Design and Implementation of Intelligent Data Analysis Platform

With the gradual maturity of artificial intelligence (AI) technology, it is urgent to change and innovate the traditional media communication paradigm. Current research and application areas include language processing, image recognition, AI systems, intelligent video technology, and text recognition. With the rapid development of artificial intelligence technology, it is used in all walks of life in society. All sectors of society have also set off the exploration and application of artificial intelligence. In the field of news communication, artificial intelligence technology brings opportunities and challenges to the news industry in publishing news content forms and organizations. Exploring the new development trends and countermeasures of the news industry in the era of artificial intelligence has become a key topic for the news communication industry and academia. With the support of the huge communication network, the information transmission of short video news is more abundant. While people obtain information, it can also enhance the interactivity of the news. In a fast-paced society, short video news is undoubtedly closer to life. However, due to some reasons, human factors lead to incomplete access to information. In this paper, an intelligent neural network is proposed to be applied to an intelligent robot or intelligent system so that it can better obtain information to edit short video news and disseminate it, and compare it with the existing such machines or systems on the market today. This paper analyzes the development of short video news dissemination based on artificial intelligence and proposes improvement strategies based on specific examples.

With the rapid development of artificial intelligence (AI) technology, the field of education has ushered in new opportunities and challenges. Especially in college English teaching, the application of AI provides new possibilities for improving the teaching effect. This paper aims to explore how to effectively improve the quality of college English teaching in the context of AI. By analyzing the current situation and problems in college English teaching and combining them with the development of AI technology, this paper puts forward corresponding strategies and schemes, aiming to provide reference for college English teaching.

Introduction: In todays rapidly changing technology, artificial intelligence (AI) is changing the way we live, work and create art at an unprecedented speed. As an important part of human culture, dance art has also ushered in new development opportunities and challenges in this scientific and technological revolution. Objectives: This paper aims to explore the innovative application and influence of artificial intelligence (AI) in dance creation and performance. With the rapid development of AI technology, its application is increasingly wide in the field of art, and dance art is no exception Methods: This paper first outlines the application of AI technology in dance creation, including the generation of new dance movements through algorithms, auxiliary choreography, etc. These applications provide unprecedented creative sources and tools for dance creators. Then, this paper analyzes the role of AI in dance performance, such as the accurate movement performance of dance robot, the immersive experience of virtual reality technology in dance performance, etc. Results: These technologies not only enrich the form of dance performance, but also broaden the viewing experience of the audience. In addition, this paper also discusses the application of AI in dance teaching, such as intelligent guidance system, distance dance teaching, etc., which bring convenience and personalization to dance teaching. However, the application of AI in the field of dance also faces technical challenges and art ethical issues, which need further research and discussion. Conclusions: In conclusion, this paper believes that the application of AI technology in dance creation, performance and teaching has broad prospects, but it also needs to pay attention to its potential impact and challenges to promote the harmonious coexistence of dance art and AI technology. The research of this paper provides new perspectives and thoughts in the field of dance art, Provides a reference for future research and practice.

This article provides a brief overview of the application and development of artificial intelligence (AI) in the field of education under the background of New Liberal Arts. It also highlights the importance and current status of innovative approaches in college English teaching. Furthermore, it presents the research objectives and methods. The application of artificial intelligence in education has witnessed significant growth and advancement in recent years. AI technologies, such as machine learning and natural language processing, have been utilized to enhance various aspects of education, including personalized learning, intelligent tutoring systems, and automated grading. These applications have shown promising results in improving students' learning outcomes and engagement. In the context of college English teaching, innovation is crucial to meet the evolving needs and expectations of students. Traditional teaching methods often fail to cater to individual learning styles and preferences, leading to limited effectiveness and student disengagement. Therefore, there is a pressing need to explore innovative approaches that leverage AI technologies to create more interactive and personalized learning experiences. The research aims to investigate the potential of integrating AI into college English teaching and its impact on students' learning outcomes and engagement. The study will employ a mixed-methods approach, combining quantitative analysis of students' performance data with qualitative interviews and surveys to gather comprehensive insights. By examining the effectiveness and challenges of AI integration in English teaching, this research seeks to provide valuable recommendations for educators and policymakers to enhance the quality of college English education. Above all , this study aims to explore the application of artificial intelligence in college English teaching, considering its potential to improve learning outcomes and engagement. By adopting a mixed-methods research approach, this study intends to provide valuable insights and recommendations for educators and policymakers to promote innovative and effective English teaching practices.

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.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Artificial intelligence (AI) technology, as one of the most sophisticated science and technology in today's world, is increasingly being used to production and life, particularly in the manufacturing business. it demonstrates how artificial intelligence technology is used in mechanical manufacturing, namely in defect detection, quality inspection, enhancing workplace safety, and other areas. Artificial intelligence technology is becoming increasingly important in people's lives as it becomes more widely used in people's daily lives, such as the widespread use of smart dishwashers and smart sweepers, which are the products of the fusion of artificial intelligence and the mechanical manufacturing industry. Indeed, artificial intelligence technology has been widely utilized in the mechanical manufacturing business, which not only ensures production precision, but also enhances job productivity and workplace safety. The rise of artificial intelligence has caused significant changes in the manufacturing industry as a whole. Without exception, the manufacturing industry must rely on AI technology to accomplish automation and intelligent development, as well as to improve productivity. Using artificial intelligence to categorize mechanical components, we may propose parts from a based solely on an image or CAD model. To find a necessary component in a machines we must currently browse through a catalogue and be able to discern which part you want based on the available possibilities and your understanding of the catalogue. There are serial numbers to memorize since a single digit or character change might indicate a different sort of part. The algorithm will choose which sections are the best and will significantly facilitate our search.

The escalation of security challenges in the context of digital transformation highlights the need for a systematic review of current practices, risks and the potential for implementing artificial intelligence in law enforcement activities. The aim of this study was to summarise scientific approaches to the application of artificial intelligence in law enforcement, focusing on the stages of its development, key areas of research and insufficiently studied aspects. The use of methods of analysis and synthesis of scientific sources, content analysis, comparative analysis, and classification of existing approaches made it possible to assess the current state of scientific research on trends, challenges, and prospects for the use of artificial intelligence. It has been established that scientific interest in the application of artificial intelligence in law enforcement has increased significantly over the last decade. The rapid development of artificial intelligence technologies has opened up new opportunities for the automation of analytical and operational functions, prompting scientists to study the possibilities and threats of artificial intelligence. Researchers focus primarily on areas such as video analytics, crime prediction, image recognition, and big data processing. At the same time, there is a lack of in- depth interdisciplinary research that takes into account the ethical, legal, and social implications of using such technologies. A disparity in approaches to risk classification and standardisation of implementation practices has been noted. The need for the formalisation of research has been demonstrated, which will contribute to the balanced development of artificial intelligence in law enforcement activities, taking into account security, legal, and humanitarian factors. The results obtained can be used by heads of law enforcement agencies, analytical units, and digital transformation specialists to determine priority development directions and consider potential risks

A branch of computer science called artificial intelligence (AI) gives machines the ability to function well and understand complex data. Artificial intelligence research has significantly grown, and it is currently moving more quickly towards applications in education and healthcare. This study shows the potential and limitations of AI in terms of medical and pharmaceutical research. The literature was compiled using specific keywords and phrases such as "Artificial intelligence," "Pharmaceutical research," "drug discovery," "clinical trial," and "disease diagnosis" from sources like PubMed, Science Direct, and Google Scholar in order to choose the research and review articles published within the last five years. This article carefully considered the application of AI in disease diagnostics, digital therapy, individualized treatment, drug discovery, and pandemic or epidemic forecasting. While wearable technology and natural language processing are used to identify patients and monitor the progress of clinical trials, deep learning and neural networks are the most well-known AI technologies. Bayesian nonparametric models are a sensible choice for clinical trial design. Using deep learning and neural networks, the spread of COVID-19, Zika, Ebola, and seasonal flu might all be predicted. According to the scientific community, the development of AI technology may result in more effective and fairly cost pharmaceutical and medical research as well as better public services.

This research examines what impact the Big Data Processing Framework (BDPF) has on Artificial Intelligence (AI) applications within Corporate Marketing Communication (CMC), and thereby the research question stated is: What is the potential impact of the BDPF on AI applications within the CMC tactical and managerial functions? To fulfill the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: management, AI technology and CMC. The findings were analyzed through performing a thematic analysis, where coding was conducted in two steps. AI has many useful applications within CMC, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated communication systems are used in some areas. Based on these findings, the impact of the BDPF on AI applications is assessed by examining different characteristics of the processing frameworks. The BDPF initially imposes both an administrative and compliance burden on organizations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the BDPF requirements. Yet, the BDPF provides flexibility in how to become compliant, which is favorable for AI applications. Finally, BDPF compliance can increase company value, and thereby incentivize investments into AI models of higher transparency. The impact of the BDPF is quite insignificant for the basic forms of AI applications, which are currently most common within CMC. However, for the more complicated applications that are used, the BDPF is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term.

Purpose The purpose of this study is to understand how the integration of artificial intelligence (AI) in advertising and marketing is transforming these fields by improving the efficiency and personalization of campaigns. This study analyses scientific output on the application of artificial intelligence (AI) in marketing communication, identifying the main thematic areas, most prolific authors and institutions, collaboration networks and future trends. Design/methodology/approach A bibliometric analysis was conducted based on 614 articles extracted from the Scopus database and filtered by business, social sciences, and economics areas. Tools such as VOSviewer and Bibliometrix were used to examine scientific output, keyword co-occurrence, thematic evolution and international cooperation networks. Findings Since 2016, scientific output has grown by 34.54% annually, demonstrating strong academic interest. The results reveal a multidisciplinary approach with main topics such as big data, machine learning and the metaverse. Four thematic clusters were identified: (1) AI technologies and data analysis, (2) AI applications in marketing strategies, (3) immersive technologies (AR/VR/metaverse) and (4) ethical and critical dimensions such as disinformation. The United States, China and the United Kingdom lead the way in terms of output, but Latin America is poorly represented, highlighting a gap in regional development and scientific cooperation. Originality/value This study offers a comprehensive overview of the evolution and future prospects of AI in marketing communication. It adds value by identifying gaps in academic research and proposing strategic lines for future research.

With the rising demand for personalized learning and increased digital natives, the traditional education model is under pressure to reform. the rapid development of artificial intelligence (AI) provides new education solutions, and the application of AI is becoming widespread, from intelligent tutoring to automated grading to learning analytics. However, the application of AI in education is not without challenges. This literature review aims to explore the impact of AI on education, analyze its potential advantages, and propose strategies to address the challenges and provide guidance for the future development of education. Understanding the impact of AI on education is instructive for the future direction of AI in education and reveals the importance of AI for education to the extent that it is important for formulating effective educational policies and cultivating future talents in AI technology. The final results of the study show that although AI in the education industry there are various difficulties and limitations, but AI offers great potential for smarter and more efficient education levels and education systems. In today's era, computer programming education is emerging as a pivotal force in shaping the future, as it not only hones students' logical thinking and innovative capabilities but also lays a solid foundation for their career paths in the digital world. By integrating knowledge across various disciplines, programming education stimulates students' enthusiasm for applying their learning to real-world problems and enhances their sense of social engagement, enabling them to tackle challenges through technological means.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

The fast advancement of artificial intelligence (AI) technologies in recent years, especially the deep learning algorithm and transformer neural network, has brought great impacts on scientific researches. As a displine focusing on curation and analysis of life science data, bioinformatics has experienced remarkable changes under the impetus of AI technologies, not only in nowadays, but also throughout its history. In this review, we firstly summarize the historical events of computer-assisted life science data analysis, then assess the features, contributions and changes of AI methods in life science research through bibliometric analysis, and discuss the future challenges for AI methods from the life science research aspects. It is no doubt that AI technologies will continuously accelerate and revolutionize life science research in the future, in the meanwhile, the development of new AI methods more suitable for life science data is also in great needs.