Development Of Intelligence Research Articles

On the issue of the use of artificial intelligence technologies by financial market entities and ensuring the security of their personal and corporate data

The current study analyzes the issues of the accelerated development of artificial intelligence (AI) in the Russian Federation, the use of artificial intelligence technologies by market participants, including financial ones, and ensuring the security of their personal and corporate data, in the context of the rapid progress of AI technologies, contributing to the efficiency of technological solutions through the use of various methods and algorithms. Development in this area is supported by government agencies, scientific institutions, government organizations, and leading companies, which portends a significant expansion of the market by 2030. Artificial intelligence offers many opportunities, but it also has negative aspects. One such aspect is the collection of personal information of users, which is necessary in order to maintain, improve the performance of AI. This can happen either within the law or through illegal actions by digital criminals. Collecting all sorts of data allows algorithms to learn, adapt to society's needs, offer personalized recommendations, improve app interactions, much more. But misuse of these practices leads to serious privacy threats. The responsibility for protecting the information received lies with organizations, including financial ones, which must comply with security measures and proper protection of personal information. However, users themselves are sometimes insufficiently vigilant and cautious in handling their data, thereby increasing the risk of confidential information leakage. Cases of increasing digital crimes confirm that AI creates favorable conditions for various manipulations by cybercriminals, which negatively affects the economic security of the country and its citizens. The article discusses the current problems associated with the introduction of artificial intelligence in all spheres of human life, provides statistics on the number of court cases related to artificial intelligence, concludes that it is necessary to introduce a multi-level set of measures, including not only the general regulation of artificial intelligence, providing basic principles and approaches, but there are also self-regulation mechanisms that will allow market participants to adapt to rapidly changing conditions.

Accuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis.

Background and Objective: The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom checker apps shows better performance in diagnostic accuracy and urgency assessment compared to physicians remains unclear. Therefore, this study aimed to investigate the performance of existing symptom checker apps in orthopedic and traumatology cases compared to physicians in the field. Methods: 30 fictitious case vignettes of common conditions in trauma surgery and orthopedics were retrospectively examined by four orthopedic and traumatology specialists and four different symptom checker apps for diagnostic accuracy and the recommended urgency of measures. Based on the estimation provided by the doctors and the individual symptom checker apps, the percentage of correct diagnoses and appropriate assessments of treatment urgency was calculated in mean and standard deviation [SD] in [%]. Data were analyzed statistically for accuracy and correlation between the apps and physicians using a nonparametric Spearman's correlation test (p < 0.05). Results: The physicians provided the correct diagnosis in 84.4 ± 18.4% of cases (range: 53.3 to 96.7%), and the symptom checker apps in 35.8 ± 1.0% of cases (range: 26.7 to 54.2%). The agreement in the accuracy of the diagnoses varied from low to high (Physicians vs. Physicians: Spearman's ρ: 0.143 to 0.538; Physicians vs. Apps: Spearman's ρ: 0.007 to 0.358) depending on the different physicians and apps. In relation to the whole population, the physicians correctly assessed the urgency level in 70.0 ± 4.7% (range: 66.7 to 73.3%) and the apps in 20.6 ± 5.6% (range: 10.8 to 37.5%) of cases. The agreement on the accuracy of estimating urgency levels was moderate to high between and within physicians and individual apps. Conclusions: AI-based symptom checker apps for diagnosis in orthopedics and traumatology do not yet provide a more accurate analysis regarding diagnosis and urgency evaluation than physicians. However, there is a broad variation in the accuracy between different digital tools. Altogether, this field of AI application shows excellent potential and should be further examined in future studies.

The Effect of Intelligent Development on Green Economy Efficiency: An Analysis Based on China’s Province-Level Data

As the main driving force of the new technological revolution, intelligent development is the key to promoting high-quality economic development. This paper empirically examines the nonlinear influence of intelligent development on green economy efficiency and its action paths using provincial panel data of China from 2009 to 2021. The result provides significant evidence of a U-shaped relationship between intelligent development and green economy efficiency, indicating that intelligent development initially leads to green economy efficiency decreases before ultimately increasing. Additional analysis confirms that environmental regulation, green finance, and industrial agglomeration positively moderate the impact of intelligent development on green economy efficiency. Furthermore, heterogeneous tests reveal that in the eastern region and after the release of “Made in China 2025” in 2015, the nonlinear effect of intelligent development on green economy efficiency is more pronounced. The findings of this paper provide a beneficial reference for how to leverage intelligent technology to release new kinetic energy for green economic growth under the new development concept.

Comparative analysis of deep learning models for crack detection in buildings

Life-time of the buildings is generally challenged by the act of nature. In-spite of the fact that the constructions provide minimum guarantee on quality and durability, certain mismatch in the composition of the materials, stress on the building, and chemical or physical imbalance of the materials, lead to surface crack. Cracks are also generated due to the shuffle of climatic conditions, which leads to the contraction and expansion of the building surfaces, and other damages. The guarantee on building safety and serviceability depends on how these buildings are successfully assessed and maintained. The development of Artificial Intelligence (AI) techniques, provide favourable solutions in-order to handle, manage and solve building cracks, through analysis using deep image neural network models, that perform classification of the building with crack images. As a result, a critical challenge for many civil engineering applications is the precise, quick, and automated identification of cracks on structural surfaces is addressed with the solutions provided by the deep image neural networks. In this research, we tackle the research gap and data scarcity by developing and curating a novel deep learning image processing for detecting cracks in brickwork. We also train and validate several deep learning models to classify brickwork images as either cracked or normal. The dataset of the proposed work contains 24,000 images which are classified through binary classes. These classes are generated for crack and non-crack images. The various parameters such as Batch size, Pooling, Activation functions Learning-rate, Kernel-Size, Normalization, and Optimizers are used for the evaluation of the model. The proposed work performs a comparative analysis of four deep image models such as Inception V3, VGG-16, RESNET-50 VGG-19, Inception ResNetV2 and CNN-RES MLP. With the analysis of all these models, the Inception V3 provides the best of all with the accuracy value of 99.98%. The InceptionV3 tops the Precision value of 99.99% and RESNET-50 tops the Recall value of 99.98%. The IncpetionV2 provided the best of the Region of Convergence value of 0.9999 which is the best among all the models for reliable and stable performance.

TinySpiking: a lightweight and efficient python framework for unsupervised learning spiking neural networks

Abstract Neural computation frameworks are essential for advancing computational neuroscience and artificial intelligence, offering a robust platform for simulating intricate brain-like processes and fostering the growth of intelligent systems. This study presents TinySpiking, a novel, lightweight, and energy-efficient Python framework designed for the simulation and learning of Spiking Neural Networks (SNNs). Unlike traditional frameworks, TinySpiking does not depend on third-party libraries, thereby reducing computational overhead and energy consumption. The framework's innovation is that it implements unsupervised learning through Spike-Time Dependent Plasticity (STDP), a biologically inspired learning rule that adjusts synaptic weights based on the precise timing of neuronal spikes. This mechanism is crucial for constructing complex neural architectures and effectively processing spatio-temporal data, which is vital for the dynamic and intricate demands of real-world data analysis. Demonstrating the practical utility of TinySpiking, we applied it to image reconstruction tasks using the MNIST and landmark datasets. The results not only validate the network's ability to autonomously identify and enhance key visual features without external supervision but also highlight its efficiency in learning from data, mirroring the adaptability of biological neural systems. In conclusion, TinySpiking's innovative, lightweight design and its proven effectiveness in unsupervised learning tasks make it a standout computational tool for the fields of computational neuroscience and machine learning. Its low time consumption, biological plausibility, and independence from third-party libraries position it as a compelling platform for future research and applications, promising to drive advancements in unsupervised learning and intelligent system development.

A Systematic Literature Review on the Relationship Between Emotional Intelligence and Transformational Leadership—A Critical Approach

The aim of this paper is to fill the identified gap in the literature regarding a holistic approach to the relationship between emotional intelligence (EI) and transformational leadership (TL). In fact, this paper contributes to the annotation of the literature in this particular field through a selective bibliography, aiming to cover the main contributors to the development of this topic and explore their different views in order to critically approach the pros and cons, producing implications for academia and organizations. A leader who applies the transformational style is able to indirectly or directly influence the efficiency of employees through increased levels of emotional intelligence and developed cognitive skills. The purpose of this paper is to examine the correlation between EI and TL. A systematic literature review (SLR) was conducted for papers published from 2000 onwards (21st century). Published papers were selected from international scientific journals that have been indexed as Q1 or Q2 (best quartiles) in the SJR database fields of business, management, and accounting. For the initial screening, the Google Scholar database was used to access papers through the following keywords: emotional intelligence, transformational leadership, organizational behavior, management, and organizational development. Furthermore, SLR’s PRISMA flow chart indicated the most suitable papers to be analyzed in the context of this paper. The research yielded 16 published studies confirming the positive relationship between EI and TL and 9 papers questioning it. These papers have been analyzed to open a discussion and draw conclusions. Research on the relationship between transformational leadership and emotional intelligence demonstrate a positive correlation in the majority, while there are some who question it.

Evolution of artificial intelligence in healthcare: a 30-year bibliometric study.

In recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. Concurrently, the integration of artificial intelligence with the medical field has garnered increasing attention from medical experts. This study undertakes a dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades to investigate the current status and trends of the fusion between medicine and artificial intelligence. Following a search on the Web of Science, researchers retrieved all reviews and original articles concerning artificial intelligence in healthcare published between January 1993 and December 2023. The analysis employed Bibliometrix, Biblioshiny, and Microsoft Excel, incorporating the bibliometrix R package for data mining and analysis, and visualized the observed trends in bibliometrics. A total of 22,950 documents were collected in this study. From 1993 to 2023, there was a discernible upward trajectory in scientific output within bibliometrics. The United States and China emerged as primary contributors to medical artificial intelligence research, with Harvard University leading in publication volume among institutions. Notably, the rapid expansion of emerging topics such as COVID-19 and new drug discovery in recent years is noteworthy. Furthermore, the top five most cited papers in 2023 were all pertinent to the theme of ChatGPT. This study reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine. Additionally, medical artificial intelligence research is dynamically evolving with the advent of new technologies. Moving forward, concerted efforts to bolster international collaboration and enhance comprehension and utilization of AI technologies are imperative for fostering novel innovations in healthcare.

Transforming Education: The Impact of Artificial Intelligence on Learning and Pedagogical Practices

The rapid development of Artificial Intelligence (AI) technology has transformed various sectors, including education and learning. In the context of education, AI significantly impacts teaching methods, classroom management, and the personalization of the learning process. AI technologies enable the creation of adaptive learning systems tailored to individual students' needs, improve the efficiency of assessments, and facilitate remote learning management. This paper explores how AI applications can enhance pedagogical effectiveness, support the role of teachers, and expand access to quality education. Additionally, it addresses the challenges of integrating AI into education, such as concerns over the replacement of teachers and the technological gap between different schools. This paper provides a comprehensive review of AI's role in education and its potential to transform future learning systems.Keywords: Artificial Intelligence, Adaptive Learning, Education, Educational Technology, Personalization, Teaching

Impact of Artificial Intelligence on Comparative Advantages in the Electronics Manufacturing Industry: A Study of China and Vietnam

In recent years, the electronics manufacturing industry has undergone rapid changes, and part of the electronics manufacturing industry is shifting from China to Vietnam. With the development of artificial intelligence, its influence in various manufacturing industries around the world is gradually increasing, especially in the electronics manufacturing industry. This study explores the application of AI in the electronics manufacturing industry in China and Vietnam and how it affects the transformation of the electronics manufacturing industry in China and Vietnam and leads to a shift in the industry chain. The study uses data analysis including sources such as the WTO and Trade Map, and analytical method of RCA index calculation. This article focuses on the research question of the impact of the application of artificial intelligence in the electronics manufacturing industry on the production and export of technology-intensive and labor-intensive electronic products in China and Vietnam and how the impact has facilitated the shifting of electronics manufacturing from China to Vietnam. The results show that AI technologies such as predictive maintenance and quality control for high-tech electronics have given China an advantage in technology-intensive manufacturing, while Vietnam has enhanced its comparative advantage in labor-intensive manufacturing by optimizing its assembly and inventory management through AI technologies.

Artificial Intelligence, Data Centers, Energy Capabilities, and International Security: An Exploratory Analysis

Previous valuable scholarship has examined how data centers affect the development and use of artificial intelligence (AI) technology. Additional research has analyzed how energy consumption and energy efficiency impact data centers. However, less scholarship has considered how data centers, energy capabilities (energy production and energy efficiency), and AI development interact to affect international security. Thus, this exploratory study considers the relationship between data centers, energy capabilities, and AI development and analyzes their potential impact on power distribution in the international system. In doing so, the study develops four indices to capture influential factors related to AI development and security. The study highlights the important role data centers, energy capabilities, and AI development may play in shaping the international balance of power and global security.

Prognosis of bronchopulmonary dysplasia

Children with bronchopulmonary dysplasia (BPD) often exhibit severe respiratory problems and significant pulmonary dysfunction during school age and adulthood. Exercise tests show a decline in cardiopulmonary function and physical performance in children with BPD, who also have a higher incidence of pulmonary hypertension. These children generally perform poorly in terms of intelligence, language, and motor development. As they age, the risk of neurodevelopmental disorders increases, and health-related quality of life is also affected. This article reviews the prognosis of the respiratory system, physical capacity, cardiovascular system, nervous system, and health-related quality of life in children with BPD, aiming to improve the management of these patients and enhance their subsequent quality of life.

Application analysis of wireless communication technology in China's smart grid

With the rapid advancement of China's economy and technological innovation, wireless communication technology has seen considerable development. Concurrently, global energy and environmental challenges have intensified, heightening the demand for efficient and sustainable energy management solutions. In response to evolving energy demands, China has introduced new standards for digital power grid construction, prioritizing the use of wireless communication technologies to improve energy efficiency and develop smarter grid systems. These technologies facilitate real-time data transmission, remote monitoring of equipment, and network interconnectivity, thereby improving operational efficiency and monitoring capabilities. This paper reviews recent applications of wireless communication technologies within smart grids, aiming to provide valuable insights for future developments based on existing literature and data. Wireless communication technology in smart grids mainly enhances the intelligence and efficiency of the grid by enabling real-time data transmission, remote device management, and optimized energy distribution. Technologies such as Wi-Fi, 5G, and LoRa facilitate key functions like smart meter monitoring, equipment status management, and demand response, driving the automation and intelligent development of power systems. Future research should focus on optimizing the integration of these technologies, strengthening network security, and leveraging big data and artificial intelligence to advance the intelligence of power systems, thereby addressing critical energy challenges and fostering sustainable development

The Practice of Large Language Models in Automated Question Generation: A Case Study of ChatGLM in High School Information Technology Curriculum

With the advancement of information technology, the field of education is undergoing transformation. In the teaching of high school information technology, scientific and efficient question formulation is crucial for enhancing the quality of education. Traditional methods of question formulation rely on the experience of teachers, leading to issues such as inconsistent difficulty levels and incomplete coverage of knowledge points. Large language models (LLMs), such as ChatGLM, leverage natural language processing and deep learning technologies to automatically generate questions that align with the curriculum, thereby improving scientific accuracy and precision, enhancing diversity and innovation, and catering to students’ personalized needs. Additionally, LLMs can enhance the efficiency of question formulation and reduce the burden on teachers. This paper explores the application value of large language models in the formulation of questions for high school information technology, through empirical research comparing the performance of human-generated and ChatGLM-generated questions in terms of accuracy, relevance, clarity, and willingness. The study selected two chapters, “Data and Information” and “Fundamentals of Algorithms,” and employed both human and ChatGLM-generated questions, inviting teachers to evaluate them. Through data analysis and statistical testing, we reveal the advantages and limitations of large language models in educational question formulation, providing insights for the intelligent development of educational assessment systems.

Application of Artificial Intelligence Advances in Athletics Industry: A Review

ABSTRACTWith the rapid development of Artificial Intelligence (AI) technology, the athletics field is undergoing profound changes. This transformation is reflected not only in the ways of training and competition but also in the overall enhancement of athletes' performance and the efficiency of event management. The introduction of AI has made data analysis feasible, enabling coaches and athletes to gain deeper insights and make more informed decisions. This paper reviews the current applications of AI in the athletics domain and its enhancement of athlete performance and competitive strategies, focusing on the practical applications of AI in event management, performance analysis, injury detection, and personalized training. Furthermore, AI systems support coaches in intelligent analysis by integrating historical data with real‐time data, thereby improving the efficiency of tactical decision‐making. However, despite the significant achievements in AI applications, a series of challenges remain, including the lack of high‐quality datasets, insufficient model interpretability, and ethical and privacy issues. In light of these challenges, we also propose viewpoints on future development directions aimed at promoting the intelligent transformation of the athletics industry.

Social smart city research: interconnections between participatory governance, data privacy, artificial intelligence and ethical sustainable development

Social aspects constitute both concerns and opportunities in smart city development, as evidenced by a rapidly increasing body of research. This article presents the first-ever review of all the existing research on social focus in smart cities, delineating the distribution of topics, knowledge bases, and research frontiers that constitute the existing body of knowledge. A bibliometric review was performed to pinpoint publication trends, influential authors, their institutions, and prevalent subject areas within the literature since 2000. Using the Web of Science database, an amalgamation of major indexes (SCI-EXPANDED, SSCI, AHCI, ESCI) were applied to consider the research pattern and citation impact in different disciplines. 1,030 selected articles were subjected to bibliometric mapping using VOSviewer. The results show an almost exponential growth in the number of publications from 2015 onwards. Four interconnected thematic clusters cropped up: (1) participatory governance, (2) data privacy and security, (3) artificial intelligence and social media, and (4) ethics and sustainable development. A deeper analysis of key terms used in recent research revealed the following hot topics: (1) governance and citizen participation, (2) artificial intelligence technologies such as machine learning, (3) blockchain, and (4) Internet of Things. Co-authorship and geographical analyses underpin a solid international collaboration for leading institutions. The results underscore the rising significance of social smart city research by emphasizing the interconnectedness of governance, technology, citizen engagement, and ethics for a comprehensive approach to smart city initiatives. Furthermore, they recommend integrating social equity into these frameworks and enhancing geographic studies through greater international collaboration.

Impact of a Structured Social Skills Training Program on Adolescents and Young Adults with Level 1 Autism.

Level 1 autism spectrum disorder (ASD) is a neurodevelopmental condition characterised by challenges in social and communication skills. Despite these difficulties, individuals with level 1 ASD often exhibit average intelligence and typical language development. Improving socialisation skills in this population requires tailored approaches that address their specific needs and include targeted strategies. This study aims to evaluate the effectiveness of a structured social skills training programme for adolescents and young adults with level 1 ASD. Participants diagnosed with level 1 ASD, regardless of gender, were consecutively recruited from an outpatient clinic. The intervention involved activities from the Social Skills, Autonomy, and Awareness Module, specifically designed for adolescents and young adults. Sessions were conducted fortnightly, lasting 1.5 to 3 h each, over 17 months. Adaptive behaviour was assessed using the Vineland Adaptive Behaviour Scales (VABS) at baseline and after completing the programme. Data were analysed with SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). Statistical methods included automatic clustering to identify optimal clusters and Pearson's Chi-square and Fisher's exact tests to evaluate variable distributions among the clusters. A total of 31 participants (77.4% female) with a mean age of 20.1 years (SD = 7.0) were included in the study. Two distinct clusters emerged. Cluster 1 (n = 8) had significantly higher mean ages and baseline Vineland II socialisation scores than Cluster 2 (n = 23). Both clusters demonstrated significant improvements in social skills following the intervention. This study highlights distinct profiles within individuals with level 1 ASD, showing a clear link between age and social skill development. The intervention improved social skills for most participants, regardless of the age at which treatment began. For some individuals, alternative or augmented treatment strategies may be necessary to achieve optimal results.

Partnership of key stakeholders in the use of generative artificial intelligence

Purpose: is to substantiate the need to improve the partnership between the state, business, universities and civil society in the field of generative artificial intelligence.Methods: the research is based on the application of theoretical and empirical analysis methods, including: logical, retrospective, generalization, modeling, comparison, statistical, observation, data visualization.Results: the article provides arguments confirming the relevance of generative artificial intelligence by its key stakeholders. The necessity of developing models of institutional interactions for building the new format of stakeholder interaction based on the principle of partial intersection of their institutional spheres of influence, coupled with the urgent demands of civil society, is substantiated. The analysis of the reasons for the interest of the state and business in using solutions based on artificial intelligence in their activities is carried out. Special attention is paid to the attitude of universities to the responsible introduction of generative artificial intelligence into the scientific and educational environment and its use in the solving educational and professional tasks. The improved model of partnership between the state, business, universities and civil society in the field of generative artificial intelligence is proposed.Conclusions and Relevance: partnership in the field of scientific and technological progress allows us to take into account the interests and needs of its key stakeholders, as well as emerging opportunities for them to develop a new role status in the development and use of generative artificial intelligence. The recommended partnership model of key stakeholders allows for the aggregation of financial and production resources of business, competencies and scientific potential of universities in joint projects to develop solutions in the field of development and use of generative artificial intelligence, which can give a significant synergistic effect if this collaboration is complemented by state participation. Inclusion in the model of civil society will ensure that its requests for the preservation of universal values are combined in decisions on the use of generative artificial intelligence and will give a human-centered character to scientific and technological progress in the context of digitalization of society.

Artificial Intelligence as a Metaphysical Event

The paper focuses on the questions of whether, to what extent, and in what ways the implications of the rapid development of artificial intelligence are changing the nature of one of the fundamental philosophical questions, “What does it (even) mean to understand?” It draws on two sources in particular: Hinton’s explanation of the technological development and functioning of deep neural networks and Nietzsche’s deconstruction of human understanding based on his key concept of “embodied errors.” In doing so, it reveals a series of unexpected parallels, relating in particular to the notion of micro- evolution and the function of error in the processes underlying “thinking” and “intelligence.” The paper therefore draws certain parallels and demarcation lines between human understanding and the “learning” procedures of digital neural networks. At the same time, it addresses the question of what it means for the interpretation of human understanding that, for the first time in history, understanding is faced with a real, existing antithesis, represented by intelligent systems which, although they do not understand, are capable of performing the tasks of understanding, and capable of replacing understanding.

Application of AI technology in audit risk assessment and control: Taking internal audit of higher education institutions as an example

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.

Understanding Agentic Frameworks in AI Development: A Technical Analysis

This technical article examines the evolution and implementation of agentic frameworks in artificial intelligence development, focusing on their transformative impact across multiple industries. The article explores the fundamental architectural components, implementation methodologies, and practical applications of these frameworks in manufacturing, financial services, and healthcare sectors. By investigating the core components, including perception systems and decision architectures, alongside the Belief-Desire-Intention model and advanced learning mechanisms, this article provides comprehensive insights into how agentic frameworks are revolutionizing autonomous decision-making capabilities. The article also addresses critical technical challenges in scalability and safety while offering potential solutions and future directions for development, highlighting the growing importance of these frameworks in shaping the future of AI technology.