The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

This exploratory study aims to identify the main characteristics and relationships between artificial intelligence (AI) and broadband development in Asia and the Pacific. Broadband networks are the foundation and prerequisite for the development of AI. But what types of broadband networks would be conducive are not adequately discussed so far. Furthermore, in addition to broadband networks, other factors, such as income level, broadband quality, and investment, are expected to influence the uptake of AI in the region. The findings are synthesized into a set of policy recommendations at the end of the article, which highlights the need for regional cooperation through an initiative, such as the Asia-Pacific Information Superhighway (AP-IS). 

The rapid advancement of information and communication technology has greatly facilitated access to information across various sectors, including healthcare services. This digital transformation demands enhanced knowledge and skills among healthcare providers, particularly in comprehensive midwifery care. However, midwives in rural areas face numerous challenges such as limited resources, cultural factors, knowledge disparities, geographic conditions, and technological adoption. This research aims to evaluate the impact of AI utilization on midwives’ knowledge and behavior to optimize the implementation of healthcare services in accordance with Delima Midwife Service standards in rural settings. The analysis encompasses competencies, characteristics, information systems, learning processes, and health examinations conducted by midwives in adopting AI. The research methodology employs a cross-sectional approach involving 413 rural midwives selected proportionally. Results from Partial Least Squares Structural Equation Modeling indicate that all reflective evaluation variables meet the required criteria. Fornell-Larcker criterion demonstrates that the square root of AVE is greater than other variables. The primary findings reveal that information systems (0.029) and midwives’ competencies (0.033) significantly influence AI utilization. Furthermore, midwives’ competencies (0.002), characteristics (0.031), and AI utilization (0.011) also significantly impact midwives’ knowledge and behavior. Midwives’ characteristics also significantly affect their competencies (0.000), while midwives’ learning influences health examinations (0.000). Midwives’ knowledge and behavior affect the transformation of healthcare services in rural midwifery (0.022). The model fit results in a value of 0.097, empirically supporting the explanation of relationships among variables in the model and meeting the established linearity test.

The aim of our study is to provide information on how and to what extent professionals of art institutions in Hungary and Slovakia (contemporary galleries and museums) use artificial intelligence in their work processes. Our research focuses on the extent to which these institutions use artificial intelligence in the development of the institution’s operational strategy, or how they can embed the assumed usefulness of artificial intelligence in the operation of the institution, be it the creation of an exhibition, the textual processing of the professional life of an artist, or a about a tool that shapes the gallery’s marketing strategy. We conducted ten in-depth interviews in the two countries, the interviewees were selected using the snowball method. The interview took place among professionals and professionally credible artists who are actively active in contemporary fine art life. The results revealed that the use of artificial intelligence as a tool in the creative work processes is not a requirement in the field of culture, neither in Hungary nor in Slovakia. All the interviewees already had professional experience with AI, 90% of those interviewed would like to deepen their knowledge of the creative use methods of AI, e.g., by creating working groups in the workplace on an experimental basis. Based on our conclusions, we can say that artificial intelligence currently has no conscious strategic use in contemporary art institutions. It can be said that creative professionals are aware of the possibilities of using artificial intelligence in their own field of image, video, and text creation, but there is uncertainty on the part of creators and curators when it comes to copyright. The in-depth interviews provided source material for the compilation of a standardized set of questions for a larger survey of 300-500 people, proportional to the sample, so our presented results are partial results of a larger research.

This paper mainly discusses the application and impact of AI tools in vocational college students' career planning and employment preparation in Chinese Mainland. Through a review and analysis of relevant literature, this article found that artificial intelligence tools can provide students with more information and assistance, thereby improving their career cognition and employment competitiveness. However, if artificial intelligence tools are not open to Chinese users or students overly rely on these tools, it may also bring some negative effects, such as job anxiety and decreased self-awareness. Therefore, the government and teaching departments should strengthen the education of career planning and employment preparation, improve the artificial intelligence system, establish personalized service mode and other measures to provide more comprehensive and personalized career recommendation and employment services for higher vocational students in Chinese Mainland.

The idea of emotions that is concealed in human language gives rise to metaphor. It is challenging to compute and develop a framework for emotions in people because of its detachment and diversity. Nonetheless, machine translation heavily relies on the modeling and computation of emotions. When emotion metaphors are calculated into machine translation, the language is significantly more colorful and satisfies translating criteria such as truthfulness, creativity and beauty. Emotional metaphor computation often uses artificial intelligence (AI) and the detection of patterns and it needs massive, superior samples in the emotion metaphor collection. To facilitate data-driven emotion metaphor processing through machine translation, the study constructs a bi-lingual database in both Chinese and English that contains extensive emotion metaphors. The fundamental steps involved in generating the emotion metaphor collection are demonstrated, comprising the basis of theory, design concepts, acquiring data, annotating information and index management. This study examines how well the emotion metaphor corpus functions in machine translation by proposing and testing a novel earthworm swarm-tunsed recurrent network (ES-RN) architecture in a Python tool. Additionally, the comparison study is carried out using machine translation datasets that already exist. The findings of this study demonstrated that emotion metaphors might be expressed in machine translation using the emotion metaphor database developed in this research.