestrial Laser Scanners (TLS), Airborne Laser Scanners (ALS), Mobile Mapping Systems (MMS), and Unmanned Aerial Vehicles (UAV) have become increasingly more common in land surveying and geoinformatics over recent years. Thanks to these modern tools, experts can survey large areas cost-effectively with either high resolution or high accuracy. However, processing the point cloud, which consists of millions of points, can be a massive challenge. Manual processing of these large datasets can often be very time-consuming and hardware-demanding, and most of the time, only a limited part of the point cloud is used to derive the final products. The solution can be to automate the process as much as possible. Several advanced mathematical methods, especially Machine Learning (ML) algorithms, allow efficient automated processing of point clouds. This paper presents a processing chain to detect and separate building points from large-scale photogrammetry-based point clouds. The processing is based on the combination of Random Sample Consensus (RANSAC) and Machine Learning (ML) algorithms like Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Multi-Layer Perceptron (MLP). Presented methods were trained and tested on established and open available Heissigheim 3D (H3D) dataset to separate roof and vegetation points with over 90% accuracy in order to enhance the separation of building points on large-scale point clouds.