Advances In Artificial Intelligence Technology Research Articles

Integrating Artificial Intelligence into Traditional Sports: A Case Study of the Asian Games and Future Directions for Modernization

Abstract: This paper explores the integration of Artificial Intelligence (AI) into traditional sports, addressing a critical gap in existing research through a qualitative case study of the Asian Games. AI's application in modern sports has demonstrated its potential to enhance event management, athlete preparation, and scoring fairness. However, its adoption in traditional sports, such as Wushu and Chinese wrestling, remains limited. This study examines the challenges and opportunities associated with applying AI to traditional sports, offering development strategies to modernize these disciplines. By analyzing the impact of AI in modern competitive sports, the research proposes specific recommendations for integrating AI into traditional sports, aiming to preserve their cultural heritage while advancing their global relevance. Limitations include the focus on a single event and the exclusion of the most recent AI developments. Future research should consider a broader range of global sporting events and incorporate the latest advancements in AI technology. This study provides a theoretical and practical foundation for the application of AI in traditional sports, with implications for future technological innovation and the evolution of the sports industry.

Artificial intelligence, medications, pharmacogenomics, and ethics.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

Exploring the Effect of Attachment on Technology Addiction to Generative AI Chatbots: A Structural Equation Modeling Analysis

With the advancement of artificial intelligence technology, generative AI chatbots (GAIC) such as ChatGPT, are integrating into various aspects of society and quietly changing people’s lives. However, although there is a plethora of research concerning social media usage and addiction, discussions about chatbots remain far from adequate. Investigating the use and potential technology addiction to generative AI chatbots (TAGAIC) holds both theoretical and practical significance. Drawing on attachment theory, this study aims to investigate the formative factors in TAGAIC. A structural equation model was used to analyze the data collected from a questionnaire survey of 364 GAIC users. Results reveal that technology addiction is positively influenced by emotional attachment but not by functional attachment. Besides, perceived anthropomorphism and perceived empathy have a positive effect on emotional attachment, while system quality and information quality have a positive effect on functional attachment. Limitations of the study were discussed.

Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions

The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and policymakers about the potential and challenges of integrating AI into audiological practice. The PubMed, Cochrane, and Google Scholar databases were searched for articles published in English from 1990 to 2024 with the following query: “(audiology) AND (“artificial intelligence” OR “machine learning” OR “deep learning”)”. The PRISMA extension for scoping reviews (PRISMA-ScR) was followed. The database research yielded 1359 results, and the selection process led to the inclusion of 104 manuscripts. The integration of AI in audiology has evolved significantly over the succeeding decades, with 87.5% of manuscripts published in the last 4 years. Most types of AI were consistently used for specific purposes, such as logistic regression and other statistical machine learning tools (e.g., support vector machine, multilayer perceptron, random forest, deep belief network, decision tree, k-nearest neighbor, or LASSO) for automated audiometry and clinical predictions; convolutional neural networks for radiological image analysis; and large language models for automatic generation of diagnostic reports. Despite the advances in AI technologies, different ethical and professional challenges are still present, underscoring the need for larger, more diverse data collection and bioethics studies in the field of audiology.

ChatGPT-4o, ChatGPT-4 and Google Gemini are compared with Students: A Study in Higher Education

The rapid advancement of artificial intelligence technologies, their pervasive use in every field, and the growing understanding of the benefits they bring have led actors in the education sector to pursue research in this field. In particular, the use of artificial intelligence tools has become more prevalent in the education sector due to the increasing number and functionality of these tools. The educational research conducted in the 4o version, which is relatively new in the context of ChatGPT tools that have gained popularity, has not yet produced a substantial body of evidence. In this study, ChatGPT-4, 4o and Google Gemini were subjected to a physiology examination taken by veterinary department students. A comparative analysis of the students' performances and the performances of these tools was conducted. In the comparison within the artificial intelligence tools themselves, the latest ChatGPT-4o version showed an accuracy rate of 90%. It was followed by Gemini and ChatGPT-4. All of the tools used in the physiology course exam were able to pass the exam with a satisfactory level. ChatGPT-4o, 4 and Gemini outperformed the students.

Harnessing Artificial Intelligence in Stock Market Investments: A Neomarxist Approach to Alternative Socioeconomic Funding Agendas for National State Revenue Accumulation

In the contemporary economic, sociological and political landscape, the successful integration of Artificial Intelligence (AI) computer programs into the predicted behaviour of stock market value in the financial markets represents a profound burgeoning frontier in economics, with profound implications for both wealth generation and national budget resource allocation. This paper explores the potential sociopolitical economic strategy of using AI-driven stock market prediction predictions to cumulatively accumulate capital funded for by taxpayer sourced representative income. As the baseline return accumulates in the form of investment potential then as a means of revenue accumulation for the state then the burden of the national budget upon its citizens decreases then ceases. This is the theorisation of the establishment of a Marxist economic social superstructure for the funding of social institutions through the powerful potential of artificial intelligence. By leveraging a portion of the national budget on advanced AI technologies, this strategy could dramatically facilitate the financial sustenance of political budgets aimed at social equity and economic justice. Through a critical review of sociological and economic theories, this theoretical research delineates exactly how such an approach could potentially reconfigure the mode of production and contribute to the reformation of a sustainable socialist economy.

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON LANGUAGE LEARNING

This article explores the significant impact of artificial intelligence (AI) oneducation, particularly in the field of English language learning. By exploring current advances in artificial intelligence technology, including natural language processing and machine learning algorithms, the article highlights how AI-based tools and applications are revolutionizing the way Englishis taught and learned. In addition, the article discusses potential challenges and ethical considerations associated with the integration of AI into education, emphasizing the importance of responsible and inclusive implementation. Overall, this article highlights the changing role of artificial intelligence in shaping the future of English language education, providing in sight into its benefits, opportunities, and implications for students and teachers.

Development of Internet of Things and Artificial Intelligence for intelligent sanitation systems: A literature review

Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

A Practical Study on Artificial Intelligence (AI)-Assisted College English Translation Teaching

With the advent of the digital age, it has become an inevitable trend to integrate advanced artificial intelligence technology with English translation teaching in colleges and universities. This paper aims to explore the practical application of artificial intelligence (AI)-assisted English translation teaching in colleges and universities, analyze its advantages in improving teaching efficiency, promoting personalized learning, and providing intelligent tutoring, and put forward corresponding strategies and suggestions. Through specific cases, this paper demonstrates the actual application effect of AI in translation teaching and looks forward to the future development direction.

Artificial Intelligence Approach to Business Process Re-Engineering the Information Flow of Warehouse Shipping Orders: An Italian Case Study

Artificial intelligence is revolutionizing and significantly shaping the work environment. It not only presents an innovative and unique challenge, but it also creates numerous opportunities in various sectors. This study aims to examine a case of artificial intelligence use in the logistics sector, specifically the implementation of text recognition algorithms that use advanced artificial intelligence technology to perform tasks and activities typically carried out by human operators. The implementation under analysis was carried out at an Italian company in Tolentino, in the province of Macerata. The implementation of this tool focuses on the ability to autonomously read shipping notes, with the aim of simplifying the flow of information in the goods receipt phase and reducing the workload of warehouse staff. As illustrated in the following chapters, when artificial intelligence follows several trainings, technical improvements and structural changes in shipping notes, the flow is greatly simplified, significantly facilitating this logistics phase.

Applications of ChatGPT in Heart Failure Prevention, Diagnosis, Management, and Research: A Narrative Review.

Heart failure (HF) is a leading cause of mortality, morbidity, and financial burden worldwide. The emergence of advanced artificial intelligence (AI) technologies, particularly Generative Pre-trained Transformer (GPT) systems, presents new opportunities to enhance HF management. In this review, we identified and examined existing studies on the use of ChatGPT in HF care by searching multiple medical databases (PubMed, Google Scholar, Medline, and Scopus). We assessed the role of ChatGPT in HF prevention, diagnosis, and management, focusing on its influence on clinical decision-making and patient education. However, ChatGPT faces limited training data, inherent biases, and ethical issues that hinder its widespread clinical adoption. We review these limitations and highlight the need for improved training approaches, greater model transparency, and robust regulatory compliance. Additionally, we explore the effectiveness of ChatGPT in managing HF, particularly in reducing hospital readmissions and improving patient outcomes with customized treatment plans while addressing social determinants of health (SDoH). In this review, we aim to provide healthcare professionals and policymakers with an in-depth understanding of ChatGPT's potential and constraints within the realm of HF care.

Smart traffic monitoring with YOLOv9 object detection algorithm

Rapid advancements in artificial intelligence technology have enabled computer vision to be utilized across a wide range of engineering disciplines. This study examines the practical solutions offered by image processing technology in manual counting applications and the accuracy of advanced algorithms. The applicability and performance of the YOLOv9 algorithm in traffic counts have been evaluated. The research shows that the algorithm operates with high accuracy and minimizes human error. The study involves classification and counting operations for three different types of vehicles. According to the results, cars and trucks are detected with over 95% accuracy, while smaller objects like motorcycles have slightly lower accuracy. The effective use of YOLOv9 in vehicle counting and traffic management applications highlights the importance of object detection technology in intelligent transportation systems. This study demonstrates the potential of this technology to improve efficiency in traffic management, providing guidance for future applications. The key role that advanced algorithms like YOLOv9 can play in the development of intelligent transportation systems is an important topic for future researchers and industry professionals.

Comparison of Large Language Models in Generating Machine Learning Curricula in High Schools

With the rapid advancement of artificial intelligence technologies, the integration of AI concepts into educational curricula represents an increasingly important issue. This paper presents a comparative analysis of four AI large language models, ChatGPT (now GPT-4o), Bard (now Gemini), Copilot, and Auto-GPT, in the last year, progressing from the previous into the newer versions, thus also revealing the progress over time. Tasks were selected from the Valence project, which aims to advance machine learning in high school education with material designed by human experts. The four LLMs were assessed across 13 topics, 35 units, and 12 code segments, focusing on their code generation, definition formulation, and textual task capabilities. The results were analyzed using various metrics to conduct a comprehensive evaluation. Each LLM was allowed up to five attempts to produce outputs closely aligned with human-written materials, with experts providing iterative feedback. This study evaluated the effectiveness and accuracy of these LLMs in educational content creation, offering insights into their potential roles in shaping current and future AI-centric education systems.

Neurophysiological markers of early cognitive decline in older adults: a mini-review of electroencephalography studies for precursors of dementia.

The early detection of cognitive decline in older adults is crucial for preventing dementia. This mini-review focuses on electroencephalography (EEG) markers of early dementia-related precursors, including subjective cognitive decline, subjective memory complaints, and cognitive frailty. We present recent findings from EEG analyses identifying high dementia risk in older adults, with an emphasis on conditions that precede mild cognitive impairment. We also cover event-related potentials, quantitative EEG markers, microstate analysis, and functional connectivity approaches. Moreover, we discuss the potential of these neurophysiological markers for the early detection of cognitive decline as well as their correlations with related biomarkers. The integration of EEG data with advanced artificial intelligence technologies also shows promise for predicting the trajectory of cognitive decline in neurodegenerative disorders. Although challenges remain in its standardization and clinical application, EEG-based approaches offer non-invasive, cost-effective methods for identifying individuals at risk of dementia, which may enable earlier interventions and personalized treatment strategies.

GPT-4-based AI agents-the new expert system for detection of antimicrobial resistance mechanisms?

The study titled "GPT-4-based AI agents-the new expert system for detection of antimicrobial resistance mechanisms?" is critically important as it explores the integration of advanced artificial intelligence (AI) technologies, like generative pre-trained transformer (GPT)-4, into the field of laboratory medicine, specifically in the diagnostics of antimicrobial resistance (AMR). With the growing challenge of AMR, there is a pressing need for innovative solutions that can enhance diagnostic accuracy and efficiency. This research assesses the capability of AI to support the existing two-step confirmatory process recommended by the European Committee on Antimicrobial Susceptibility Testing for detecting beta-lactamases in Gram-negative bacteria. By potentially speeding up and improving the precision of initial screenings, AI could reduce the time to appropriate treatment interventions. Furthermore, this study is vital for validating the reliability and safety of AI tools in clinical settings, ensuring they meet stringent regulatory standards before they can be broadly implemented. This could herald a significant shift in how laboratory diagnostics are performed, ultimately leading to better patient outcomes.

Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

Reinforcement Learning-Based Optimal Hull Form Design with Variations in Fore and Aft Parts

Abstract With recent advancements in artificial intelligence technology, various studies are being conducted in the shipbuilding industry. Traditionally, hull form variation methods have relied on the intuition and expertise of designers, leading to inconsistent results and unintended changes in the ship's main dimensions depending on the designer's competence. Moreover, the iterative process of design variation and analysis to derive the optimal hull form is both costly and time-consuming. To address these issues, this study proposes an optimal hull design technique utilizing reinforcement learning, a type of unsupervised learning in machine learning. Reinforcement learning allows the model to learn from past policies by recording and accumulating the rewards associated with various actions taken by an agent in a specific environment. In this study, after calculating the main parameters of the ship, the agent defines a state representing hull information and performs local transformations of the bow and stern. The reward of reinforcement learning is defined as the change in total resistance due to the hull deformation, constrained by limiting the tolerance of the ship's prismatic coefficient (CP) and longitudinal center of buoyancy (LCB). In this study, the problem is solved by comparing the proximal policy optimization (PPO) algorithm and the deep deterministic policy gradient (DDPG) algorithm to find the best deep reinforcement learning (DRL) model for the hull optimization problem. The results were compared with the genetic algorithm and speed-constrained multi-objective particle swarm optimization (SMPSO), and the optimal hull resistance values were less different, but the time of the reinforcement learning model was five times shorter.

The Role of Artificial Intelligence in Autonomous Vehicles: Challenges and Opportunities

Autonomous vehicles (AVs) represent a paradigm shift in transportation, promising safer, more efficient, and convenient mobility. Central to the realization of AVs are advancements in artificial intelligence (AI) technologies, enabling vehicles to perceive their environment, make decisions, and navigate autonomously. This paper examines the multifaceted role of AI in AVs, focusing on the challenges and opportunities it presents. The discussion encompasses the application of AI techniques such as machine learning, computer vision, and sensor fusion in enabling autonomous driving systems. Furthermore, it addresses the technical, regulatory, and ethical challenges associated with AI implementation in AVs, including sensor limitations, data privacy concerns, and legal frameworks. Despite these challenges, AI-enabled AVs offer significant opportunities, including improved road safety, enhanced mobility, and reduced environmental impact. Recent innovations and advancements in AI technologies pave the way for overcoming existing barriers and accelerating the adoption of AVs. Through case studies and real-world applications, this paper highlights the transformative potential of AI-driven AVs across various sectors, from transportation and logistics to urban planning. Looking ahead, future research and development efforts must focus on addressing remaining challenges and maximizing the societal benefits of AI in autonomous vehicles.

New intelligent flower arrangement system

Currently, the art of flower arrangement plays a significant role in both personal life and collective work. However, for non-professionals, creating flower arrangements with high aesthetic value can be challenging. With the rapid advancement of artificial intelligence technology, its application in the field of flower arrangement offers new possibilities for addressing this issue. To achieve a balance between aesthetic appeal and efficiency in intelligent flower arranging, this paper proposes an innovative intelligent flower arrangement assistance system. This system encompasses core functions such as intelligent recognition of floral materials, matching suitable options from a database, realizing flower arrangement designs, and constructing the database. Through a series of experiments, the system has demonstrated a good balance between aesthetic appeal and efficiency in flower arrangement design, with results indicating the effectiveness and feasibility of the proposed method.

The risk assessment of public-private partnership projects using artificial intelligence algorithms

Purpose: is to develop an innovative approach to risk management in public-private partnership (PPP) projects using advanced artificial intelligence technologies that allow creating the risk assessment model that takes into account non-linear relationships between various risk factors.Methods: in addition to traditional methods of scientific knowledge, interdisciplinary approaches of risk management and established practice of machine learning were used in the work. The methodological basis of the study was formed by works on the risk assessment and the application of AI algorithms in this area. The empirical basis of the study was the data of the official portal of ROSINFRA on public-private partnership projects.Results: the practice of applying AI algorithms to the task of assessing the risks of PPP-projects in Russia and abroad was studied. It is established that the most effective result is shown by the models based on Random-Forest-Classifier. However, the presented solutions do not take into account Russian economic realities. The authors have structured a database of implemented PPP-projects suitable for risk modeling. The model for assessing the risk of failure to achieve the objectives of Russian PPP-projects has been developed and its quality has been assessed. Recommendations on implementation of the model in the operational loop of PPP projects realization processes are offered.Conclusions and Relevance: the developed model allows, according to the general parameters of PPP project (region, authority, term of agreement, industry and scope of implementation, etc.) with the accuracy of 93% (according to the ROC\AUC metric), to assess the risk that the project will end incorrectly (due to a failed tender, refusal to launch, termination by court decision, cancellation/annulment of the tender). With the help of the model the executive authorities of the Russian Federation can build risk management for PPP projects management in the regions and thus contribute to their efficiency improvement. The article may also become useful for project management practitioners and appraisers.