Intelligent Technology Research Articles

Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model

For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.

Collective Wisdom in Constitutional, Informational and Digital Dimensions: on the Concept and Nature of Popular Deliberative Constitutionalism for Public Law

The article discusses the scientific and intellectual origins of collective wisdom in the constitutional, information and digital dimensions, reveals the doctrinal approaches and normative foundations of popular deliberative constitutionalism, the development of an inclusive constitutional paradigm and its significance for the use of procedures and technologies for citizen participation, expert opinion and civil society institutions in the constitutional development, in social transformations, in constitutional changes brought to life by the needs of society and the state. Popular deliberative constitutionalism is identified in the article as a public legal meta-concept that explains the process and need for constitutional involvement of citizens. The author concludes that popular deliberative constitutionalism allows us to better understand the problems of implementing the principle of popular sovereignty in modern democratic states, create an optimal model and consider the forms of exercising the constituent power of the people when implementing constitutional changes. In the article, the author notes that the future of democratic constitutionalism is associated with the creation and use of collective intelligence technologies in the public legal sphere, which, in the context of the development of an algorithmic and information society, combine classical procedures for discussing constitutional issues and the use of aggregated preferences. The practical application of the achievements of natural and artificial intelligence is intended to discuss and resolve issues of public legal significance, is based on co-evolutionary development and leads to the emergence of mini-publics, civil assemblies, and constitutional crowdsourcing.

The concept of a risk-based approach to the development of artificial intelligence technologies Danil Vyacheslavovich Budko

The concept of a risk-based approach to the development of artificial intelligence technologies Danil Vyacheslavovich Budko

The Construction of Personalized Language Learning System under the Guidance of ChatGPT

With the development of artificial intelligence technology, AI-powered chatbots are gradually being applied to language teaching and learning. Among them, ChatGPT, as a generative language intelligence model, plays its unique advantage in promoting personalized language learning. Hence, this article starts by elucidating recent research on the use of ChatGPT in language teaching and learning, giving some evidence about the possibility of personalized language learning under the guidance of ChatGPT. Then advantages associated with its usage in personalized language learning are discussed, including providing personalized language input, cultivating personalized language learning abilities and giving personalized language feedback for language learners. Based on these advantages, this article compares the traditional personalized language learning with the one under the guidance of ChatGPT and try to put forward a three-step personalized language learning system under the guidance of ChatGPT, hoping to improve the effectiveness of language learning and realize the truly personalized language learning in the future.

Regulatory impact of a governmental approach for artificial intelligence technology implementation in Vietnam

This study assesses Vietnam’s state-level implementation of artificial intelligence (AI) technology and analyses the government’s efforts to encourage AI implementation by focusing on the National Strategy on AI Development Program. This study emphasizes the possibility of implementing AI at the state level in Vietnam and the importance of conducting continuous reviews and enhancements to achieve sustainable and inclusive AI growth. Impact evaluations were conducted in public organizations alone, and implication evaluations were considered optional. AI impact assessments were constrained by societal norms that necessitated establishing relationships among findings. There is a lack of official information regarding the positive impact of Vietnam’s AI policy on the development of AI infrastructure, research, and talent pools. The study’s findings highlight the necessity of facilitating extensive AI legislation, and strengthening international cooperation. The study concludes with the following recommendations for improving Vietnam’s AI policy: implementing a strong AI governance structure and supporting AI education and awareness.

European Respiratory Society International Congress 2024: attendance of the Ukrainian delegation

ABSTRACT. From September 7 to 14, 2024, the Congress of the European Respiratory Society was held in Vienna (Austria) and for the third time it was organized in hybrid format. This event attracts participants from more than 160 countries from all continents and provides a scientific forum with a broad multidisciplinary program. Every year, the Ukrainian delegation takes part in the congress, where it highlights the results of the investigations of Ukrainian scientists and doctors. In 2024, 48 abstracts of reviews from Ukraine were selected for the congress. The structure of their topics reflected the current problems of national health care. The largest number of works (16) were devoted to chronic obstructive pulmonary disease and bronchial asthma, the most common conditions among chronic pathologies of the respiratory system. The next important topic of the Ukrainian delegation was tuberculosis (9 reports), as an epidemic of this disease has been registered in Ukraine since 1995 and continue now. The third important group of questions (8 presentations) related to today’s global problem – the consequences of the COVID-19 pandemic. Two oral presentations of the Ukrainian researchers on the topic of artificial intelligence and digital technologies were associated to the main theme of the congress “Humans and machines: getting the balance right”.

Innovative strategies for intelligent services in smart libraries in the information age based on linear discriminant analysis

With the advent of the information age, to provide better services and ensure the security management of libraries, intelligent facial recognition technology has gradually become a hot research direction in library management. Meanwhile, to further improve the comprehensive performance of facial recognition, this study attempts to integrate principal component analysis and linear discriminant analysis on the basis of analyzing the framework of recognition technology. Afterwards, it introduced support vector machines for recognition and classification, and proposed a new recognition model. The experimental results show that the recognition accuracy of the proposed model is up to 97 % in the ORL dataset and 94 % in the Yale dataset. The recognition error rate is as low as 0.1 % when the number of training samples is 215 and the number of iterations is 200. The model has the best recognition performance when the image size is 25 × 25 mm and the number of noises is 10. In addition, the model is particularly effective in recognition on single person color or gray images, with the highest P-value of 98.7 %, the highest R-value of 98.8 %, and the highest F1-value of 97.5 %. These results show that the proposed model significantly improves the accuracy and robustness of face recognition, and provides strong technical support for intelligent service innovation in smart libraries.

The Use of the OpenAI GPT-3 AI Technology for Marketing Content Purposes

Artificial intelligence (AI) technologies rapidly transform marketing worldwide with fast-paced content creation productivity driven by machine learning algorithms generating heightened interest from researchers. In recent years, AI tools helped writers by acting as a helping hand in the creation process, quick fact-checking and grammar correction. As OpenAI introduced the Generative Pre-trained Transformer 3 model (GPT-3), its newest autoregressive language model that uses deep learning to produce human-like text; we have researched its usability for generating a wide range of marketing content. The main objective of the research was to analyze different outputs of the GPT-3 AI with the help of a group of anonymous respondents to attempt to measure its usability in marketing environments as well as a way to measure if GPT-3 could pass the Touring test.

Evaluasi Pengalaman Pengguna Pada Learning Management System Menggunakan Metode User Experience Questionnaire

The purpose of this study was to conduct a survey to assess the performance of the Learning Management System (LMS). This is done related to the need to evaluate its use by users. This study used the User Experience Questionnaire (UEQ) method regarding (1) attractiveness, (2) clarity, (3) efficiency, (4) reliability, (5) stimulation, and (6) novelty. The survey was conducted online via a Google form and had 164 participants. The benchmark results show that all criteria are above the average, but the UEQ scale for novelty is the lowest at 0.87, while attractiveness is 1.44, pragmatic quality is 1.37, and hedonic quality is 1.05. Therefore, it is suggested that the learning management system be improved through interactive learning styles, such as live discussions in LMS, integration with other technologies, updating artificial intelligence and big data-based technologies, and adapting to the metaverse, while maintaining the attractiveness and identity of the platform through design. rework color schemes, content, including engaging videos and gamification, and implement live chat with lecturers to overcome time constraints during video conferencing.

Reconstructing AI Ethics Principles: Rawlsian Ethics of Artificial Intelligence

The popularisation of Artificial Intelligence (AI) technologies has sparked discussion about their ethical implications. This development has forced governmental organisations, NGOs, and private companies to react and draft ethics guidelines for future development of ethical AI systems. Whereas many ethics guidelines address values familiar to ethicists, they seem to lack in ethical justifications. Furthermore, most tend to neglect the impact of AI on democracy, governance, and public deliberation. Existing research suggest, however, that AI can threaten key elements of western democracies that are ethically relevant. In this paper, Rawls’s theory of justice is applied to draft a set of guidelines for organisations and policy-makers to guide AI development towards a more ethical direction. The goal is to contribute to the broadening of the discussion on AI ethics by exploring the possibility of constructing AI ethics guidelines that are philosophically justified and take a broader perspective of societal justice. The paper discusses how Rawls’s theory of justice as fairness and its key concepts relate to the ongoing developments in AI ethics and gives a proposition of how principles that offer a foundation for operationalising AI ethics in practice could look like if aligned with Rawls’s theory of justice as fairness.

Car detection algorithm in urban based on YOLOv5

Car detection is essential to promote the development of intelligent technologies such as autonomous driving and car forward collision warning. To deal with low detection accuracy, poor real-time performance and robustness of existing object detection algorithms, a YOLOv5-based car detection algorithm is used. The advanced structure design of Backbone, Neck and Head in YOLOv5 demonstrates powerful feature extraction and multi-scale object detection capabilities. The detection results reveal that the algorithm is able to quickly recognize cars in images in different urban scenes, and the detection accuracy can reach more than 95%, which can satisfy the application demands for real-time car detection.

AI-assisted marking: Functionality and limitations of ChatGPT in written assessment evaluation

Generative artificial intelligence technologies, such as ChatGPT, bring an unprecedented change in education by leveraging the power of natural language processing and machine learning. Employing ChatGPT to assist with marking written assessment presents multiple advantages including scalability, improved consistency, eliminating biases associated with human subjectivity. This work aimed to evaluate the usefulness, reliability and accuracy of ChatGPT in marking written assessments of varied types and to identify its limitations and challenges. ChatGPT was instructed using a set of prompts to mark the assessment based on a rubric. ChatGPT was able to evaluate and assess both coding and reflective assessments and to distinguish between assignments of different quality, demonstrating high consistency and accuracy for higher quality assessments, comparable to a human marking. ChatGPT was also able to generate textual detailed justifications based on the rubric and assessment task description. There was a significant difference in the outcomes generated by different prompts. These preliminary findings suggest that utilising ChatGPT as a marking assistant can increase written assessment marking efficiency, reduce cost and potentially decrease the unfairness and bias by providing a moderating perspective. Implications for practice or policy: Assessment designers could reconsider the design, purpose and objectives of written assessments and leverage ChatGPT effectively for teaching and learning. Assessors might consider adapting the technology as a grading aid, to support a human-in-the-loop grading process, providing additional resources and time, moderating and refining individual feedback, to increase consistency and quality. Curriculum and programme leaders could develop guidelines around the ethical use of generative AI-assisted assessment practice, monitor and regulate the ongoing evaluation and refinement.

A facial and vocal emotion recognition system based on deep learning

The traditional teaching mode, mainly characterized by "cramming" instruction, often overlooks the absorption of knowledge by students during the teaching process. Even in recent years, the emerging "flipped classroom" model heavily relies on teachers' experience and expertise, making it challenging to quantify students' classroom learning states. Among young teachers lacking teaching experience, the replication of excellent teaching models faces difficulties. With the rapid development of artificial intelligence technology, deep learning and other AI techniques are increasingly applied to people's lives. Additionally, emotion computing technology is also becoming more sophisticated. In the field of education, the use of AI technology holds the potential to help teachers systematically and comprehensively assess teaching quality, promoting the sharing of high-quality educational resources nationally and globally. Existing emotion detection technologies often focus on single techniques such as image recognition or natural language processing, resulting in low accuracy in discerning human emotions and making it difficult to accurately identify emotional changes in complex scenarios such as classrooms. Furthermore, there is a lack of comprehensive emotional statistical analysis methods for audio and visual data. In light of these, we have designed and implemented a multi-modal emotion recognition and analysis system. Through testing in classroom settings, this system has the potential to assist classroom teaching and enhance teaching quality.

The relationship between stress, anxiety and eating behavior among Chinese students: a cross-sectional study.

The expansion of higher education and the growing number of college students have led to increased awareness of mental health issues such as stress, anxiety, and eating disorders. In China, the educational system and cultural expectations contribute to the stress experienced by college students. This study aims to clarify the role of anxiety as a mediator in the relationship between stress and eating behaviors among Chinese college students. This study utilized data from the 2021 Psychology and Behavior Investigation of Chinese Residents, which included 1,672 college students under the age of 25. The analysis methods comprised descriptive statistics, t-tests, Pearson correlation analyses, and mediation effect analysis. The findings indicate that Chinese college students experience high levels of stress, with long-term stress slightly exceeding short-term stress. Both types of stress were positively correlated with increased anxiety and the adoption of unhealthy eating behaviors. Anxiety was identified as a significant mediator, accounting for 28.3% of the relationship between long-term stress and eating behavior (95% CI = 0.058-0.183). The mediation effect of short-term stress on eating behavior through anxiety was also significant, explaining 61.4% of the total effect (95% CI = 0.185-0.327). The study underscores the importance of stress management and mental health services for college students. It recommends a comprehensive approach to reducing external pressures, managing anxiety, and promoting healthy eating behaviors among college students. Suggestions include expanding employment opportunities, providing career guidance, enhancing campus and societal support for holistic development, strengthening mental health services, leveraging artificial intelligence technologies, educating on healthy lifestyles, and implementing targeted health promotion programs.

Mitigating Intermittent Connectivity Problems in Vehicle-to-Vehicle Communication (V2VC): A Sparse Network Computational Model (SNCM)

INTRODUCTION: Wireless communication has made remarkable progress, by the rapid development of wireless technology in Artificial Intelligence (AI). Intelligent Transportation Systems (ITS), and Vehicular Ad Hoc Networks (VANETs) have received significant attention to ensure safety. However, V2V communication in VANETs faces uncontrollable challenges due to frequent intermittent connectivity issues in infrastructure-less networks. Addressing these problems in both safety and non-safety applications is a complex task.OBJECTIVES: To mitigate the intermittent connectivity problems, a novel Sparse Network Computational Model (SNCM) was proposed.METHODS: Extensive simulations using MATLAB to analyze the impact of spatial-temporal variations under different traffic flow densities. We varied the sensitivity factor (λ) at different time intervals while maintaining a constant traffic density. RESULTS: The findings indicate that there is no need to increase λ beyond certain thresholds for each level of service. The simulation results provide valuable guidelines for designing sparse networks, effectively mitigating frequent intermittent disconnections. Simulation experiments revealed an optimal threshold for the sensitivity factor λ for each level of service. Increasing λ beyond certain thresholds did not yield significant improvements in mitigating disconnections in V2V communication.CONCLUSION: The results provide valuable insights and guidelines for designing sparse networks to enhance connectivity and address intermittent disconnection issues. This paper presents a groundbreaking endeavor, and therefore, direct comparisons with existing protocols to evaluate its overall performance are beyond the scope of this paper. Instead, the SNCM protocol is intended to set a standard for future researchers to benchmark their research contributions against.

Mapping the scientific landscape and evolution of the international journal of surgery: A scientometric analysis (2004-2024)

Aim: The purpose of this study was to analyze the publication characteristics and development of the International Journal of Surgery over its 20-year history. Methods: This study included articles published in IJS during the 20 years from 2004 to 2024. The data were analyzed using the Citespace, VOS viewer, and the ‘Bibliometrix’ package in R software. We studied the dynamics and trend patterns of IJS literature production through descriptive bibliometrics and identified the most prolific authors, publications, institutions, and countries. Bibliometric maps were utilized to visualize published articles’ content and identify the most prolific research terms and topics in IJS, as well as their evolution over time. Results: A total of 5964 publications in the IJS from 2004 to 2023 and 356 publications in 2024 were included in the analysis. The study revealed a positive trend in literature production, although the number of articles published in IJS has slightly decreased recently. The most productive country was China (n=1,211), the most productive institution was Sichuan University (n=151), and the most prolific author was Wang Yang (n=87). The top 15 most cited articles focus primarily on research reporting standard guidelines for surgical studies. Research published in IJS mainly targeted middle-aged and older adults, emphasizing postoperative complications and treatment outcomes. The future research focus in IJS might center on integrating artificial intelligence and deep learning technologies to revolutionize surgical research and practice. Conclusions: The International Journal of Surgery significantly contributes to advances in surgical research. IJS prioritized enhancing patient outcomes and advancing surgical techniques, focusing on middle-aged and older adults, postoperative complications, and treatment outcomes. The journal emphasized robust evidence through retrospective, controlled, and cohort surgical studies. Integrating artificial intelligence and deep learning represented a significant frontier poised to revolutionize surgical care, shaping the future landscape of research and practice.

Are Recommendation Systems Annoying? An Empirical Study of Assessing the Impacts of AI Characteristics on Technology Well‐Being

ABSTRACTRecommendation systems—that is, a class of machine learning algorithm tools that filter vendors' offerings based on customer data and automatically recommend or generate personalized predictions—are empowered by artificial intelligence (AI) technology and embedded with AI characteristics; but the potential consequences for customer well‐being are greatly overlooked. Hence, this research investigates the impact of AI characteristics on technology well‐being (self‐efficacy, technology satisfaction, emotional dissonance, and autonomy) through two mechanisms: intuitiveness versus intrusiveness. A literature review which conceptualizes AI characteristics and technology well‐being in the recommendation system context is followed by a US‐based survey approach which shows that higher levels of information optimization, predictability, human likeness, and customizability lead to higher levels of intuitiveness, whereas only information optimization and human likeness leads to increased intrusiveness. However, both intuitiveness and intrusiveness are found to promote technology well‐being in the context of a recommendation system, especially for those more vulnerable individuals who respond positively to intrusiveness. Hence, the conclusion is “the recommendations are not always annoying,” whereby the relationships between AI characteristics and technology well‐being are significantly influenced by perceived intrusiveness. These findings help business practitioners to identify how consumers perceive and engage different AI characteristics, and therefore could better take care of technology well‐being while boosting AI development.

Recent Developments on Novel 2D Materials for Emerging Neuromorphic Computing Devices

The rapid advancement of artificial intelligent and information technology has led to a critical need for extremely low power consumption and excellent efficiency. The capacity of neuromorphic computing to handle large amounts of data with low power consumption has garnered a lot of interest during the last few decades. For neuromorphic applications, 2D layered semiconductor materials have shown a pivotal role due to their distinctive properties. This comprehensive review provides an extensive study of the recent advancements in 2D materials‐based neuromorphic devices especially in multiterminal synaptic devices, two‐terminal synaptic devices, neuronal devices, and the integration of synaptic and neuronal devices. Herein, a wide range of potential applications of memory, computation, adaptation, and artificial intelligence is incorporated. Finally, the limitations and challenges of neuromorphic devices based on novel 2D materials are discussed. Thus, this review aims to illuminate the design and fabrication of neuromorphic devices based on van der Waals (vdW) heterostructure materials, leveraging promising engineering techniques to excel the applications and potential of neuromorphic computing for hardware implementations.

Securing the Future: The Impact of AI on Cybersecurity and Economic Stability in the Banking Industry

People are unable to handle the volume of data and the complexity of processes required to secure cyberspace without significant automation. However, software and technologies with conventional fixed implementations are challenging to construct in order to effectively protect against security risks. AI learning techniques and machine simplicity can be used to treat this issue. And when we talk about cybersecurity in banking industry then these industries are trying to adopt artificial intelligence to develop a cyber defence system that would reduce unwanted access and cyberattacks. and how cybersecurity contributes to long-term economic growth. Meanwhile, there is a significant technological disruption taking place in the financial sector. It becomes crucial to comprehend the effects of Artificial intelligence (AI) and other technologies on the cybersecurity of banks.

Distribution characteristics and development layout of rural tourism resources based on LSTM in-depth learning in the context of common prosperity

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:150%"><span style="font-family:Calibri,"sans-serif""><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman","serif"">The development and layout of rural tourism resources is the core issue supporting the development and planning of tourism destinations. Currently, it mainly relies on the experience and judgment of planners, with strong subjectivity and lack of objective information technology support. Therefore, in the context of common prosperity, a method for distribution characteristics and development layout of rural tourism resource based on LSTM deep learning is proposed. After distinguishing various rural tourism resources using the classification method of rural tourism resources based on LSTM in-depth learning, the spatial distribution characteristics of various tourism resources are estimated using the analysis method for spatial distribution characteristics of tourism resources based on kernel density estimation; After identifying the current status of tourism resource functional areas through a method of tourism resource development and layout based on a grid spatial analysis model combined with the distribution characteristics information mastered, and from the perspective of tourists, it can optimize the layout mode of functional areas to maximize the benefits of rural tourism resources on the basis of satisfying tourists' preferences. After testing, this method can utilize intelligent information technology to optimize the development and layout of rural tourism resources.</span></span></span></span></span></span></p>