Concept Of Intelligence Research Articles

The Causal Factors Affecting the Management of Predictive Student Relationship using Business Intelligence Concept for the Retention of Undergraduate Students

This study aims to analyze causal factors affecting predictive student relationship management for undergraduate student retention using business intelligence. Phase 1 involved identifying key factors influencing retention through document analysis, categorizing them into social, learning, teaching, and student-related factors. Social factors include student community, friendships, communication channels, and organizational culture, which promote engagement, motivation, and perseverance. Learning and teaching factors, such as supportive learning environments, scholarships, instructional design, and structured assignments, impact academic success and retention. Student-related factors, including learning abilities, academic preparedness, goals, and parental support, are essential for persistence. Data was gathered from 1,574 students at Valaya Alongkorn Rajabhat University, with 1,160 usable entries after cleansing. Exploratory Factor Analysis (EFA) grouped these variables into five components: Student Communication Channels, Academic Proficiency, Parental Guidance, Scholarships, and Organizational Culture. Confirmatory Factor Analysis (CFA) validated the model, highlighting well-clustered factors. In Phase 2, a predictive model was developed using stepwise multiple regression, identifying impactful variables, such as note-taking abilities, scholarship counseling, peer communication, and access to advisors. The final model, with an R value of 0.881 and an adjusted R² of 0.777, demonstrated 77.7% predictive accuracy, emphasizing the combined influence of academic support, communication, financial aid, and social integration on student retention. The findings suggest that institutions should prioritize these areas to foster a conducive environment for student success.

Exploring the Impact of Combining Neuromarketing and AI Adoption on the Perceived Effectiveness of Social Media Advertising Campaigns

Literature shows that integrating advanced tech-oriented marketing skills and techniques (i.e., Neuromarketing and Artificial Intelligence concepts) and assessing that influence toward incentivizing SMEs owners to outsource their marketing activities are still omitted and need further clarifications. As a response, through a deep exploration of the literature and insights from the related Key Performance Indicators (KPIs), this research articulates a proposed research framework, upon the premise of extending Taylor and Todd's Decomposed Theory of Planned Behavior. This research seeks to contribute by presenting a framework that further allows M&A agencies to comprehend the underpinnings of integrating such a tech-oriented combination toward enhancing their performance while crafting advertising campaigns for SMEs. This is along with showing how such integration would improve the image of those M&A agencies showing their superb capabilities in crafting advertising campaigns. The authors believe the framework is expected to serve as a guideline for future researchers to incorporate their primary data towards framing their research.

THEORETICAL CONCEPT OF ARTIFICIAL INTELLIGENCE, ITS IMPACT ON THE MODERNISATION OF BUSINESS PROCESSES AND STRATEGIC DEVELOPMENT OF ENTERPRISES

THE PURPOSE OF THE ARTICLE is to study the theoretical concept of artificial intelligence and its impact on the modernisation of business processes and strategic development of enterprises. RESEARCH METHODS. The article uses the following methods: expert assessments; algorithmic analysis; experimental research; statistical analysis; monitoring and evaluation of results; analysis and synthesis; graphical method, etc. PRESENTING MAIN MATERIAL. Artificial intelligence (AI) is a multidisciplinary scientific concept that has a huge potential for transformation in the field of business process modernisation and strategic development of enterprises. AI is capable of radically modernising business processes and their strategic development by automating decision-making systems and predicting innovations in production based on data analytics. As AI touches upon issues such as ethics, privacy, and cybersecurity, the misuse of AI can have serious negative consequences for users. The main types of AI fall into two broad areas: Weak AI – systems that are capable of performing specific tasks, but without understanding the broader context or the ability to adapt in general, which can outperform humans in certain tasks but do not have true ‘consciousness’; Strong AI – systems that can think, understand and learn at the level of human intelligence (Strong AI is still a hypothetical area of research, as there is no fully implemented Strong AI in modern science). Currently, there are several main approaches to AI development, each of which has its own peculiarities. The main methods are machine learning, expert systems, neural networks, and evolutionary algorithms. AI is becoming a relevant tool for modernising business processes, optimising resources, and developing enterprises strategically. Thus, AI opens up new horizons for business, allowing companies to optimise their business processes, minimise costs and increase efficiency. Integrating AI into the development strategy of enterprises requires a deeper understanding of its potential and limitations, where the introduction of AI changes not only individual business processes but also affects the overall approach to the management and development of enterprises. One of the key areas of AI use in the strategic development of enterprises is forecasting and strategic planning, where AI can help enterprises predict economic trends, analyse the competitive environment, and develop strategies that meet future customer demands. CONCLUSIONS. It has been established that AI has a mega-potential for business transformation, contributing to the modernisation of business processes and strategic development of enterprises. However, to achieve effective results, it is important to be aware of both the advantages and disadvantages associated with its integration. Successful AI integration requires not only technical training, but also the adaptation of business models, a strategic approach, attention to all ethical aspects, investment in innovation, and continuous training of employees. The introduction of AI will allow businesses to optimise their operations, make more informed decisions and adapt to the changing environment. Understanding the importance of AI for businesses allows them not only to adapt to new conditions but also to gain a competitive advantage, as AI is becoming one of the key factors in business development, and its role will only grow in the future. KEYWORDS: artificial intelligence; modernisation of business processes; strategic development of enterprises; business transformation; integration; innovation; enterprises; adaptation; implementation.

Impact of Emotional Intelligence in Banking Sector

The purpose of this research is to conduct a structured analysis of the influence of emotional intelligence in Banking Sector. The sources supporting the study of emotional intelligence will be reviewed in this paper. The current study seeks to understand the concept of emotional intelligence. Recently, emotional intelligence has become a debated and crucial subject due to its importance and links to various other areas of life, leading to in-depth research. Emotional intelligence acts as the guide for emotions, thoughts, knowledge acquisition, analytical thinking, and innovative thinking. The Banking sector relies heavily on emotion. Employees in the banking industry build and sustain relationships in emotionally charged work situations where providing services is heavily reliant on emotion. Emotions have an intrapersonal impact on bank workers as well as an impact on professional relationships and service performance.

Analysis of Criminal Legal Risks and Response Concepts of Artificial Intelligence in the Digital Age

人工智能的出现使得社会的发展达到了前所未有的高度。其为社会带来巨大利益的，同时也蕴含着诸多刑事法律风险，主要包括侵犯数据安全的风险、算法错误引起的风险、引起知识产权纠纷的风险。新生积极事物的发展必须考虑创新与规制之间的平衡点，一味限制必然导致新生事物的积极作用被抑制。通过考察积极刑法观与谦抑性两种观念的差异，辨析适合人工智能创新发展的模式。积极刑法观强调依靠刑法实现人工智能刑事法律风险的防范，但是万能刑法观将会导致技术的停滞不前，且无法应对多元的刑事法律风险。为此，应当坚持谦抑主义的治理理念，提升对人工智能带来风险的容忍度；保持刑法的谦抑性，为前置留足空间，构建平衡的法律体系，系统治理人工智能的刑事法律风险；坚持比例原则，在适用刑法时充分考虑比例原则，降低刑法对人工智能的影响，保障人工智能的发展，坚持安全与发展并重原则。

Research on the Application of Artificial Intelligence in Museum Exhibition Design

With the rapid development of information technology, artificial intelligence has developed rapidly, and is gradually widely used in all walks of life, which has a profound impact on social development. In the field of museum exhibition design, artificial intelligence also plays a role that cannot be ignored. Relevant designers should pay attention to the role of high-tech in the information age in promoting the development of the museum industry, innovatively introduce artificial intelligence technology, and promote the development of the museum to follow the pace of the time and better cater to the needs of the audience. In this context, this paper will first briefly expound the relevant concepts of artificial intelligence and museum exhibition design, then briefly discuss the application principles of artificial intelligence in museum application in museum exhibition design, and finally explore the relevant application paths, aiming to give full play to the application value of artificial intelligence in museum exhibition design, innovative design direction, and create diversified design forms. To provide visitors with a more novel tour experience, improve the recognition of the museum at the social level, and strengthen its role in cultural construction through scientific and technological means.

Digital Technologies in the National Cadastre System of Uzbekistan: Issues of Legal Regulation

Objective: to critically analyze the state of national legislation of Uzbekistan in terms of legal regulation of digitalization and the use of artificial intelligence in the cadastral sphere.Methods: the research is based on such methods of scientific cognition as formal-legal and comparative-legal analysis, induction and deduction.Results: the provisions that regulate digitalization and the use of artificial intelligence in the cadastral sphere were analyzed, legal gaps were identified. It was determined that the practical application of artificial intelligence technologies outpaces its legal regulation. The shortcomings of legal regulation in this sphere were noted (lacking legal definition of the legal status of artificial intelligence in the national legislation; regulation of business entities’ participation in the management of artificial intelligence, etc.). The said shortcomings hinder its full application and harmonization with traditional sources of cadastral information. The author substantiated the need for universal digitization of the national cadastre and predicts the possibility of wider application of artificial intelligence in the natural-resource cadastral system. It is argued that the existing system in its current state may lead to wrong decisions and cadastral errors, hence, it is necessary to improve the legal regulation of cadastre.Scientific novelty: for the first time the results of the national cadastre digitization were assessed. Forecasts were given about the possibility of using artificial intelligence in this area, subject to further improvement of legal regulation. The latter is fundamentally important for reforming the cadastral system, since the technological basis of this system does not fully meet the needs of the digital economy.Practical significance: it is due to the lack of legal regulation of the artificial intelligence concept and legal status in the national legislation, as well as a unified approach to the cadastral system digitalization. Modern technologies are actively used in practice, but lack a sufficient legal basis. The main conclusions, proposals and recommendations of the study can be a basis for further improvement of the legal framework of Uzbekistan in terms of the application of artificial intelligence technologies.

‘Why Is the Chubby Guy Running?’: Trans Pregnancy, Fatness, and Cultural Intelligibility

ABSTRACTSince the late 2000s trans pregnancy has received increasing public and academic attention, and stories of the ‘pregnant man’ have become a media staple. Existing research has critiqued such spectacularization and the supposed tension between maleness, masculinity, and pregnancy that underpins it. Extending that work, this article draws on interview data from an international study of trans reproductive practices and analyzes participants' experiences of being, and expecting themselves to be, perceived in public space not as spectacularly ‘pregnant men’, but as fat men. As a starting point we take the experience of one participant whose heavily pregnant participation in a five‐kilometer race prompted the question: ‘Why is the chubby guy running?’ Using Judith Butler's concept of the cultural intelligibility of gender, we ask why the question asked was not: ‘Why is the pregnant guy running?’ We further consider the degree to which pregnant trans people manage their unintelligibility within the matrix of pregnancy, fatness, and trans/gender and how this reveals the limits of gender intelligibility itself.

The conceptual framework for the therapeutic approach used in phase 3 trials of MDMA-assisted therapy for PTSD.

Results from multiple recent studies support further evaluation of 3,4-methylenedioxymethamphetamine (MDMA) in conjunction with psychotherapy (i.e., MDMA-Assisted Therapy) in the treatment of post-traumatic stress disorder (PTSD). In two Phase 3 trials, MDMA-Assisted Therapy comprised a short-term, intensive psychotherapy that included three sessions directly facilitated by MDMA (referred to as "experimental sessions"), as well as a number of non-drug psychotherapy sessions. This treatment model aimed to harness the potential of MDMA to facilitate recall and processing of traumatic memories, and to increase learning in a social context, integrating "top-down" and "bottom-up" approaches to trauma-focused care. To date, the conceptual framework for this treatment has not been described in the scientific literature. This omission has contributed to misunderstandings about both the theoretical underpinnings of this modality and the therapeutic approach that emerges from it. This paper delineates the psychotherapeutic concepts, theories, and historical antecedents underlying the inner-directed approach to MDMA-Assisted Therapy for PTSD. Broadly speaking, this therapeutic framework centered the concept of the participant's inner healing intelligence as the primary agent of change, with the therapeutic relationship being the core facilitative condition fostering the participant's self-directed movement toward recovery and growth. Corollaries to this holistic, self-directed, relational, and trauma-informed framework include a non-pathologizing approach to the participant's embodied experience (including the possibility of intense emotional and somatic expression, experiences of multiplicity, suicidal ideation, and multigenerational and transpersonal experiences), as well as the therapists' own psychodynamic, somatic, and transpersonal awareness, empathic attunement, relational skillfulness, and cultural humility. The use of MDMA in conjunction with this psychotherapy platform outperformed the use of placebo with psychotherapy in Phase 2 and 3 trials, as measured by symptom reduction in participants with PTSD. However, within-group comparisons also identified significant symptom reduction in participants who did not receive MDMA, lending empirical support to the psychotherapy model itself. In addition to comparative efficacy trials, future research should investigate which elements of the conceptual framework and therapeutic approach underlie the clinical benefit in individuals with PTSD.

The invisible becomes visible.

The concept of artificial intelligence is not new and the technology has been running in the background of our lives for some time. But as it now comes to the fore, questions have begun to emerge alongside it.

Adapting Artificial Intelligence Concepts to Enhance Clinical Decision-Making: A Hybrid Intelligence Framework.

Artificial intelligence (AI) holds great potential for revolutionizing health care by providing clinicians with data-driven insights that support more accurate and efficient clinical decisions. However, applying AI in clinical settings is often challenging due to the complexity and vastness of medical information. This perspective article explores how AI development methodologies can be adapted to support clinicians in their decision-making processes, emphasizing the importance of a hybrid approach that combines AI capabilities with clinicians' expertise. We developed a conceptual framework designed to integrate AI-driven hybrid intelligence into clinical practice to enhance decision-making. This framework focuses on adapting key AI concepts, such as backpropagation, quantization, and avoiding overfitting, to help clinicians better interpret complex medical data and improve diagnosis and treatment planning. Several AI methodologies were adapted to enhance clinical decision-making. First, backpropagation allows clinicians to refine initial assessments by revisiting them as new data emerges, improving diagnostic accuracy over time. Second, quantization helps break down complex medical problems into manageable components, enabling clinicians to prioritize critical elements of care. Finally, avoiding overfitting encourages clinicians to balance rare diagnoses with more common explanations, reducing the risk of diagnostic errors and unnecessary complexity. The integration of AI-driven hybrid intelligence has the potential to enhance clinical decision-making. By adapting AI methodologies, clinicians can enhance their ability to analyze data, prioritize treatments, and make more accurate diagnoses while preserving the essential human aspect of health care. This framework highlights the importance of combining AI's strengths with clinicians' expertise for more effective and balanced decision-making in clinical practice. This perspective highlights the value of hybrid intelligence in achieving more balanced, effective, and patient-centered decision-making in health care.

THEORETICAL PRINCIPLES OF EMOTIONAL INTELLIGENCE FORMATION OF JUNIORS

In the 20th century, logical intelligence was key to science and education, but in the 21st century, when globalization has covered all areas of life, emotional intelligence and its practical and creative aspects have become relevant. The aim of the article is the analysis of the theoretical foundations of the emotional intelligence development of juniors. We have come to the conclusion that the concept of emotional intelligence emphasizes the importance of developing emotional skills in social and professional spheres of life, as well as their impact on personal success and well-being. Emotional intelligence can be improved through training and practice, and it plays an important role in interpersonal relationships, communication and leadership skills. Also, to form the emotional culture of students in modern conditions, as provided for in the Concept of the New Ukrainian School, it is necessary to use effective pedagogical methods and tools. With their help, teachers can contribute to the psychological support of children and help overcome difficult emotional states, both in the team and in each individual. It is significant to create a safe and supportive environment for children where they can feel safe and enjoy childhood life, learning and play again.

Political ideology shapes support for the use of AI in policy-making.

In a world grappling with technological advancements, the concept of Artificial Intelligence (AI) in governance is becoming increasingly realistic. While some may find this possibility incredibly alluring, others may see it as dystopian. Society must account for these varied opinions when implementing new technologies or regulating and limiting them. This study (N = 703) explored Leftists' (liberals) and Rightists' (conservatives) support for using AI in governance decision-making amidst an unprecedented political crisis that washed through Israel shortly after the proclamation of the government's intentions to initiate reform. Results indicate that Leftists are more favorable toward AI in governance. While legitimacy is tied to support for using AI in governance among both, Rightists' acceptance is also tied to perceived norms, whereas Leftists' approval is linked to perceived utility, political efficacy, and warmth. Understanding these ideological differences is crucial, both theoretically and for practical policy formulation regarding AI's integration into governance.

Artificial Intelligence and Education: A Metaphorical Analysis on the Perceptions of Students with Special Abilities

The main goal of this study is to reveal special talented primary school students' perceptions of artificial intelligence, one of the popular concepts of recent times, through metaphors. In this study, the phenomenological design, which is within the scope of qualitative research, was used. In this study, Türkiye Science and Art Center included special talented primary school students in the education field. The 104 special talented primary school students participating in the research were 9-14 years old. Of these students, 53 were boys and 51 were girls. The participants were in the 3th grade, 4th grade, 5th, 6th, 7th and 8th grades of primary education. Purposive sampling method was chosen in the research. When the answers from the students were examined, a total of 36 different categories emerged in line with the relevant metaphors. When these categories are examined in detail, it is seen that the concept of artificial intelligence is represented by different metaphors. According to the findings, it was determined that primary school students simulated the concept of artificial intelligence to different metaphors such as robot, smiling robot, robot vacuum cleaner. As a result, it has been revealed that special talented student generally have positive opinions about the concept of artificial intelligence.

Enhancing legal writing skills: The impact of formative feedback in a hybrid intelligence learning environment

AbstractRecent developments in artificial intelligence (AI) have significantly influenced educational technologies, reshaping the teaching and learning landscape. However, the notion of fully automating the teaching process remains contentious. This paper explores the concept of hybrid intelligence (HI), which emphasizes the synergistic collaboration between AI and humans to optimize learning outcomes. Despite the potential of AI‐enhanced learning systems, their application in a human‐AI collaboration system often fails to meet anticipated standards, and there needs to be more empirical evidence showcasing their effectiveness. To address this gap, this study investigates whether formative feedback in an HI learning environment helps law students learn from their errors and write more structured and persuasive legal texts. We conducted a field experiment in a law course to analyse the impact of formative feedback on the exam results of 43 law students, as well as on the writer (students), the writing product and the writing process. In the control group, students received feedback conforming to the legal common practice, where they solved legal problems and subsequently received general feedback from a lecturer based on a sample solution. Students in the treatment group were provided with formative feedback that specifically targeted their individual errors, thereby stimulating internal cognitive processes within the students. Our investigation revealed that participants who were provided with formative feedback rooted in their errors within structured and persuasive legal writing outperformed the control group in producing qualitative, better legal text during an exam. Furthermore, the analysed qualitative student statements also suggest that formative feedback promotes students' self‐efficacy and self‐regulated learning. Our findings indicate that integrating formative feedback rooted in individual errors enhances students' legal writing skills. This underscores the hybrid nature of AI, empowering students to identify their errors and improve in a more self‐regulated manner. Practitioner notesWhat is already known about this topic Collaboration between humans and AI in educational settings advances learning mutually, fostering a unified developmental process. Collaborative education models advocate leveraging human and AI strengths for adaptive learning. Despite abundant theoretical research, empirical studies in HI remain limited. This gap underscores the need for more evidence‐based approaches in integrating AI into educational settings. What this paper adds Field experiment investigating the impact of formative feedback in a hybrid intelligence learning environment based on the theory of learning from errors. Comparison of a traditional legal learning environment (lecturer teaching using sample solutions) versus formative feedback in a hybrid intelligence learning environment. Implementing formative machine learning‐based feedback supports law students in producing more structured and persuasive legal texts, leading to enhanced exam performance and higher grades. Implications for practice and/or policy Our research contributes significantly to computer‐based education by presenting empirical evidence of how formative writing feedback impacts students' legal knowledge and skills in educational settings. This underscores the importance of incorporating empirical data into the development of AI‐based educational tools to ensure their effectiveness. By focusing on individual errors corrected by formative feedback, we contribute to the learning from errors literature stream. This perspective offers valuable insights into how such feedback can support students' writing and learning processes, filling a gap in empirical evidence. Our findings demonstrate the potential impact of ML‐based learning systems, particularly in large‐scale learning environments like legal mass lectures. Formative writing feedback emerges as a scalable and beneficial addition to traditional learning environments, triggering internal learning processes, fostering self‐regulated learning and increasing self‐efficacy among students. By demonstrating the effectiveness of formative feedback within the framework of HI, particularly in legal education, our research underscores the potential of combining human understanding with AI‐supported feedback to enhance learning outcomes.

Connecting the Wings of Dynamism: Bibliometric Analysis of Artificial Intelligence and Entrepreneurship Fields

This study aims to create a holistic viewpoint by concentrating on two dynamic areas of artificial intelligence and entrepreneurship with bibliometric analysis. The concept of artificial intelligence, which is constantly heard as the digital world gradually penetrates our lives, and entrepreneurship, which is referred to as the atomic element of the economic infrastructure, are addressed in the same pot with this research. The attitude of both areas against varying circumstances constitutes the essential basis of this examination. The view that the effectiveness in the areas can be increased with the synergy to be created between the two focuses is supported. With this intention, the study commences with an informative literature section, where the introductory elements of the areas are conveyed. Afterward, it tries to clarify why these zones need to be examined together. Following this, a bibliometric analysis study, frequently used to bring unfamiliar kinds of literature jointly, is conducted using data obtained from the Web of Science database and subjected to various analyses. In the last stage, the study is completed by examining these outputs and analyzes. As a result, conclusions support “the duo” can be investigated jointly. The study contributes to the idea that artificial intelligence and entrepreneurship are wings working in synchrony for the requirement of success.

General Definitions of Information, Intelligence, and Consciousness from the Perspective of Generalized Natural Computing

Atoms themselves have no thoughts and cannot be thinking. Why does the human body, which is composed of atoms, have consciousness? The widely used concepts of information and intelligence in today’s science, which are related to this, do not yet have appropriate general definitions. Answering these interesting questions is a crucial issue for technological development in the historical context of human society entering the era of intelligence. The key lies in how to fully utilize the existing fundamental theories subtly related to information science. Here we attempt to give the definition of general information and general intelligence from the perspective of generalized natural computing, based on the least action principle, Hamilton-Jacobi equation, dynamic programming, reinforcement learning, and point out the relationship between the two. The least action principle for describing conservative systems can be seen as an intelligent manifestation of natural matter, and its equivalent form, the Hamilton-Jacobi equation, can be extended to describe quantum phenomena and is a special case of continuous dynamic programming equations. Dynamic programming is an efficient optimization method under deterministic models, while reinforcement learning, as a manifestation of biological intelligence, is its model-free version. The statement that reinforcement learning is the most promising machine learning method has a profound physical foundation. General information is defined as the degree to which a certain environmental element determines the behavior of the subject. General intelligence is defined as the automatic optimization ability of the action or value function of a system with a certain degree of conservatism. Intelligence is a basic property of material systems, rather than an emergent property that only complex systems possess. Consciousness is an advanced intelligent phenomenon, a reconstruction of quasi conservative systems based on complex systems.

The relationship between the concepts of intelligence and artificial intelligence

Artificial intelligence is needed to perceive, remember, creatively process incoming information an order of magnitude better and faster than natural intelligence (or in reverse order), constantly replenish its reserves in order to make extraordinary decisions and actions in specific situations. However, without natural intelligence, all this is impossible to do, and therefore it would be correct to say about the improvement of the prototype (its elements), and not the creation of an artificial prototype.

Investigation of the Awareness of Automated News in terms of Public Opinion: Artificial Intelligence Journalism

Developments in information and communication technologies have created change and transformation in news production and reading habits, and the use of artificial intelligence in the journalism sector has become inevitable as the process transforms society. With artificial intelligence journalism, news can be produced and edited quickly. However, this speed and automation can create problems for readers in terms of accuracy and detail in consuming the news. In this context, the roles of journalists are also changing, and they are effective in checking the accuracy of news and developing public awareness. Automated news production has developed with the use of artificial intelligence-based software. In the automated news production process, which has come to the agenda through artificial intelligence, the automated creation of news through a number of algorithms and software and its rapid presentation to consumers has taken its place in the new generation journalism. Therefore, with the use of artificial intelligence in news production, public awareness should also be taken into consideration. This study investigates public awareness in the context of automated news production and news reliability, objectivity, confidentiality, employment problems of journalists and ethical rules. The independent variables of the study are the gender, age, education and occupation of newspaper consumers, while the dependent variables are the public opinion on the characteristics of the news in the face of news produced with artificial intelligence. The population of the study consists of individuals who use social media, read digital newspapers and are over the age of 18, while the sample consists of 400 people who voluntarily responded to the study. In the study, half of the participants are aware of the concept of artificial intelligence and that news is sometimes produced with artificial intelligence. Most of the participants think that weather news is produced by artificial intelligence, as well as economy, earthquake, weather and sports news are all produced automatically. The majority of the participants think that they are concerned about the reliability of the news produced by artificial intelligence, that these news are more qualified and more reliable than the news produced by the traditional news production process. Some of the participants also think that their news needs should be met through news produced by artificial intelligence today. It has been also found that there were significant differences between the gender, age and occupational groups of the participants and their attitudes towards news produced by artificial intelligence.

The Use of eXplainable Artificial Intelligence and Machine Learning Operation Principles to Support the Continuous Development of Machine Learning-Based Solutions in Fault Detection and Identification

Machine learning (ML) revolutionized traditional machine fault detection and identification (FDI), as complex-structured models with well-designed unsupervised learning strategies can detect abnormal patterns from abundant data, which significantly reduces the total cost of ownership. However, their opaqueness raised human concern and intrigued the eXplainable artificial intelligence (XAI) concept. Furthermore, the development of ML-based FDI models can be improved fundamentally with machine learning operations (MLOps) guidelines, enhancing reproducibility and operational quality. This study proposes a framework for the continuous development of ML-based FDI solutions, which contains a general structure to simultaneously visualize and check the performance of the ML model while directing the resource-efficient development process. A use case is conducted on sensor data of a hydraulic system with a simple long short-term memory (LSTM) network. Proposed XAI principles and tools supported the model engineering and monitoring, while additional system optimization can be made regarding input data preparation, feature selection, and model usage. Suggested MLOps principles help developers create a minimum viable solution and involve it in a continuous improvement loop. The promising result motivates further adoption of XAI and MLOps while endorsing the generalization of modern ML-based FDI applications with the HITL concept.