ДОСЛІДЖЕННЯ ВПЛИВУ ВПРОВАДЖЕННЯ НЕЙРОННИХ МЕРЕЖ В ЕНЕРГЕТИЧНУ ГАЛУЗЬ УКРАЇНИ

The Artificial Intelligence (AI) industry has developed rapidly in recent years. AI has become an emerging industry in China. This paper first introduces the characteristics and connotations of AI and then compares the status of the AI industry in more developed cities at home and abroad with that in Tianjin. It finds that the AI industry in Tianjin faces some challenges, including insufficient innovation ability, few AI hardware and software products, lack of benchmarking enterprises, and poor integration with the manufacturing industry. To promote the development of the Tianjin AI industry, it is necessary to take some measures to guarantee the AI enterprise growth. Some measures about independent innovation, talents training, policy support, and AI hardware and software products research and development should be adopted.

The Symposium on the Application of Artificial Intelligence in Industry was held in conjunction with the Sixth Pacific Rim International Conference on Artificial Intelligence (PRICAI-2000), Melbourne Australia, August 2000. It was the second of the Symposium series aiming to highlight actual applications of Artificial Intelligence in industry and to share and compare experiences in doing so. The Symposium brought together researchers and developers of applied Artificial Intelligence systems. The symposium is the leading forum in the Pacific Rim for the presentation of innovative applications of AI in industry.

The present study presents an examination of fault detection and classification in manufacturing that is possible due to IoT and Artificial Intelligence in the Industry 4.0 structure. The study is based on IoT and compares three Artificial Intelligence algorithms such as Naive Bayes , Extreme Gradient Boosting , and k-Nearest Neighbors . The results displayed that in ten trials of the experiment achieving the accuracy from 0.82 to 0.89 . The interpretability tool allowed for the understanding of the models’ predictions. The consistent accuracy in both SHAP and LIME results, i.e. 0.80 and 0.89, suggest that both models performed effectively in model explanation. Thus, end users need to combine advanced machine learning algorithms with interpretable explanations to enhance fault detection and classification in manufacturing. This will consequently allow for better production processes and increased innovation in Industry 4.0 manufacturing.

Collaboration networks play a crucial role in fostering innovation within the artificial intelligence (AI) industry. This study investigates the evolutionary mechanisms underlying collaboration networks within China’s AI industry. Utilizing a stochastic actor-oriented model (SAOM) framework, we integrate endogenous structural effects, exogenous organizational attributes, and dyadic proximity characteristics to analyze longitudinal patent data from 2013 to 2024. The results reveal that key network dynamics, such as transitivity and preferential attachment, actively shape tie formation. Importantly, universities and research institutions play a more central role in driving network evolution than firms, and organizations with higher innovativeness attract more collaborative partners. While geographical, cultural, and institutional proximities facilitate collaboration, technological proximity has a noteworthy negative effect, underscoring the importance of complementary knowledge in AI innovation. Our findings enhance the understanding of the differences in the different dimensional factors that drive the evolution of AI industry collaboration networks and advance the research on network dynamics.

The rapid development of the artificial intelligence (AI) industry has led to a surging demand for professionals equipped with AI-related hardware development and debugging skills. However, hardware-oriented courses in universities often suffer from outdated content and insufficient practical alignment, making it difficult to meet industry needs. As a result, promoting curriculum reform has become an urgent requirement to bridge the gap between talent cultivation and industrial demands. Universities are therefore expected to adapt reforms to their specific contexts, clarify the primary directions of curriculum adjustment, and enhance the relevance of teaching by integrating both students’ learning needs and industry employment standards. Such efforts are crucial to fostering students’ comprehensive development. This paper focuses on the AI industry’s demand for hardware-oriented talent and explores pathways for curriculum reform. Strategies are proposed in three key areas: goal reconstruction, content optimization, and methodological innovation, with the aim of improving students’ hardware practice capabilities and AI adaptability, thereby providing a reference for the reform of hardware-oriented courses in higher education.

The adoption of Artificial Intelligence (AI) in the hotel industry in Nigeria has experienced dramatic growth over the years in different areas, including technology. The integration of technology has totally changed customer experience and operational efficiency. The application of AI in the hotel industry comes with benefits and challenges, with emphasis on both customer experience and operational efficiency. The roles of AI in the hotel industry includes enhanced customer service, operational efficiency, personal experience, revenue management, and security and safety. Applications of AI in the hotel industry includes chatbots and virtual assistant, smart rooms and personalized experience, and automated check-ins and check-outs. The importance of customer experience impacts the hotels reputation, profitability and competitive standing, and factors influencing it includes service quality, technology and convenience, cleanliness and comfort, personalization, and security and safety. Operational key indicators are cost management, staff productivity, guest satisfaction score, automation and technology utilization, supply chain and inventory management, and energy and resource efficiency. The challenges of operational efficiency in Nigerian hotels includes high operational cost, skills gap and work challenges, limited adoption of technology, inconsistent power supply, and supply chai disruption. The factors impacting adoption of AI are cost, infrastructure and technology readiness, regulatory and data privacy concerns, technical expertise of skills gaps, customer acceptance and trust. The benefits of AI integration include competitive advantage and innovation, data driven decision making, enhanced customer service and personalization, enhanced security and safety, improved revenue management, and increased operational efficiency. A qualitative methodology approach in form of systematic review has been used. Findings and results clearly shows there are only a handful of hotels using AI powered solutions, but there is high potential for improvement in the industry.

The food industry is one of the most rapidly evolving sectors, with new technological advancements constantly emerging. Artificial intelligence (AI) has emerged as a crucial component in this industry, revolutionizing various aspects of food production, from recipe generation to quality control. In recent years, AI has been integrated into the food industry in a big way, and its impact on the industry has been profound. An important benefit of AI in the food industry is its capacity to automate monotonous tasks, freeing up human workers for more complex responsibilities. AI also aids in recipe generation, enabling food companies to create innovative products that cater to consumer preferences. Moreover, AI plays a crucial role in maintaining quality control, guaranteeing that food products adhere to safety and quality requirements before they are delivered to consumers. To provide insights into the impact of AI in the food industry, this chapter will present case studies highlighting successful AI implementations.

In Industry 5.0 surroundings, artificial intelligence (AI) plays an essential role in enhancing human machine collaborations, prioritizing personalization, collaboration, sustainability, and ethical considerations. The research examines the connection between the use of AI, employee engagement (EE), and intention to stay (ITS). Data was gathered through a survey of 156 Information Technology employees from Tamil Nadu and analyzed using structural equation modeling (SEM) through JMP Pro (John's Macintosh Project) software to evaluate the relationship among the variables. The findings highlight that AI implementation has a positive correlation of 0.733 on EE and 0.648 on employees ITS. Al so, EE functions as a key mediating factor with an indirect effect of 0.105 and a total effect of 0.1832, strengthening the effect of AI on employees’ intention to remain with the organization. The coefficient of regression (R² value) shows 0.502 on employee ITS, ensuring the AI plays a significant role in employee growth and development. These results point out the importance of integrating AI into human resource practices, improving EE, and playing an important role in retaining talent. This study provides essential insights for human resource professionals and managers seeking to use AI effectively to develop a more engaged workforce, ultimately supporting organizational stability and growth.

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.

In this paper, the impact of Robotic Process Automation (RPA) and Artificial Intelligence (AI) on industrial operations in Industry 4.0 is extensively examined. In industrial automation process and to make the self optimizing system for industrial automation and smart manufacturing can be achieved by using tool like RPA with AI, machine learning, computer vision and integrated tool manufacturing. The main goal is to provide the RPA and AI in smart manufacturing process like predictive maintenance, supply chain, quality assurance and autonomous decision making. The main advantage of automation system is to reduce cost, increase precision and accuracy and overall productivity of the industry in many case studies and literature survey. Significantly the many difficulties like cyber security, hazardous environment to workforce and reintegration of work force also be considered. In addition to that the research is more on focus of exponential use of AI, latest trends and technologies, hyper automation and looking forward to the use of RPA and AI as an integral part how to contribute in the industrial manufacturing ecosystem.

This article emphasizes the importance of artificial intelligence in the tourism sector. Various technologies are being integrated to enhance customer services. The world is making significant efforts to strengthen industrial competitiveness using big data and artificial intelligence technologies, and tourism is facing increasing demands for new strategic solutions through the digital transformation of the existing tourism industry using cutting-edge technologies. Artificial intelligence quickly addresses customer requirements by providing timely information on critical factors such as hotels, airplanes, cathedrals, general facilities, and natural resources. This information can include interactive messages, virtual tours, interactive booking processes, language translations, and global positioning system technology. Artificial intelligence algorithms in the tourism industry help predict demand, revenue, and business trends. The tourism industry incorporates new technologies like virtual reality, chatbots, and language translation. Adopting artificial intelligence apps requires significant investments, including initial capital costs, maintenance, software updates, and staff training. Understanding the tourism and artificial intelligence industry will help build an artificial intelligence that will significantly transform the tourism industry in the future. The rise of artificial intelligence simplifies the process of making travel plans. Tourists used to decide their destinations and activities using pictures in a catalog or on the Internet. They have recently begun using artificial intelligence to find and tailor their specific requirements. Artificial intelligence is a valuable supplementary aspect to the future of the tourism industry. However, it cannot surpass the human touch, an essential determinant of experiential tourism.

Artificial Intelligence (AI)breaks all barriers of convention and disrupting the creative apacein areas like art, music, and fashion design in the Fashion Industry. This research is carried out by a sustainable supply chain that mainly focuses on one of the stages and studies the impact and significance of Artificial Intelligence in the fashion industry. Maybe through the Ecommerce market or the current scenario of digital and technological innovation. With the popular databases ?Scopus and WEB of Science? where the first research article is machine language, expert systems, decision support systems, optimization, image recognition, and computer vision, these are based on supply chain stages targeting the Design Process, targeting the Process designing in Fashion management, furthermore, the supply chain stages were classified based Business to Business and Business to Customer to get a broader outlook in the Industry. Now with AI in the fashion industry (2021), this has entrenched 44% of the fashion retailers who do not indulge in AI, are facing bankruptcy. As a global trend AI technologies are expected to reach 7.3 billion each year in the fashion Industry. Visual Merchandising had enabled technologies, augmented reality with virtual reality that is closing the gaps between online and in-store shopping experiences. Shoppers online can access any merchandise through digital media. Now with machine learning algorithms can accurately predict the inventory. This will help in reducing wastage and unsold inventory costs. Research gaps are still to be identified in the application of Artificial Intelligence techniques. The supply chain stages form business based on the gaps.

This study aims to address the unclear impact of artificial intelligence (AI) in the banking industry on banking institutions by exploring the mediating role of the non-performing loan ratio (NPL) and cost-income ratio (CIR) in AIs influence on bank performance, revealing the differentiated laws of AI efficiency transformation in Chinas banking industry, and providing a theoretical basis for the application of AI in Chinas banking industry. Research Tasks: 1.Variable Design: Based on the resource-based view, demonstrate the strategic value of technological human capital and establish a theoretical connection between personnel ratio and technology implementation. 2.Dual-Path Mechanism Test: Use a mediating effect model to verify two paths: "AI investment → optimize asset quality (reduce NPL) → improve performance (ROA)" and "AI investment → reduce operating costs (CIR) → improve performance". Robustness tests are conducted by replacing key indicators. Data primarily sourced from annual financial reports of 17 Chinese listed banks (2019-2024). Econometric modeling and statistical software STATA 18 were used for data analysis. AI improves bank performance by reducing NPL and CIR, with the risk control path contributing 41.6% to ROA and the cost-efficiency path contributing 47.8%. Large banks excel in risk management due to data advantages, while small banks thrive through the efficiency path. The study provides a theoretical foundation for digital transformation and calls for hierarchical supervision, algorithm transparency, and data governance to promote balanced industry development.

BackgroundThe use of artificial intelligence (AI) in the medical industry promises many benefits, so AI has been introduced to medical practice primarily in developed countries. In Japan, the government is preparing for the rollout of AI in the medical industry. This rollout depends on doctors and the public accepting the technology. Therefore it is necessary to consider acceptance among doctors and among the public. However, little is known about the acceptance of AI in medicine in Japan.ObjectiveThis study aimed to obtain detailed data on the acceptance of AI in medicine by comparing the acceptance among Japanese doctors with that among the Japanese public.MethodsWe conducted an online survey, and the responses of doctors and members of the public were compared. AI in medicine was defined as the use of AI to determine diagnosis and treatment without requiring a doctor. A questionnaire was prepared referred to as the unified theory of acceptance and use of technology, a model of behavior toward new technologies. It comprises 20 items, and each item was rated on a five-point scale. Using this questionnaire, we conducted an online survey in 2018 among 399 doctors and 600 members of the public. The sample-wide responses were analyzed, and then the responses of the doctors were compared with those of the public using t tests.ResultsRegarding the sample-wide responses (N=999), 653 (65.4%) of the respondents believed, in the future, AI in medicine would be necessary, whereas only 447 (44.7%) expressed an intention to use AI-driven medicine. Additionally, 730 (73.1%) believed that regulatory legislation was necessary, and 734 (73.5%) were concerned about where accountability lies. Regarding the comparison between doctors and the public, doctors (mean 3.43, SD 1.00) were more likely than members of the public (mean 3.23, SD 0.92) to express intention to use AI-driven medicine (P<.001), suggesting that optimism about AI in medicine is greater among doctors compared to the public.ConclusionsMany of the respondents were optimistic about the role of AI in medicine. However, when asked whether they would like to use AI-driven medicine, they tended to give a negative response. This trend suggests that concerns about the lack of regulation and about accountability hindered acceptance. Additionally, the results revealed that doctors were more enthusiastic than members of the public regarding AI-driven medicine. For the successful implementation of AI in medicine, it would be necessary to inform the public and doctors about the relevant laws and to take measures to remove their concerns about them.