Classification Algorithms Framework (CAF) to Enable Intelligent Systems Using JetBrains MPS Domain-Specific Languages Environment

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

In today's business environment, where competition is increasing every day, effective leadership becomes a key aspect of success for any enterprise. The use of cutting-edge technologies in the development and implementation of strategic management decisions is crucial in this process, providing leadership with the necessary tools for analysis, planning, and execution of strategic tasks. The application of advanced software products, analytical tools, and intelligent systems helps increase management productivity, reduce risks, and optimize costs. Moreover, these technologies facilitate improved interaction between enterprise departments and the implementation of innovative strategies. The application of advanced management technologies can encompass a wide range of tools, from Customer Relationship Management (CRM) systems to business analytics and Artificial Intelligence (AI) systems. CRM systems enable the collection, analysis, and utilization of customer information to improve service and increase sales. Business analytics provides the ability to gain deep insights from large volumes of data, aiding in making informed strategic decisions. Artificial intelligence is used for process automation, forecasting, and decision optimization. The decision-making process may involve several stages, starting from gathering and analyzing information, formulating alternative courses of action, assessing risks and potential benefits, making decisions, implementing them, and finally, monitoring and evaluating the results. An important component of decision-making is also involving relevant stakeholders and considering their perspectives and interests. The main forms of implementing management decisions may vary depending on the specific situation and organizational goals. This could involve the implementation of new strategies, changes in organizational structure, launching new products or services, optimizing production processes, and so forth. Therefore, the use of advanced technologies and effective decision-making and implementation are important components of successful leadership in today's business environment, where the speed and accuracy of decision-making play a crucial role in achieving competitive advantages.

This study examines the shifting role of business analytics within the automotive sector, focusing on research from 2020 to 2025. Given the rapid rise of digitalization, artificial intelligence, and data-driven methods, the authors aim to chart prevailing research directions, highlight key thematic concerns, and point out overlooked areas. Drawing from nearly 300 Scopus-indexed articles and conference proceedings, the analysis leverages VOS viewers to visualize the research landscape through network, overlay, and density maps. The findings reveal five main thematic clusters, with recurring attention to decision making, machine learning, Industry 4.0, and the Internet of Things. Much of the literature centers on operational and strategic improvements within automotive organizations. That said, topics such as electric vehicles, smart cities, and customer analytics remain notably underrepresented. While the field is clearly moving toward sustainable and intelligent systems, there is a distinct lack of focus on consumer behavior and analytics specific to electric vehicles. In summary, business analytics appears deeply embedded in core automotive functions, yet there is a clear need for future research to address consumer engagement and regional challenges particularly in emerging markets. It is important to note that the study’s scope was limited to English-language publications within the business and management domain, potentially leaving out significant technical contributions.

Recent advances in artificial intelligence and robotics have led to the emergence of technological solutions capable not only of performing tasks, but also of making autonomous decisions, adapting to changing conditions, and interacting with other entities. In this regard, researchers are faced with the question of whether such systems can be recognized as subjects of law. This study is based on the anthropocentric approach, according to which the human being is recognized as the highest value and the primary reference point for legal regulation. By applying philosophical-anthropological, comparative legal, and functional-analytical methodologies, the author explores the limitations of traditional legal understanding in the digital era and argues against the recognition of intelligent agents as subjects of labor law. The focus is placed on preserving the ontological and legal primacy of the human in new labor formats, while proposals are formulated for the normative designation of intelligent systems as tools of labor. The article substantiates the need to modernize labor legislation to ensure the safety, sustainability, and ethical nature of human–technology interaction. The author draws special attention to the risks associated with replacing the human being in the legal regulation of labor and presents scientific concepts that allow for the possibility of creating “techno-subjects.” The article substantiates the need to legally define intelligent systems as tools of labor and proposes specific amendments to the Labor Code of the Republic of Kazakhstan aimed at regulating the safety of interaction with intelligent and robotic systems. Recent advances in artificial intelligence and robotics have led to the emergence of technological solutions capable not only of performing tasks, but also of making autonomous decisions, adapting to changing conditions, and interacting with other entities. In this regard, researchers are faced with the question of whether such systems can be recognized as subjects of law. This study is based on the anthropocentric approach, according to which the human being is recognized as the highest value and the primary reference point for legal regulation. By applying philosophical-anthropological, comparative legal, and functional-analytical methodologies, the author explores the limitations of traditional legal understanding in the digital era and argues against the recognition of intelligent agents as subjects of labor law. The focus is placed on preserving the ontological and legal primacy of the human in new labor formats, while proposals are formulated for the normative designation of intelligent systems as tools of labor. The article substantiates the need to modernize labor legislation to ensure the safety, sustainability, and ethical nature of human–technology interaction. The author draws special attention to the risks associated with replacing the human being in the legal regulation of labor and presents scientific concepts that allow for the possibility of creating “techno-subjects.” The article substantiates the need to legally define intelligent systems as tools of labor and proposes specific amendments to the Labor Code of the Republic of Kazakhstan aimed at regulating the safety of interaction with intelligent and robotic systems. It is concluded that the adaptation of labor law should be based not on the rejection of anthropocentrism, but on its conceptual renewal. The human being must remain the central figure of the legal system, while technology should serve as a means of realizing human labor potential.

Background: The evolution of Artificial Intelligence (AI) and deep learning has changed the context within which business analytics previously functioned, allowing businesses to take advantage of predictive and prescriptive versions of decision making in relation to traditional data driven methods. Although AI and deep learning methodologies hold promise, their implementation merits rigorous empirical investigation to establish the nature and extent of the adoption, applications, and implications for management purposes. Methods: A survey was conducted with 125 respondents that are managers, data analysts, and IT professionals to examine the adoption and influence of AI and deep learning in business analytics. Data were analyzed using descriptive statistics and percentage distributions to articulate distinct adoption trends and managerial perspectives. Results: The research shows that 73.6% of participants use AI predictive analytics and 64.8% use NLP for sentiment analysis and 60.8% use AI process automation. The study shows that 53.6% of respondents use AI to forecast strategically and 47.2% use it for fraud detection. The survey results show that better decision-making (78%) and better customer engagement (65%) and decreased operational inefficiencies (70%) are all important managerial implications. The research reveals that ethical use along with data privacy and workforce skill gaps remain major problems. The research proves that deep learning together with AI transforms business analytics by providing better predictive functions alongside improved operational efficiency. Conclusion: The successful integration of these systems depends on solving ethical and technical problems as well as organizational challenges. Management needs to develop strategic frameworks which focus on training and governance and scalability to optimize benefits.

Intelligent computing system based on pattern recognition and data mining algorithms

With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote "intelligent buildings" characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.

Background. The intensive deployment of digital technologies and artificial intelligence algorithms in the agro-industrial complex outpaces the development of a specialized regulatory and technical framework, which creates constraints on the practical integration of intelligent systems into production processes. Existing strategic policy documents define general directions for digital transformation but do not provide sector-specific detailing of requirements for the architecture, reliability, and verification of artificial intelligence systems. Technical standards and construction regulations are primarily oriented toward universal engineering solutions and do not account for the specificity of intelligent biotechnical systems, while data governance frameworks do not distinguish technological information of the agro-industrial complex as an independent category of regulation. These circumstances determine the need for a comprehensive analysis of the regulatory environment and the development of a systemic model for its transformation, aimed at creating conditions for the sustainable and technologically grounded deployment of artificial intelligence in the agro-industrial complex of the Russian Federation. Thepurposeof the article is to develop a systemic understanding of the current state of the regulatory and technical framework for the integration of artificial intelligence systems into the agro-industrial complex of the Russian Federation, as well as to identify key directions for its development that enable large-scale deployment of intelligent technologies in greenhouse facilities. Materials and methods.The materials of the article include federal-level strategic documents, regulatory legal acts, national digital development programs, technical standards, construction codes, and documents governing data processing. The study employs methods of systemic and structural analysis of regulatory documentation, comparative analysis of regulatory acts at different levels, and a qualitative-quantitative assessment of the regulatory readiness of key governance clusters based on a normalized maturity scale. Results.The analysis revealed structural heterogeneity in the regulatory environment for the integration of artificial intelligence into the agro-industrial complex, caused by inconsistencies among strategic, technical, sectoral, and legal documents. It was established that the strategic level of regulation demonstrates a high degree of development, while technical, construction, and sectoral regulations do not provide sufficient conditions for the practical deployment of intelligent systems in greenhouse enterprises. A multi-level regulatory transformation model was developed, focused on the advancement of data governance regimes, the creation of sector-specific AI trust standards, and the formulation of digital readiness requirements for controlled-environment agricultural facilities.

The science of intelligent health systems (IHS) is crucial to the fundamental transformation of modern healthcare by embedding artificial intelligence (AI), robust data analytics, and resilient system architecture. IHS can handle unlimited clinical, imaging, and biomarker data through scalable architecture and interoperable platforms. The main components are acquisition devices, linear extraction and transformation lines, a computational engine driven by AI, and interfaces for clinical decision making, all of which require tight integration of engineering principles. Between 2018 and 2024, hospitals implementing IHS reported a 60% faster turnaround time for diagnostics, a 25% to 30% decrease in operating costs, and a 40% increase in efficiency for clinical workflows. The engineering architecture includes containerized environments, edge-computing devices, micro services architecture, and standardized health communications protocols. In this review, we examined how engineering principles facilitate data integrity, fault tolerance, scalability, and system performance. We also studied the systems that support the AI models that provide predictive diagnostics, risk stratification, and real-time treatment plan recommendations. The examples from various healthcare systems, additional to rural deployments and academic hospitals, demonstrate that an IHS is adaptable and scalable if it is properly engineered and, consequently, used appropriately. For example, business analytics platforms can be incorporated into the IHS and maximize financial planning, optimization of resources, and forecasting return-on-investment. The long-standing challenges of interoperability constraints, cybersecurity, infrastructure costs, and clinician adoption are discussed from an engineering perspective.

Domain-specific languages (DSLs) allow developers to write code at a higher level of abstraction compared with general-purpose languages (GPLs). Developers often use DSLs to reduce the complexity of GPLs. Our previous study found that developers performed program comprehension tasks more accurately and efficiently with DSLs than with corresponding APIs in GPLs. This study replicates our previous study to validate and extend the results when developers use IDEs to perform program comprehension tasks. We performed a dependent replication of a family of experiments. We made two specific changes to the original study: (1) participants used IDEs to perform the program comprehension tasks, to address a threat to validity in the original experiment and (2) each participant performed program comprehension tasks on either DSLs or GPLs, not both as in the original experiment. The results of the replication are consistent with and expanded the results of the original study. Developers are significantly more effective and efficient in tool-based program comprehension when using a DSL than when using a corresponding API in a GPL. The results indicate that, where a DSL is available, developers will perform program comprehension better using the DSL than when using the corresponding API in a GPL.

In this chapter, we discuss, in depth, visual information processing and a new artificial intelligent (AI) system that is based upon cognitive mechanisms. The relationship between a general model of intelligent systems and cognitive mechanisms is described, and in particular we explore visual information processing with selective attention. We also discuss a methodology for studying the new AI system and propose some important basic research issues that have emerged in the intersecting fields of cognitive science and information science. To this end, a new scheme for associative memory and a new architecture for an AI system with attractors of chaos are addressed.

The fourth edition of this bestselling textbook explains the principles of artificial intelligence (AI) and its practical applications. Using clear and concise language, it provides a solid grounding across the full spectrum of AI techniques, so that its readers can implement systems in their own domain of interest. The coverage includes knowledge-based intelligence, computational intelligence (including machine learning), and practical systems that use a combination of techniques. All the key techniques of AI are explained—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), agents, objects, frames, symbolic learning, case-based reasoning, genetic algorithms and other optimization techniques, shallow and deep neural networks, hybrids, and the Lisp, Prolog, and Python programming languages. The book also describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. Fully updated and revised, Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence, Fourth Edition features: A new chapter on deep neural networks, reflecting the growth of machine learning as a key technique for AI A new section on the use of Python, which has become the de facto standard programming language for many aspects of AI The rule-based and uncertainty-based examples in the book are compatible with the Flex toolkit by Logic Programming Associates (LPA) and its Flint extension for handling uncertainty and fuzzy logic. Readers of the book can download this commercial software for use free of charge. This resource and many others are available at the author’s website: adrianhopgood.com. Whether you are building your own intelligent systems, or you simply want to know more about them, this practical AI textbook provides you with detailed and up-to-date guidance.

Since the beginning of the 21st century, research in the intelligent systems had gained tremendous popularity as various technologies such as the micro-electronics technology, the intelligent sensor technology, the advanced intelligent control technology and other information technology, especially with the recent breakthrough in the field of deep learning, neural network and other artificial intelligence, had seen incredible advancement. It is worthwhile to note that the development of the intelligent technology is closely related to the current low-cost and high-speed data computing and storage capabilities. For instance, the network communication technology that had been applied in the military, industrial, medical and other relevant fields, had proven outstanding performance which result in the increased trust, and hence, attention on the intelligent unmanned systems. It can be predicted that the intelligent unmanned systems will become the commanding point of science and technology in the near future and an indicator of a country’s scientific and technological international status.

In this paper, we consider the results of research on some approaches to decision-making in irrigated agriculture - Intelligent Systems. The traditional resources for increasing the efficiency of agriculture have practically dried up. Achieving a qualitatively new level of agricultural development is possible only with the use of modern digital and intelligent technologies, because of traditional resources for increasing efficiency do not lead to a result. Global climate change and the need for agriculture in risky farming areas necessitate decision support systems for effective governance and sustainable agricultural development. The rapid development of data science and artificial intelligence has led to a new stage in the development of decision support systems - intellectualization. We have proposed to put the Kohonen neural network (Self-organizing map) as the basis for an intelligent decision support system for agro-climatic resources. At the same time, the use of modern software makes it possible to implement complex mathematical models, while new tools for creating intelligent systems such as remote sensing, cloud technologies, big data processing systems and machine learning allow more accurate management decisions at all levels of agricultural production.KeywordsIntellectualizationDecision supportIrrigated agriculture

Computational semiotics is a quite new field of research, which emerged from advanced studies on artificial intelligence and intelligent systems. It is an alternative to the mainstream approaches - those based either on cognitivism or on biologically inspired techniques. Instead, computational semiotics seeks its inspiration on semiotics, a tradition on the philosophy of mind dealing with the concepts of representation and communication from a more technical perspective. The whole story besides this new field of research is usually unknown from many researchers working on the field of intelligent systems. This story started during the 1970's, where two main precursors first published works relating semiotics to intelligent systems development, almost at the same time in Russia and in the U.S. These pioneering studies were performed by Dmitri Pospelov in Russia and Eugene Pendergraft in U.S. Despite promising research results, for diverse reasons they stayed ignored from the mainstream in artificial intelligence for about 25 years. However, around 1995 they were rediscovered, motivating the development of this new paradigm of research. The goal of this paper is to briefly review the main contributions in this area.