Advances In Artificial Intelligence Technology Research Articles

The rising role of artificial intelligence in renewable energy development in China

Exploring the role of artificial intelligence (AI) in renewable energy (RE) development is pivotal for seizing technological opportunities and achieving climate objectives. This study uses wavelet analysis to examine the correlation between AI and RE in China. Our findings indicate a co-movement between AI and RE from 2014 to 2016 and a positive influence from AI to RE emerging from late 2018 to 2022. This suggests that AI acts as a facilitator for China's energy transition. Nevertheless, this effect is not constant; it becomes more pronounced with advancements in AI technology. These outcomes align with the techno-economic paradigms framework, implying that China can benefit from AI breakthroughs to accelerate its energy transition. Future policy efforts may focus on fostering collaboration among the government, businesses, and universities to promote AI and RE development.

Application of Machine Learning in Vegetable Classification and Recognition

The rapid development of artificial intelligence plays an important role in People's Daily life today. Vegetable classification is closely related to life. With the continuous advancement of artificial intelligence technology, similar applications in the agricultural and retail industries are expected to play a more significant role. This paper aims to envision a vegetable identification system based on a convolutional neural network to provide intuitive and accurate vegetable classification information for supermarket managers and customers. This study mainly uses the convolutional neural network model as the main research model to carry out the design of a vegetable classification system. In this paper, the usefulness and disadvantages of the CNN model are summarized, how to use the model for deep learning and big data is learned, and the problems that may be encountered after the model is run are also predicted. Furthermore, the future development direction of the project is predicted too. As technology continues to evolve, it is expected that more innovative solutions will emerge, delivering enhanced benefits to society.

Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Leveraging sophisticated AI algorithms, the study focuses on scrutinizingsubtle periodic patterns and uncovering relationships among the collected datasets. Through thiscomprehensive analysis, the research endeavors to pinpoint crime hotspots, detect fluctuations infrequency, and identify underlying causes of criminal activities. Furthermore, the research evaluates theefficacy of the AI model in generating productive insights and providing the most accurate predictionsof future criminal trends. These predictive insights are poised to revolutionize the strategies of lawenforcement agencies, enabling them to adopt proactive and targeted approaches. Emphasizing ethicalconsiderations, this research ensures the continued feasibility of AI use while safeguarding individuals'constitutional rights, including privacy. The anticipated outcomes of this research are anticipated tofurnish actionable intelligence for law enforcement, policymakers, and urban planners, aiding in theidentification of effective crime prevention strategies. By harnessing the potential of AI, this researchcontributes to the promotion of proactive strategies and data-driven models in crime analysis andprediction, offering a promising avenue for enhancing public security in Los Angeles and othermetropolitan areas.

Research on the impact of the digital economy on the level of industrial structure: An empirical study of 280 cities in China.

As the digital revolution deepens, the digital economy (DE) is reshaping the global industrial structure (IS). This paper utilizes data from 280 Chinese cities between 2007 and 2020 to conduct an in-depth analysis of how DE propels the upgrading of IS and explores the role of human capital (HC) in this process. The research indicates that DE significantly fosters the optimization of IS. Additionally, it was discovered that the growth of HC plays a pivotal mediating role in this evolution, complementing existing research on the relationship between higher education and industrial upgrading. By adopting spatial econometric methods, this study unveils the spillover effects of DE in geographical space, identifying a positive influence on the industrial upgrading of surrounding regions. The paper also confirms the nonlinear characteristics of DE's impact on IS upgrading, which manifests as a pronounced inverted U-shaped trend, marking a novel discovery. Further findings suggest that in regions with more advanced artificial intelligence technologies, the impact of DE on industrial optimization is more significant, highlighting the role of regional disparities in the digital transformation. In conclusion, the paper proposes policy recommendations based on the research findings to facilitate the development of DE and the elevation of IS levels, thereby promoting high-quality economic growth.

Artificial replication cohort: Leveraging AI-fabricated data for genetic studies.

Recent advancements in artificial intelligence (AI) present both opportunities and challenges within the scientific community. This study explores the capability of AI to replicate findings from genetic research, focusing on findings from prior work. Using an AI model without exposing any raw data, we created a dataset that closely mirrors the results of our original study, illustrating the ease of fabricating datasets with authenticity. This approach highlights the risks associated with AI misuse in scientific research. The study emphasizes the critical importance of maintaining the integrity of scientific inquiry in an era increasingly influenced by advanced AI technologies.

Future of Artificial Intelligence & Business Analytics

Artificial intelligence (AI) and business analytics are two rapidly developing technologies transforming how businesses operate. AI is the branch of computer science that focuses on developing intelligent machines that can perform tasks that typically require human intelligence, while business analytics refers to the process of using data to make informed business decisions. The purpose of this paper is to explore the future of AI and business analytics and how these technologies will shape the business landscape in the coming years. The increasing availability and use of artificial intelligence (AI) technologies in business analytics have the potential to revolutionize the way organizations operate. This research paper explores the future of AI and business analytics, examining how advances in AI technology are shaping the field and what changes we can expect to see in the coming years. The paper examines the benefits and challenges of using AI in business analytics, as well as the ethical considerations that arise with the increased use of this technology. Additionally, the paper explores how organizations can best leverage AI in their business analytics strategies to maximize the potential benefits while minimizing the risks. Through a review of existing literature and analysis of emerging trends, this paper provides valuable insights for businesses and policymakers seeking to understand the evolving landscape of AI and business analytics. Looking ahead, the future of AI and business analytics is likely to be shaped by a number of key trends. These include the increasing use of machine learning and deep learning algorithms, the integration of AI with other emerging technologies such as blockchain and the Internet of Things, and the growing importance of explainable AI that can provide transparency and accountability in decision-making.

Challenges in Artificial Intelligence Development in Higher Education in China, India, and Indonesia: International Students’ Perspectives

This research explores the challenges of developing artificial intelligence (AI) at universities in China, India, and Indonesia for teacher education students. A qualitative research method was employed, with data collected through in-depth focus group discussions with 12 doctoral students from the 3 countries in equal proportions. The sample selection was based on the diversity of the participants’ relevant backgrounds, experiences, and understandings. The data collected were analyzed using a thematic approach involving the identification, mapping, and interpretation of themes. The research findings indicate variations in the main challenges in developing AI to improve the quality of teacher education in each country. In Indonesia, infrastructure and Internet access are the main constraints limiting the application of AI technology. Meanwhile, in India, the main concern relates to the lack of human resources skilled in the field of AI, prompting the need for relevant skills development among educators. Conversely, in China, the problem concerns striking a balance between utilizing advanced AI technologies, safeguarding privacy, and developing the capacity to accommodate rapid advances in technology-based education. The findings of this study provide valuable strategic insights, enabling the design of appropriate strategies in each country. The implications of the findings can assist the relevant parties in overcoming specific barriers in the context of each country, supporting innovative developments in technology-based teacher education.

Visualization Analysis of Artificial Intelligence Literature in Forensic Research.

To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database, to explore research hotspots and developmental trends. A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the literature measuring tool CiteSpace. The authors, institution, country (region), title, journal, keywords, cited references and other information of relevant literatures were analyzed. A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries (regions) were identified, with the number of articles published showing an increasing trend year by year. Among them, the United States had the highest number of publications and China ranked the second. Academy of Forensic Science had the highest number of publications among the institutions. Forensic Science International, Journal of Forensic Sciences, International Journal of Legal Medicine ranked high in publication and citation frequency. Through the analysis of keywords, it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technology for sex and age estimation, cause of death analysis, postmortem interval estimation, individual identification and so on. It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research. Exploring the combination of advanced artificial intelligence technologies with forensic research will be a hotspot and direction for future research.

Artificially disinformed and radicalised: How AI produced disinformation could encourage radicalisation

Key digested messageThe rapid advancements in artificial intelligence technologies have enhanced the ability for individuals who want to generate a substantial amount of seemingly genuine discussions, images and/or videos that are tailored to promote specific narratives. Unfortunately, this advancement has also provided a valuable tool for actors who seek to promote potentially harmful ideologies and share disinformation to large online audiences. By leveraging AI, these individuals can significantly enhance their recruitment efforts and bolster their perceived credibility, by producing seemingly legitimate but artificially fabricated evidence that supports their proposed narrative. This pressing issue is discussed in terms of its potentially negative consequences on the encouragement of radicalisation in users exposed to this artificially produced disinformation. Not only does it pose a risk to the integrity of people’s perception of truth, but it also has the potential to exacerbate the likelihood of radicalisation occurring.

Generative artificial intelligence and building design: early photorealistic render visualization of façades using local identity-trained models

Abstract This paper elucidates an approach that utilizes generative artificial intelligence (AI) to develop alternative architectural design options based on local identity. The advancement of AI technologies has increasingly piqued the interest of the architecture, engineering, construction, and facility management industry. Notably, the topic of “visualization” has gained prominence as a means for enhancing communication related to a project, especially in the early phases of design. This study aims to enhance the ease of obtaining design images during initial phases of design by drawing from multiple texts and images. It develops an additional training model to generate various design alternatives that resonate with the identity of the locale through the application of generative AI to the façade design of buildings. The identity of a locality in cities and regions is the capacity for the cities and regions to be identified and recognized as a specific area. Among the various visual elements of urban and regional landscapes, the front face of buildings may play a significant role in people’s aesthetic perception and overall impression of the local environment. The research proposes an approach that transcends the conventional employment of three-dimensional modeling and rendering tools by readily deriving design alternatives that consider this local identity in commercial building remodeling. This approach allows for financial and temporal efficiency in the design communication phase of the initial architectural design process. The implementation and utilization of the proposed approach’s supplementary training model in this study proceeds as follows: (i) image data are collected from the target area using open-source street-view resources and preprocessed for conversion to a trainable format; (ii) textual data are prepared for pairing with preprocessed image data; (iii) additional training and outcome testing are performed using varied text prompts and images; and (iv) the ability to generate building façade images that reflect the identity of the collected locale by using the additional trained model is determined, as evidenced by the findings of the proposed application method study. This enables the generation of design alternatives that integrate regional styles and diverse design requirements for buildings. The training model implemented in this study can be leveraged through weight adjustments and prompt engineering to generate a greater number of design reference images, among other diverse approaches.

Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education.

The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on "informed consent" in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-and extending them by integrating 3 public health ethics principles-efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.

The investigation of artificial intelligence-based applications in music education

The field of music education has garnered significant attention due to the burgeoning advancements in artificial intelligence technology. Currently, there are many works on applying Artificial Intelligence (AI) technology to the field of music education, and this review aims to comprehensively summarize the application of AI methods in multiple subfields of music education. In this paper, according to the focus of AI technology, the areas of music education to which it has been applied are divided into four modules, namely personalized learning, automatic assessment, composition assistance and interactive teaching. Firstly, representative AI methods applied to each of these four subfields are introduced, including but not limited to: the principle of the AI method, the improvement brought by the application. The text then describes the current status of the application of these methods in their domains, discusses the limitations of the application, and makes brief conjectures on how to break through these limitations. The research in this review paper is important for the development of AI technology applied to the field of music education. At the same time, this review also provides directions and suggestions for future research to promote the further development of AI in the field of music education.

Research on unsupervised image retrieval methods based on contrastive learning

In the convergence of fashion and artificial intelligence (AI), significant strides have been made in areas such as clothing recognition, retrieval, and classification, enabled by advanced AI technologies and expansive annotated datasets. As the AI in Fashion market continues to surge, the future of the fashion industry promises to be redefined by intelligent, efficient, and more accessible solutions. Image retrieval, one of the important parts in AI, has experienced remarkable growth, empowered by advanced algorithms and vast annotated datasets, making it a crucial component in various domains such as digital libraries, online marketing. Therefore, this report mainly provides an extensive review of image retrieval methods and the emerging paradigm of contrastive learning, underscoring their relevance and applications in the realm of artificial intelligence. This paper primarily reviews the technologies in the amalgamation of the image retrieval field and contrastive learning. It elucidates the history and progression of image retrieval, offers a methodical analysis of the two primary approachestext-based image retrieval and content-based image retrievaland examines how contrastive learning is employed in image retrieval systems.

Untouchable them: the effect of chatbot gender on angry customers

PurposeThis study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.Design/methodology/approachThis research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.FindingsThis research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.Originality/valueThe rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.

Comparison of regression, ANN, ANFIS, and ChatGPT prediction of turning cutting force

In this study, training and prediction performance on turning data were investigated with ChatGPT, which is a popular AI platform nowadays. In this context, the resultant cutting forces obtained as a result of different turning simulations with FEM, training and estimation were made using regression, ANN, ANFIS methods. Using the same data, training and predictions were made with ChatGPT-3 with different prediction algorithms. As a result, the lowest average error rates in the predictions made with the training data; 2E-6% for ANFIS in prediction methods and 0.19% for ANN1 conversation in GPT-3 were obtained. The lowest average error rates in the predictions made with the test data; 5.41% for regression using logarithmic Box–Cox transformation in prediction methods, and 22.66% for ANN1 conversation in GPT-3 were achieved. The highest prediction performance in GPT-3 conversations was observed when GPT-3 was asked to make predictions with ANN algorithm on both training and test data. As a result, GPT-3 has not yet generated acceptable solutions for machining problems due to its low performance in predicting test data. However, due to the fast advancement of artificial intelligence technologies, it is obvious that solutions to this and more engineering problems will be generated in near future. Highlights Prediction with ChatGPT-3 Prediction performance of artificial intelligence Cutting force prediction of turning operation

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

This comprehensive study explores the integration and effectiveness of Artificial Intelligence (AI) in Identity and Access Management (IAM) within cloud environments. It primarily focuses on how AI can enhance user authentication, authorization, and access control, addressing the challenges and possibilities in cloud computing. The study adopts a mixed-methods approach, employing both quantitative and qualitative analyses. A survey involving 582 cybersecurity experts provides insights into the current state and potential of AI in IAM, while multiple regression analysis examines the impact of various factors on system effectiveness. Four hypotheses are explored: the impact of hardware and software configurations on system accuracy (H1), the influence of computational environments on reliability (H2), the role of demographic factors in user acceptance (H3), and the effect of technological enhancements on system performance and acceptance (H4). Findings indicate significant correlations between these factors and the effectiveness of AI in IAM. Notably, hardware configurations and security concerns influence system accuracy; computational environment variations affect system reliability; demographic factors impact user acceptance; and enhancements such as user feedback, advancements in AI technology, continuous learning algorithms, and system transparency improve performance and acceptance. These insights underscore the need for advanced hardware, standardized software, user-centric design, and continuous improvement in AI technologies for effective IAM in cloud environments. The study provides actionable recommendations for cloud service providers and developers, emphasizing the importance of involving users in development processes, ensuring transparency, and adopting adaptive algorithms. Future research directions include longitudinal studies on the impact of technological advancements and exploring demographic-specific responses to AI-integrated IAM solutions.

Intelligent Management Experience on Efficient Electric Power System

Electric power system is one of the most critical and strategic infrastructures of industrial societies. Nowadays, it is necessary the modernization and automation of the electric power grid to increase energy efficiency, reduce emissions, and transit to renewable energy. Power utilities face the challenge of using information and communication networks more effectively to manage the demand, generation, transmission, and distribution of their commodity services. Communication network constitutes the core of the electric system automation applications, the design of a cost-effective, and reliable network architecture is crucial. To resolve this difficulty in this work we study the integration of advanced artificial intelligence technology into existing network management system. This work focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. We have normalized the knowledge management base necessary to manage the current resources in the telecommunication networks. Intelligent agents learn the normal behaviour of each measurement variable and combine the intelligent knowledge for the management of the network resources. We present an analysis of corporate network management requirements and technologies, together with our implementation experience with the development of an integrated management system for a company network.

LSTENet: Cement productivity prediction using a self-attention spatio-temporal variational autoencoder

In the advent of the Industry 4.0 paradigm, intelligent manufacturing has gained prominence with the integration of advanced Artificial Intelligence (AI) technologies aimed at augmenting production efficiency and mitigating operational costs. Particularly, in process industries such as cement manufacturing, the efficacy of production is intrinsically tied to the manifold process elements involved. The underlying complexity is further accentuated by the fact that the production chain is typified by a multi-variate, time-variant, and non-linear system. Such characteristics exacerbate the challenges associated with predicting the quality of final goods, especially considering the intricate physical–chemical reactions that persist even in steady states, which will reflect to the data representation. Additionally, prevailing prediction systems are impeded by their inability to assimilate ancillary spatiotemporal information, rendering their predictive accuracies suboptimal for practical production demands. Addressing these issues, this study introduces an innovative approach for online prediction of final production quality by employing a Spatiotemporal Neural Network. The core attributes of this technique encompass the extraction of latent spatial information and the effective handling of extensive temporal sequences within two main components by a variational autoencoder framework. In the variational autoencoder, it is achieved through the strategic application of learnable convolutional neural layers, supplemented by gated recurrent layers with self-attention. In a novel approach, these two fundamental components are seamlessly integrated within a single end-to-end optimization framework. Empirical evidence derived from a real-world dataset serves to substantiate the superior performance of our proposed methodology in contrast to extant machine learning algorithms. The findings are indicative of the potential that deep learning architectures harbor in addressing pragmatic challenges within industrial contexts.

GAS SOURCE LOCALIZATION THROUGH DEEP LEARNING METHOD BASED ON GAS DISTRIBUTION MAP DATABASE

The incident of harmful gas leakage can cause severe damage to the environment and several casualties to human beings while the gas localization system plays a major role in mitigating those causalities. With the advances in artificial intelligence technology, deep learning is able to enhance the accuracy of the gas localization system to locate the gas source. This paper proposes a gas localization system that utilizes three different deep learning models namely DNN, 1DCNN, and 2DCNN to locate the gas source within the gas map. The proposed method involves generating the gas distribution map through the large gas sensor array platform in real-world indoor scenarios. Those models are then trained using the collected database which allows for accurate prediction of the gas source location. The performance of each proposed deep learning model was compared to find the best model demonstrating the highest effectiveness in identifying gas leaks. The study has shown that the 1DCNN has the highest effectiveness in predicting the gas source in the range between 0.0 m to 0.3 m with 90.3% compared to the DNN and 2DCNN models.