Intelligent Industry Research Articles

The Application of Artificial Intelligence and Big Data in the Food Industry.

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

Transfer learning approach to reduce similar IOT sensor data for industrial applications

The Industrial Internet of Things (IIoT) is a developing technology that has the potential to advance industrial intelligence, achieve targeted effectiveness, and lower manufacturing costs. Data fusion, a procedure that enables the collection, analysis, and processing of the enormous amounts of Internet of Things (IoT) information created by industrialized machinery and applications, is essential to IIoT's advancement and improvement of industrial products and applications. A real-time, efficient, and private information image fusion mechanism is required by the IIoT. Moreover, the current efforts call for such training of multiple systems in data analysis that also fall short of meeting real-time IIoT needs. The ineffectiveness of internal defenses and the challenge of finding the right balance between system stability and data confidentiality, however, undermine the efficiency and privacy preservation of a data fusion process. In this paper, we propose a Transfer learning-based Secure Data Fusion (TSDF) for the IIoT to address the aforementioned issues. In addition to the proposed network, we developed and deployed a defect estimation method that makes use of sensor data fusion to evaluate the manufacturing plant's operational state. The efficiency outcomes of evaluating the conceptual model are included in the original study, including a comparison to a conventional deep neural network structure. The study's findings demonstrate that TSDF may provide privacy, preserving information fusion in diverse IIoT application environments while reaching higher system capacity and low latency.

Towards Legal Regulations of Generative AI in the Creative Industry

Objective: this article aims to answer the following questions: 1. Can generative artificial intelligence be a subject of copyright law? 2. What risks the unregulated use of generative artificial intelligence systems can cause? 3. What legal gaps should be filled in to minimize such risks?Methods: comparative legal analysis, sociological method, concrete sociological method, quantitative data analysis, qualitative data analysis, statistical analysis, case study, induction, deduction.Results: the authors identified several risks of the unregulated usage of generative artificial intelligence in the creative industry, among which are: violation of copyright and labor law, violation of consumers rights and the rise of public distrust in government. They suggest that a prompt development of new legal norms can minimize these risks. In conclusion, the article constants that states have already begun to realize that the negative impact of generative artificial intelligence on the creative industry must not be ignored, hence the development of similar legal regulations in states with completely different regimes.Scientific novelty: the article provides a comprehensive study of the impact of generative artificial intelligence on the creative industry from two perspectives: the perspective of law and the perspective of the industry. The empirical basis of it consists of two international surveys and an expert opinion of a representative of the industry. This approach allowed the authors to improve the objectivity of their research and to obtain results that can be used for finding a practical solution for the identified risks. The problem of the ongoing development and popularization of generative artificial intelligence systems goes beyond the question “who is the author?” therefore, it needs to be solved by introduction of other than the already existing mechanisms and regulations - this point of view is supported not only by the results of the surveys but also by the analysis of current lawsuits against developers of generative artificial intelligence systems.Practical significance: the obtained results can be used to fasten the development of universal legal rules, regulations, instruments and standards, the current lack of which poses a threat not only to human rights, but also to several sectors within the creative industry and beyond.

Traffic Sign Detection in Complex Scenarios based on YOLOV7

In recent years, thanks to the development of the automobile industry and the advancement of industrial intelligence, driving assistance systems have been developing rapidly under the requirements of national policies and the demands of the consumer market. Traffic sign detection module, as an indispensable part of the driving assistance field, is in urgent need of new target detection algorithms in this field to solve the problems of poor detection accuracy, low computing speed and poor adaptability of the existing algorithms to the vehicle hardware. To solve the application problem, in this paper, we propose a traffic target detection algorithm based on YOLOV7. The model is trained using the CCTSDB dataset and tested in complex scenarios such as different lighting conditions and weather conditions. The mAP of YOLOV7 is stable at over 82.46% and the mAP value of YOLOV7-tiny is stable at over 78.57%, demonstrating the effectiveness of our method that it has the ability to detect and recognize our traffic signs in practical scenarios. In addition, this paper conducts a comparative study on the commonalities and differences of the above two models and draws the corresponding conclusions. In summary, YOLOV7 series can be effectively used in traffic sign detection work.

RECOGNITION OF OBJECT PROPERTIES BASED ON PASSIVE TRIANGULATION AND 2D CAMERA SYSTEM

Every advanced knowledge about the process, object, or workplace is fundamental. It concerns current trends (digitalization, automation, intelligent industry) and the need to fill the missing technological gaps. Their goal is to increase quality and competitiveness. Therefore, this article aims to present a framework of the systematic approach as an affordable way to obtain relevant information about the object in the field of view of the captured scene. In addition, by using known triangulation methods, the article wants to build a relevant knowledge base that can be used as a suitable basis for solving similarly focused application tasks. Regardless, concerning the sensitive integration of image recognition and a methodological point of view, the article wants to summarize and verify the knowledge of epipolar geometry on a practical example of a selected object.

Industrial intelligent production and firm ESG Performance

ABSTRACT This study confirms the positive impact of industrial intelligence on corporate ESG performance with the data of Chinese manufacturing listed companies from 2010 to 2019. This positive effect is manifested through green technology innovation and internal controls, and it is particularly pronounced in heavy-polluting industries, high-tech sectors, and enterprises with lower productivity levels.

Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review

This bibliometric review examines the research state of artificial intelligence (AI) and machine learning (ML) applications in the Banking, Financial Services, and Insurance (BFSI) sector. The study focuses on Scopus-indexed articles to identify key research clusters. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 39,498 articles were screened, resulting in 1045 articles meeting the inclusion criteria. N-gram analysis identified 177 unique terms in the article titles and abstracts. Co-occurrence analysis revealed nine distinct clusters covering fintech, risk management, anti-money laundering, and actuarial science, among others. These clusters offer a comprehensive overview of the multifaceted research landscape. The identified clusters can guide future research and inform study design. Policymakers, researchers, and practitioners in the BFSI sector can benefit from the study’s findings, which identify research gaps and opportunities. This study contributes to the growing literature on bibliometrics, providing insights into AI and ML applications in the BFSI sector. The findings have practical implications, advancing our understanding of AI and ML’s role in benefiting academia and industry.

Artificial intelligence in the construction sector

The application of artificial intelligence in the construction industry is divided into two main areas: 1. Using new technologies such as big data, cloud computing and the Internet of Things to optimise building design and management and provide users with a better living environment and service experience. 2. Using artificial intelligence technology to carry out intelligent building operation and maintenance, reduce the number of construction site operators and improve the efficiency of the construction site. Based on the analysis of the application scenarios in these two aspects, the application of artificial intelligence in the field of construction has a broad prospect. Especially with the continuous development and improvement of the Internet of Things and big data technology, artificial intelligence can better enhance the quality of intelligent buildings.

The Employment Impact of Digital Transformation in the Logistics Industry in the Age of Artificial Intelligence --Take Shunfeng as an Example

This paper presents the application of artificial intelligence in the logistics industry and its impact on employment as well as countermeasures. Shunfeng Logistics has enhanced efficiency and precision with intelligent distribution and warehousing applications. It is important for logistics enterprises and practitioners to pay attention, adapt, and acquire the necessary skills and knowledge to meet the demands of the AI era. The article suggests countermeasures like acquiring new skills, changing jobs, or transforming, along with government support. AI has significantly influenced the logistics industry by enhancing efficiency and accuracy and generating new employment opportunities resulting in some job transformations and upgrades. In the future, the logistics industry will require more highly skilled and specialised professionals and will strive for enhanced intelligence and efficiency. Professionals in traditional logistics practices will need to continuously learn and improve their skills and expertise to keep pace with the evolving challenges and changes in the industry.

The Transformative Role of Artificial Intelligence in the Event Management Industry

This opinion paper explores the burgeoning influence of Artificial Intelligence (AI) within the event management industry. AI has emerged as a powerful tool, offering innovative solutions for event planning, execution, and attendee engagement. This paper provides a comprehensive overview of AI's role in event management, discussing its applications, benefits, and potential drawbacks based on contemporary researches and publications. By analysing six (6) current trends and four (4) critical reception of AI in the event industry, we aim to shed light on its transformative potential and its implications for the future of event management. At the end of the paper, the researchers provide recommendations on how to navigate the obstacles in implementing AI technology in event management. This paper is critical in its role to ignite discussions regarding AI implementation in event industry as there are currently very few academic writings on this topic.

Exploring the Ethical Dynamics of the Use of Artificial Intelligence (AI) in Hiring in Healthcare Organizations

Abstract Artificial Intelligence (AI)-driven resume screening holds immense potential for improving the hiring process. However, organizations must actively address the ethical and bias challenges that may arise. This paper explores a consultant-based intervention research approach in a real-world healthcare organization. Integrating Artificial Intelligence (AI) in the healthcare industry has brought about transformative advancements in patient care, diagnostics, and administrative processes. Healthcare companies have recently embraced AI technologies to revolutionize their resume screening and hiring procedures. This paper explores the various ways in which healthcare organizations utilize AI in their talent acquisition endeavors. From optimizing candidate sourcing and screening to identifying top talent and enhancing diversity, AI-driven systems have demonstrated their efficacy in streamlining the hiring process. However, it is essential to address potential ethical and bias concerns and ensure that AI complements the human element in making informed and inclusive hiring decisions. By understanding the multifaceted applications of AI in healthcare companies’ hiring practices, this paper emphasizes the pivotal role that AI plays in shaping the future of healthcare talent acquisition.

The rise of AI copilots: How LLMs turn data into actions, advance the business intelligence industry and make data accessible company-wide

AI-powered collaboration is quickly advancing due to a convergence of technologies like large language models and language model programming. These developments have spawned the rise of AI (artificial intelligence) copilots, which are changing the way marketing analysts make decisions and boost productivity. This paper explores the capabilities of AI copilots and delves into the challenges, ethical considerations and data privacy issues that come with their adoption. It discusses real-world applications and future trends in the AI copilot landscape. The paper also emphasises the importance of data integration and personalisation in marketing strategies and offers insights into training and skill development for effective collaboration with AI copilots. This comprehensive analysis aims to equip marketing analytics professionals for the future of AI-powered collaboration.

AI Ethics and Bias: Exploratory study on the ethical considerations and potential biases in ai and data-driven decision-making in banking, with a focus on fairness, transparency, and accountability

The integration of Artificial Intelligence (AI) and data-driven decision-making in the banking industry has ushered in unprecedented opportunities for efficiency, risk assessment, and customer service. However, this rapid adoption of AI technology comes with its own set of ethical considerations and potential biases. This research paper delves into the intricate landscape of AI ethics and bias within the banking sector, with a central emphasis on fairness, transparency, and accountability. In sum, this paper contributes to the ongoing discourse on AI ethics and bias by providing valuable insights into the ethical considerations and potential biases inherent in AI and data-driven decision-making within the banking sector. It underscores the necessity of fairness, transparency, and accountability as guiding principles in the responsible integration of AI technology in banking, while also presenting future research directions for this evolving field.

The role of industrial intelligence in peaking carbon emissions in China

As industrial intelligence empowers economic advancement and technological progress, its capacity to enable decarbonization during China's low-carbon transition remains uncertain. Existing literature concentrates on the environmental impact of information and communication technologies or uses single-dimension measuring indicators, failing to capture the comprehensiveness of industrial intelligence. Based on the concept of full-cycle management, this study constructs a comprehensive index to evaluate industrial intelligence from intelligent inputs, intelligent production capacity, and intelligent outputs. Using 2006–2020 Chinese provincial panel data and fixed-effects models, we analyze the effects, mechanisms and thresholds of industrial intelligence on carbon emissions. The results show that: (1) The level of industrial intelligence plays a role in reducing carbon emissions after a certain threshold. (2) Industrial intelligence reduces carbon emissions by advancing green technology and optimizing production structure. (3) Industrial intelligence mitigates carbon emissions in eastern and north China but not western China. According to current growth rates of industrial intelligence, nine Chinese provinces may achieve a carbon peak through industry intelligence by 2030. (4) Moreover, industrial intelligence's carbon emission reduction effect is significant in labor- and technology-intensive industries but does not work in capital-intensive industries. These revelations have meaningful implications for orienting industrial intelligence development and decarbonization initiatives.

Probabilistic stationary subspace regression model for soft sensing of nonstationary industrial processes

AbstractBenefitting from the development of industrial intelligence, data‐driven soft sensors have been widely applied in industrial processes in recent years. Driven by fluctuations in raw material quality, varying loads, and uncertain disturbances, industrial processes are often characterized by nonstationary properties. However, the nonstationary characteristics of the process are not considered in traditional data‐driven methods, leading to the poor prediction performance of soft sensors. Meanwhile, numerous nonstationary modelling methods have been proposed for process monitoring, with probabilistic stationary subspace analysis (PSSA) showing significant application potential. Unfortunately, PSSA has not yet been used to develop effective soft sensors for nonstationary processes. Therefore, the PSSA model is extended to the regression form (PSSR), and a corresponding soft sensing model is developed in this paper. Unlike previous approaches, the proposed PSSR can offer a suitable description of nonstationary processes and fully capture the mathematical correlation between input and output variables. Finally, the prediction performance of PSSR is verified through case studies on a numerical example and penicillin fermentation process. The experimental results indicate that PSSR can provide a superior solution for soft sensing modelling of nonstationary industrial processes.

Real-time monitoring and measurement of energy characteristics in sustainable machining of titanium alloys

The development of cutting-edge monitoring technologies such as embedded devices and sensors has become necessary to ensure an industrial intelligence in modern manufacturing by recording machine, process, tool, and energy consumption conditions. Similarly, machine learning based real time systems are popular in the context of Industry 4.0 and are generally used for predicting energy needs and improving energy utilization efficiency and performance. In addition, sustainable and energy-efficient machining technologies that can reduce energy consumption and associated negative environmental effects have been the latest topic of much study in recent years. Concerning this regard, the present work firstly deals with the real time monitoring and measurement of energy characteristics while machining titanium alloys under dry, minimum quantity lubrication (MQL), liquid nitrogen (LN2) and hybrid (MQL + LN2) conditions. The energy characteristics at different stages of machine tools were monitored with the help of a high end energy analyser. Then, the energy signals from each stage of machining operation were predicted and classified with the help of different machine learning (ML) models. The experimental results showed that MQL, LN2, and hybrid conditions decreased the total energy consumption by averagely 2.6 %, 17.0 %, and 16.3 %, respectively, compared to dry condition. The ML results demonstrated that the accuracy of the random forest (RF) approach obtained higher efficacy with 96.3 % in all four conditions. In addition, it has been noticed that the hybrid cooling conditions are helpful in reducing the energy consumption values at different stages.

Artificial Intelligence in Pharmacy

Artificial intelligence research tried and removed many of the different approaches since its founding, including simulating the brain, modeling human problem solving, learning, formal logic, large databases of knowledge and imitating animal behavior. Artificial intelligence in pharmaceutical industry shows no sign of slowing down. According to recent research, about 50% of global healthcare companies plan to implement artificial intelligence strategies broadly adopt the technology by 2025.

An empirical study on the response of the energy market to the shock from the artificial intelligence industry

This paper makes the first attempt to look at the response of the energy market to the shock from the AI industry and the role of AI in the energy market risk spillover network. The innovation of the paper lies in adopting a quantile-based and time-varying framework in the empirical analysis. This enables us to reveal the cross-quantile and lead-lag correlations between AI and the energy market. We find that an extreme negative shock from AI tends to correspond to an extreme downward movement in crude oil, gasoline, gas oil, and clean energy markets and the shock response dissipates after 66 days. Meanwhile, the cross-quantile correlations are subject to structural changes once the extreme events arrive. As for the total spillover effect of the energy market, it increases by 5.34 % after AI is considered as a source of uncertainty in the risk spillover network dominated by the crude oil market. Thus, the shock from AI challenges the stability of the energy market. A close relationship between the AI industry and the clean energy market is also identified. We contribute to studying the relationship between the AI industry and the energy market from the perspective of the risk connectedness between the two fields.

How Do Heterogeneous Networks Affect a Firm's Innovation Performance? A Research Analysis Based on Clustering and Classification.

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry-University-Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers.

Employees’ service recovery performance: the role of functional manager’s emotional intelligence in the hospitality industry

Emotional intelligence has taken a firm hold in organizations. The emotional intelligence of managers is essential for building a motivated workforce. This study investigates the relationship between functional managers’ emotional intelligence and employees’ service recovery performance in the context of the hospitality industry. In this study, functional managers’ emotional intelligence includes four dimensions: self-emotion appraisal, other-emotion appraisal, use of emotion, and regulation of emotion. A mixed method was employed to explore the influence of functional managers’ emotional intelligence on employees’ service recovery performance. The data were collected from 238 functional managers at 33 four- and five-star hotels in five provinces and cities in Vietnam. Factor analysis, correlation analysis, and regression analysis were used to analyze the data. The results show that functional hotel managers’ emotional intelligence has a positive and significant impact on employees’ service recovery performance, with others’ emotion appraisal having the strongest impact, followed by emotion regulation, use of emotion, and self-emotion appraisal. These findings contribute to the literature and deepen the understanding of service recovery. This study also provides managers with useful advice on managing customer service as a competitive advantage.