Development Of Artificial Intelligence Technology Research Articles

Employment Legal Protection in Facing Artificial Intelligence Disruption: Efforts to Overcome the Replacement of Human Workers

<p><em>This study aims to analyze employment legal protection in the face of disruption from artificial intelligence (AI) technology that threatens the existence of human labor. The development of AI technology has created new challenges for the world of employment, where many jobs have the potential to be replaced by machines and automated systems. This study uses a normative legal method with a statutory approach to examine existing employment regulations and their relevance in protecting workers' rights in the digital era. The data used consists of legal literature, laws and regulations related to the application of AI in the world of work. The results of the study show that Law Number 13 of 2023 concerning Manpower emerged as a response to the unfavorable situation for workers, with the aim of protecting labor rights and implementing international instruments and human rights declarations. Technological advances, especially automation and artificial intelligence (AI), bring benefits such as efficiency and new opportunities, but also pose challenges such as job loss. To address this, the government needs to strengthen employment legal protection, design policies to support affected workers, and provide skills training and incentives for companies. The Circular of the Ministry of Communication and Information on the ethics of using AI emphasizes inclusivity, humanity, accessibility, and sustainable development, as well as the protection of workers' rights such as the right to work and fair treatment. However, current national regulations do not specifically regulate the use of AI by companies, so new legal arrangements are needed to protect workers' rights and manage the transition due to Termination of Employment</em><em>.</em></p>

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Artificial Intelligence and Intellectual Property: Impact and Legal Implications

The rapid spread and development of artificial intelligence technologies has raised important questions that have an impact on laws and regulations related to intellectual property. In light of this, the research aims to explore the impact of artificial intelligence on intellectual property laws and regulations, and to examine the legal implications of the innovations generated by artificial intelligence on authorship, invention, ownership, infringement, and enforcement of intellectual property laws. In light of the great concerns about its impact on intellectual property laws and regulations and the uncertainty and ambiguity in the application of intellectual property laws, the researcher considered it a problem of the study as it leads to potential risks and challenges for creators, inventors, and intellectual property rights holders. The research followed the comparative approach to provide a view of the different approaches regarding this topic, and to help shed light on the main difficulties faced in applying the law in such an evolving context. The study reached many conclusions, but the most important of them is that artificial intelligence threatens office jobs due to its ability to generate high-quality content quickly and at a low cost, and intellectual property laws will continue to be breached, which necessitates the need for the law to adapt to establish rules of conduct and limits of human work, and recommends engaging in an international dialogue to unify laws and manage disputes related to intellectual property across borders.

Review of photovoltaic power prediction based on artificial intelligence methods

With the continuous growth of global demand for renewable energy, photovoltaic power generation, as a clean and efficient form of energy, has received widespread attention. However, photovoltaic power is affected by various factors such as irradiance, temperature, resulting in significant fluctuations in its output power. Therefore, accurate prediction of photovoltaic power is of great significance for the stable operation of the power system. In recent years, the rapid development of artificial intelligence technology has provided new solutions for photovoltaic power prediction. This paper will review the photovoltaic power prediction based on artificial intelligence methods, introduce its basic principles, application status, and challenges, in order to provide useful insights in related fields.

Current Status, Hotspots, and Trends of "Artificial Intelligence + Education" Research in China

The rapid development of artificial intelligence technology is profoundly leading education towards an intelligent direction. This article takes the literature related to "Artificial Intelligence + Education" published in the core journals of CSSCI from 2013 to 2024 as the research object, and uses the CiteSpace tool for visualization analysis. It combs and discusses the current status of domestic research in this field from aspects such as publication status, authors, institutions, and keyword co-occurrence and burst words, analyzes research hotspots, and predicts research trends. The results show that: in the past decade, the number of publications in this field in China can be divided into three stages: preliminary exploration, rapid development, and steady growth; some high-productivity authors have gradually formed a core group of authors, showing an overall development trend of multi-author cooperation and multi-unit collaboration; the types of publishing institutions are mainly normal colleges and departments of colleges and universities, with relatively low participation of enterprises and government institutions; research hotspots focus on three aspects: educational reform, categories of education, and educational technology, revealing the active exploration of the education field for intelligent technology; with the transformation of education and the development of technology, the combination of artificial intelligence and digital economy, new requirements for citizens' core literacy, and new challenges for teachers' professional literacy and educational models have become hot topics in current artificial intelligence research.

Substitution or creation? Identifying the role of artificial intelligence in employment

Recognising the significant role of artificial intelligence in the labour market is essential for China to develop sustainably. The research utilises the mixed frequency vector auto-regression (MF-VAR) technique, which would innovatively incorporate data at different frequencies into one model to identify the intricate correlation between the monthly artificial intelligence index (AII) and the quarterly unemployment rate (UR) in China. Through comparison, the MF-VAR method has a more substantial explanatory power than the low-frequency VAR (LF-VAR) model, the impulse responses of the former reveal that AII exerts favourable and adverse influences on UR. Among them, the positive effect occurs on the AII in the first and second months. In contrast, the negative one appears on the AII in the third month, highlighting that artificial intelligence has both stimulating and inhibiting effects on the labour market in China. By analysing UR’s predictive error variance decomposition, the total impact of China’s artificial intelligence technology on employment is a substitution; this outcome is accordant with the theoretical dis¬cussion. In the new round of scientific and technological revolution and industrial transformation, meaningful recommendations for China would be put forward to avert the wave of unemployment brought by the development of artificial intelligence technology.

Research on the Dissemination Effect of AIGC-generated Content on New Media Platforms: A Case Study of Xiaohongshu

With the rapid development of artificial intelligence technology, AIGC (Artificial Intelligence Generated Content) technology has shown strong application potential and communication effect on new media platforms. Taking Xiaohongshu as an example, this paper deeply discusses the communication effect of AIGC-generated content on new media platforms. Through empirical analysis, this paper reveals how AIGC technology affects content creation, communication efficiency and user interaction, and analyzes the mechanism, influencing factors and future development trends behind it. The purpose of this article is to provide valuable reference and inspiration for content creators, brand owners and platform managers of new media platforms.

AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis

The industrial sector is undergoing a transformation thanks to artificial intelligence (AI), which is bringing revolutionary changes to a number of areas like robots and automation, supply chain efficiency, predictive maintenance, and quality control and assurance. This thorough analysis investigates AI's significant influence on contemporary manufacturing procedures. Artificial Intelligence (AI) improves machine capabilities in robotics and automation, creating more intelligent and flexible systems. Robots can now complete complicated tasks with more flexibility and precision thanks to AI-driven developments, which boosts manufacturing efficiency and human-robot cooperation. Another crucial area where AI has a big impact is predictive maintenance. With the use of machine learning algorithms and real-time data analysis, artificial intelligence (AI) helps manufacturers anticipate equipment faults before they happen. By taking a proactive stance, unplanned downtime is decreased, resource usage is optimized, and machinery longevity is increased. AI has a significant positive impact on quality assurance and control because to cutting-edge technologies like data analytics and computer vision. Artificial intelligence (AI) solutions facilitate predictive quality management, improve fault identification, and offer real-time monitoring. Higher quality standards, less waste, and more customer happiness are the outcomes of this. Artificial Intelligence (AI) tackles issues related to supplier performance, accurate forecasting, and inventory management in supply chain optimization. Automation and analytics powered by AI simplify supply chain processes, increase transparency, and facilitate improved decision-making, which lowers costs and increases flexibility. All things considered, integrating AI into manufacturing processes offers a strategic advantage by promoting increased accuracy, flexibility, and efficiency. The continued developments in AI technology have the potential to significantly influence how manufacturing develops in the future by creating new avenues for creativity and excellence in the sector.

Disaster Rescue Drone Based on YOLOv4 Algorithm

Abstract With the rapid development of artificial intelligence technology, the application of unmanned aerial vehicles (UAV) in disaster relief is becoming more widespread. This article presents a disaster relief UAV based on the YOLOv4 algorithm, aimed at improving the speed and efficiency of emergency response and rescue. The article designs and implements a UAV integrated with the YOLOv4 object detection algorithm, used for real-time identification and location of people within disaster areas and for deploying rescue materials using a mechanical claw. Through experiments and comparative verification, the system has demonstrated high-efficiency in target detection and tracking in various disaster environments.

Better approximation of sigmoid function for privacy-preserving neural networks

Abstract With the development of artificial intelligence technology, privacy protection has become a prominent research direction. Homomorphic encryption is an important and promising technology in the construction of privacy-preserving neural networks. However, computing nonlinear activation functions remains a challenging task in the context of homomorphic encryption. Considering Sigmoid is one of the most commonly used activation functions, this paper proposes a polynomial approximation method for the Sigmoid function and applies it to the homomorphic encryption scheme of CKKS. We design polynomial approximation functions using Improved Taylor Expansion Segmentation (ITES), Least Squares method (LS), and Random Noise Addition method, respectively. These methods are implemented through the CKKS scheme in the homomorphic encryption library of SEAL. The experimental results show that the ITES achieves high fitting accuracy, reducing the average absolute error by 11.09% and the mean squared error by 38.06% compared to previous fitting methods. Therefore, experiments have identified methods with better fitting results in the CKKS scheme, which can meet the requirements of using homomorphic encryption in deep learning technologies. Finally, our analysis points out that different technologies have unique advantages and adapt to different scenarios, e.g., ITES has higher accuracy, LS has higher performance in scenarios with limited computing resources.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ИСКУССТВЕ. ТВОРЧЕСТВО В ЭПОХУ ИСКУССТВЕННОГО РАЗУМА

The article examines the impact of the development of artificial intelligence technology on art in general. The author describes the principles of image generation using neural networks, citing as examples only a few popular neural technology services, trying to explain in simple words the principle of operation of the image generation algorithm. Innovative artificial intelligence technologies are considered that allow you to independently create copies of artistic designs. Both the characteristic features and disadvantages of digital art, its development in the field of art and the introduction of artificial intelligence into creative practices are analyzed. The relevance of new neural creativity and the creation of artistic works using artificial intelligence are discussed. The technological skills of artificial intelligence in the person of the artist are considered, giving birth to a new era of art, creating their masterpieces differently.This scientific article required the study of both a large amount of literary material and electronic Internet resources, as a result of which the main types of graphic editors, their advantages and disadvantages were studied.

Research on English Translation Teaching Mode Based on Artificial Intelligence

With the continuous development of artificial intelligence technology, its application in the field of education is becoming increasingly widespread. This article aims to explore the effectiveness of an AI based English translation teaching model and its impact on traditional teaching models. By analyzing the current application status of artificial intelligence in English translation teaching, this article summarizes the advantages of artificial intelligence in personalized learning, real-time feedback, language data processing, etc., while also pointing out its limitations in the teaching process. Finally, this article proposes the development direction and improvement suggestions of English translation teaching mode based on artificial intelligence, in order to provide useful reference for educators.

Research on the Cultivation Mode and Path of Innovation and Entrepreneurship Ability of Computer and Electronic Engineering College Students under the Background of Artificial Intelligence

As a disruptive technology, artificial intelligence has affected global economic development on multiple levels. With the widespread application of artificial intelligence technology in various industries, the market demand for artificial intelligence technology and applications continues to grow, providing college students with opportunities to participate in emerging industries. College students can create new products and services and achieve technological innovation with the continuous development of artificial intelligence technology. This paper adopts the method of literature review, and combines the existing artificial intelligence background to explore the possibility of using artificial intelligence as an auxiliary tool for cultivating college students' innovation and entrepreneurship ability. Combining cutting-edge theories with a variety of practical explorations, this paper finally forms a set of methodologies for computer and electronic engineering college students to complete learning and entrepreneurial practice through artificial intelligence in the stage of university education.

Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth.

Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.

Optimization of English Teaching Model Based on Deep Learning Algorithm

With the vigorous development of artificial intelligence technology, especially in the field of deep learning, English education and teaching models are facing unprecedented opportunities and challenges. In order to enable students to master English knowledge more efficiently and align with international standards, it is particularly important to study the optimization of English teaching models. This paper uses specific deep learning algorithms and techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model and optimize English teaching modes, aiming to solve many problems in English teaching and improve the quality of English teaching. Through deep learning algorithms, we can analyze students’ learning behavior, habits and grades, thereby providing them with personalized learning resources and teaching strategies. Deep learning technologies such as CNN and RNN are used to recognize keywords and phrases in text, as well as to process sequence data such as speech and text, helping teachers better understand students’ learning needs and interests, thereby adjusting teaching content and methods. In addition, the adaptive nature of deep learning algorithms allows automatic adjustment of teaching content and difficulty according to the actual situation of students, providing them with learning resources and support that better meet their learning needs. This study not only applies deep learning algorithms to optimize English teaching modes but also delves into how these algorithms affect the learning outcomes of students and the teaching efficiency of teachers. Through empirical research and case analysis, we hope to provide new ideas and methods for the future development of English education.

Artistic sense of interior design and space planning based on human machine intelligent interaction

The rapid development of artificial intelligence technology is gradually penetrating into multiple fields such as interior design and spatial planning. The aim of this study is to integrate artificial intelligence with interior design, enhance design artistry and user experience, and address the interactive needs of interior space design choices. A set of indoor space design recognition system has been designed by introducing artificial intelligence networks and attention mechanisms. This study first optimizes the CenterNet algorithm based on attention mechanism and feature fusion to improve its accuracy in identifying complex components. Afterwards, the long short-term memory network and convolutional neural network are trained to complete the task of spatial layout feature recognition and design. The performance test results showed that after testing 100 images, the software could recognize indoor design space images and create corresponding vector format space maps in about 5 minutes, providing them to the 3D modeling interface to generate 3D scenes. Compared to the approximately 25 minutes required by manual methods, the design efficiency has been significantly improved. The research and design method has a fast convergence speed and low loss during the retraining process. In simulation testing, its mAP value reached 91.0%, higher than similar models. It performs better in detecting walls, doors and windows, bay windows, double doors, and two-way doors. Moreover, it has outstanding ability when facing structures such as short walls and door corners, and can recognize and create vector format spatial maps within 5 minutes, which is accurate and efficient. The system designed in this project has optimized the interaction between designers and clients in interior design, accurately capturing user intentions and assisting designers in improving work efficiency.

IMPLEMENTATION OF A DYNAMIC PRICING SYSTEM IN RETAIL: AN ANALYTICAL REVIEW OF THE PRACTICES OF RUSSIAN COMPANIES

Over the past few years, the conditions and nature of doing business for Russian companies have changed dramatically. The processes of procurement, logistics, product sales, accounting and other business processes are automated. The development of artificial intelligence technology and the growing volume of data are additional growth drivers for the company. Within the framework of this article, an analytical review of the practices of applying the pricing system in retail of Russian companies was conducted. The pricing process is key, which allows you to directly influence the main economic indicators of the company’s activities, since sales volume, profit, demand for products, inventories of inventory resources can be regulated by means of competent changes in prices for goods. Dynamic pricing is gaining great popularity among retailers, which leads to the expansion of patent activities in terms of software and models for automating pricing, including using machine learning. In this article, the authors reviewed some of the most popular offers on the market for the introduction of dynamic pricing systems and analyzed them, revealed the main features and identified common features of building automated dynamic pricing models. The practices of implementing dynamic pricing systems in Russian companies were also investigated. The results obtained allow us to draw conclusions about the importance of using automated pricing systems in retail in modern conditions, their capabilities and application problems.

Artificial Intelligence and International Law: The Impact of Emerging Technologies on the Global Legal System

With the rapid development of artificial intelligence technology, its impact on international law is becoming increasingly significant. This paper explores the application of AI in international relations and the challenges it poses to existing legal systems, analyzing AI regulation within the current international law framework and the exploration of emerging international legal norms. Through specific cases, the paper elaborates on legal issues in the fields of intelligent weapons, cybersecurity, and data protection, emphasizing the importance of international cooperation and global governance mechanisms in addressing these challenges. Finally, the paper looks forward to future trends in AI technology and international law’s response strategies, proposing the necessity of constructing effective legal frameworks and ethical guidelines to promote the sustainable development of AI technology and the healthy development of the global legal system.

Securing the Socio-Cyber World: Multiorder Attribute Node Association Classification for Manipulated Media

With the rapid development of information technology, social network has become an indispensable part of daily life. People have been able to get news from all over the world through social networks for a long time. People spend more time online than they do in real life. However, the information we get in the world of social network is not purely benign. Due to the development of artificial intelligence technology, more and more tampered media information appears in social networks, some for entertainment, while others become the dark side of social networks, of which the most harmful is to people in the media tamper. For fake news and misinformation caused by media tampering, we need to trace the source and clearly distinguish the truth from the manipulated. This article proposes an image media forgery classification method of multiorder attribute nodes. First, we use different methods to extract the edge, texture, grayscale, and color attributes of the image. Second, according to the characteristics of different attributes, we calculate the first-order entropy of edge attributes, the second-order entropy of texture attributes, local entropy of grayscale, and color properties. Finally, we represent each image with some nodes and build a graph convolutional network (GCN) to classify real and fake images. Experimental results on mainstream media manipulation datasets show that our method is the state-of-the-art compared with similar methods.

Artificial Intelligence in Healthcare 5.0: Strengthening Practices for Medicos

Abstract: From the beginning, the Healthcare sector stood a pioneer for the development of Artificial Intelligence technology. Numerous research and discussion have been conducted till today based on the idea of Artificial Intelligence. This is because of the nature of the amenities and the susceptibility of ample portion of consumers. In the present study, a blended approach both qualitative and quantitative has been applied to determine the constituents of Artificial Intelligence for healthcare industry and analysis is conducted to find its effect on value formation along with market performance. By analysis of the patient perspective, it revealed the ways in which different Artificial Intelligence components contribute to healthcare organizations and provide improved patient-centered health care.