Artificial intelligence and machine learning

The article examines the role of modern digital tools, namely artificial intelligence and machine learning technologies, in promoting tourist services for small businesses on social media. The article highlights some of the problems and shortcomings, faced by entrepreneurs when promoting a tourism product in the digital landscape. Examples are given of how customer experience, reflected in social media, can be useful for increasing the popularity of small businesses and attracting new customers. The importance of active promotion of tourist products through social networks is considered, further emphasized by scientific research by domestic and foreign authors. A hypothesis regarding the importance of using social networks for advertising small businesses with limited budgets is formulated and tested. The article proposes a new approach to quantitative and qualitative assessment of social media activity for small businesses, which is based on a complex multifactorial indicator consisting of the number of publications in social media, the number of customer reviews and the rating by reviews. The effectiveness of utilizing marketing opportunities in social media to increase the value of services and profitability of small businesses in the tourism industry has been demonstrated based on a paired regression model. The study investigates how self-learning artificial intelligence digital systems can be used to enhance the productivity of small businesses in the tourism industry and which areas of interaction with consumers of a tourism product are of most interest to AI (artificial intelligence) developers. Based on the obtained results, practical recommendations are provided for preparing small business entities in the tourism sector for activity on social media using the opportunities arising from the development of artificial intelligence and machine learning technologies.

With the continuous development of artificial intelligence technology, people’s research on it has become more in-depth and machine learning has become a new research core issue in artificial intelligence. Before artificial intelligence technology was mature, it was a very difficult task to realize automatic character recognition and input. At present, human beings have made some progress in intelligent robots, voice recognition and network search. The text recognition method based on machine learning is of great significance to the promotion of information technology. Text recognition based on machine learning, although it has formed relatively rich theoretical support in principle and skill, but still faces various factors during actual practice, making its recognition effect difficult to be reflected. Based on the feature fusion perspective, this paper combines the background and main identification methods of text recognition in artificial intelligence machine learning to analyze the purpose, which lays a foundation for future specific applications.

Against the background of the new era, the birth of artificial intelligence technology opened a new chapter in human development. Artificial intelligence technology has been developed in many places. The report discusses the application and innovation of artificial intelligence technology in college sports. The first thing people think of artificial intelligence is robots. In fact, it can be said that artificial intelligence can replace human labor. Artificial intelligence is a product of modern technology and cannot be understood unilaterally. For example, the various means of transportation used in people’s daily travel is an artificial intelligence that can replace people’s walking. Another example is that elevators can replace people on stairs. It is also a type of artificial intelligence, which is not an advanced technology. Product, it is also seen everywhere in people’s daily life. Artificial intelligence is a synthesis of scientific and intelligent technology created by human beings. It is a scientific technology including computer science, cognition, logic, etc., formed through mutual intersection and integration. In recent years, artificial intelligence has achieved considerable development, but there are still a series of unsolved problems, and the development of artificial intelligence is still very long. This article studies what artificial intelligence has facilitated and changed the physical education in colleges and universities, and what changes have taken place in the teaching mode are all worthy of our thinking. As a result, artificial intelligence technology is convenient for teachers and students in teaching. Let’s take a look at how to innovate.

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

The fast development of Artificial Intelligence (AI) and machine learning (ML) technologies has had an influence, on different industries with education being one of the most changed. Incorporating AI and ML into education platforms has transformed conventional teaching and learning methods making personalized learning experiences easier automating administrative duties and improving the efficiency of educational delivery. This article delves into the role played by AI and ML in shaping the future of online education focusing on their practical applications, advantages, and the potential obstacles they may present. By examining implementations across different educational levels this research underscores how these technologies have the power to revolutionize education by making it more accessible, engaging, and efficient in an ever-evolving global environment. Additionally, it delves into the implications of these technologies such as their influence on educational equality, teacher responsibilities and the integration of data driven decision making, in designing curricula and teaching approaches. The discourse also touches upon considerations surrounding data usage in educational contexts to ensure that AI and ML contribute positively while upholding student privacy rights and fairness. Keywords: Artificial Intelligence, Machine Learning, Remote Education, Personalized Learning, Educational Technology

Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) – Forecasting

The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.

This article is dedicated to identifying the fundamental differences between artificial intelligence and machine learning. The aim of the research is to analyze, systematize, and improve the existing theoretical and methodological framework concerning the functioning of artificial intelligence and machine learning, as well as to define the distinctions between these systems. The article examines the current state and prospects for the development of artificial intelligence and machine learning as integral components of innovative technologies that facilitate the automation of complex processes and enhance efficiency across various fields. The research outlines the main stages in the development of artificial intelligence: weak artificial intelligence, which specializes in performing narrow tasks; strong artificial intelligence, aimed at achieving human cognitive capabilities; and superintelligence, which is expected to surpass human intelligence in many aspects. The study substantiates the role of machine learning as a key tool for implementing artificial intelligence, enabling the creation of systems that can self-learn and adapt to changing conditions without additional programming. The article provides examples of the application of artificial intelligence and machine learning in various sectors such as medicine, finance, cybersecurity, marketing, and transportation, where these technologies contribute to improving diagnostic processes, forecasting market fluctuations, and optimizing decision-making. In particular, the main advantages of machine learning are identified, including adaptability and the ability to make predictions based on large data sets, which enhances the effectiveness of analysis and decision-making in real time. The research into the development of artificial intelligence has revealed the technical and ethical challenges associated with creating strong artificial intelligence and superintelligence, which require the development of appropriate regulatory measures. The article emphasizes the significance of artificial intelligence and machine learning for modern society, their impact on various fields of economics, science, and technology, as well as the necessity for further research to ensure the safe and effective development of these technologies.

The purpose of this research is to investigate the nature of the relationship between artificial intelligence (AI) and machine learning (ML) technologies in service-based organizations on the one hand and organizational resilience on the other, as well as the potential impact of external market shocks on this relationship. A quantitative survey of 171 service-based groups was used to perform the research. The study's findings indicate that using AI and ML technologies in service delivery has a beneficial and substantial effect on organizational resilience. Furthermore, the research discovered that the degree to which external market shocks influence the efficacy of these technologies in improving an organization's resilience moderates the connection between the use of AI and ML technologies and organizational resilience. According to the results, service-based companies can improve their resilience by incorporating AI and ML technologies into their service delivery processes. They must, however, evaluate the possible influence of external market shocks on the efficacy of these technologies in increasing resilience. The research has ramifications for service-based organizations and adds to the body of knowledge on organizational resilience and the application of AI and ML technologies in service delivery.

In recent years, due to the rapid development of artificial intelligence (AI) and machine learning, its application has been widely used in many aspects of financial area, as well as significantly impacts financial market, institutions and regulation. The artificial intelligence technology brings enormous change to the entire financial industry, which creates a series of innovative financial services such as intelligent consultant, intelligent lending, monitoring and warning, and intelligent customer service as times required. In this paper, it aims to summarize the development and application of artificial intelligence and machine learning in financial system, as well as its impacts on macroeconomics and microeconomics. In the meantime, it is realised that a series of problems and risks were conducted by artificial intelligence during its use. Lastly, some suggestions and strategies are provided for reasonable usage of artificial intelligence in financial risk management, based on the financial risk management raised by artificial intelligence.

PurposeAs artificial intelligence (AI) and machine learning (ML) technologies continue to revolutionize various industries, understanding their impact on job embeddedness becomes crucial. This study examine the role of AI and ML technologies on job embeddedness, identifying key trends and proposing future research directions. It seeks to understand how these technologies influence employee attachment within organizations.Design/methodology/approachThis study uses bibliometric analysis to assess 890 articles published from 2001 to 2023 on job embeddedness and its relationship with AI and ML. The Scopus database is examined utilizing VOSviewer and Biblioshiny applications to determine themes and research deficiencies. This study visualizes the intellectual landscape of this area.FindingsThis study highlights the growing interest in AI and ML job embeddedness, highlighting the complex relationship between AI adoption and employee attachment, links and fit. It also highlights emerging themes like AI-enabled talent management, remote work implications and ethical considerations in AI-driven workplaces.Research limitations/implicationsThe extent of the Scopus database, the time period and the metadata correctness all provide restrictions on this investigation of how AI and ML affect job embeddedness. Nonetheless, the results underscore the need for empirical study on the effects of AI and ML and provide researchers with useful insights. This study also highlights how technological improvements influenced employee attitudes and actions.Originality/valueThis study presents a comprehensive overview of job embeddedness and AI/ML technologies, utilizing bibliometric techniques to evaluate research publications. It reveals key trends, identifies gaps and suggests future directions in this field.

In order to identify areas that need further research, this study will analyze how machine learning (ML) and artificial intelligence (AI) technologies are applied in various customer services. This is done by using a systematic review of literature procedure to examine publications on the application of AI and ML in customer service that have been posted on various academic websites. Numerous AI and ML approaches may be applied by businesses to improve customer support and assistance. The use of self-service technologies, integrated product-service offers, service excellence, and word-of-mouth marketing are some of these techniques. The study’s conclusion addresses the customer service knowledge gaps that it identified when reviewing the literature. The information provided by the current study on how Artificial Intelligence (AI) and Machine Learning (ML) technologies may be applied or properly used to improve customer service or may be fruitful to businesses. The findings of the study will provide theoretical and practical implication of AI and ML in customer support system to develop more effective and efficient strategies to the users and policy makers. Ultimately the findings of the study will help increase the degree of satisfaction of customer. Longitudinal studies should assess AI and ML’s long-term impact on customer support systems, balancing human-AI collaboration, developing transparent models, considering cross-cultural factors, and adopting a human-centric design approach.

Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the increasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polypharmacology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based virtual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided assistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effective drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the descriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.

Industry 4.0 brought with it by the next-gen Industry 5.0 and Society 5.0 paradigms, catalysed by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies. These advances have the benefit of encouraging sustainability, improving output, and updating manufacturing. By enabling self-decision, continuous monitoring, and predictive maintenance with the processing of large data, AI is dramatically reducing downtime and associated costs of system downtime. As a result, ML algorithms, in light of their applicability for continuous learning and adaptation, have contributed to enriching product quality, streamlining supply networks and okaying personalized customer experiences. Neural networks are also being leveraged to improve computer vision and speech capabilities, for applications such as smart automation and human-robot cooperation in challenging industrial contexts. Industry 5.0 truly puts humans back at the centre of innovation. It is aimed to create an evolved society in which AI, ML, and DL are fused with the digital and physical world Society 5.0. The integration aims to address a plethora of societal challenges: environmental sustainability, health and ageing population, among others. It is a convergence of these said technologies that lead to a paradigm shift towards more resilient, adaptive, and sustainable industrial ecosystems. This paper aims to address these questions in a systematic way to offer a comprehensive view of what the future industrial landscape could look like leveraging the promise of Industry 4.0 and Industry 5.0 thus, and more opportunities to embrace intelligent and sustainable industries of tomorrow.