Development Of Artificial Intelligence Technology Research Articles

Experience in psychological counseling supported by artificial intelligence technology.

In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare. This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes. Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling. The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling. In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.

TREND IN THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE IN THE WORLD

The article discusses current trends and prospects for the development of artificial intelligence technology. International research data and ratings of leading trends in the digital economy are analyzed. The volume of the global market for AI technologies and the leading countries in terms of the amount of investment in this area have been studied. The purpose of this article is to consider how artificial intelligence can improve the process. The article analyze various techniques that make positive changes to the practice of artificial intelligence and discuss the potential benefits, challenges and ethical issues associated with this development. Using examples of the successful implementation of artificial intelligences, the article shows how technology is changing the way of learning and people are thereby helping to reach new heights.

Open data and explanatory artificial intelligence: legal perspectives

The article comprehensively analyzes the legal issues of open data. The concept of “citizen-generated data” is explored, and its role, place and importance for legal regulation in the open data system are determined. The paper also describes the features that distinguish this category of data from similar “citizen science” and “public participation” data. The process of forming training datasets for the development of artificial intelligence technologies using open data is comprehended. The concept and features of “explanatory artificial intelligence” are investigated. It is established that the development of explanation algorithms should take into account the human thought processes and cognitive biases of the person who forms, perceives and evaluates the decisions of explanatory artificial intelligence. The use of open data in other areas, in particular for analyzing the indicators of the Sustainable Development Goals, is investigated. It was found that some of the data necessary for comparing SDG indicators are published by the same data managers on different platforms. This leads to duplication of some data, the need to transform or compile data for publication on the Open Data Portal and the Open SDG Platform in Ukraine.

Social entertainment robot based on artificial intelligence algorithm application in interactive mode of music classroom

With the rapid development of artificial intelligence technology, in-depth research on the application of social entertainment robots in the field of education has become increasingly important. Therefore, this study aims to explore how to use social entertainment robots based on artificial intelligence algorithms to improve the interactive mode in music classrooms, in order to enhance learning effectiveness and classroom atmosphere. The study introduced the detection algorithm of social entertainment robots and designed a complete detection process for accurately locating and identifying social entertainment robots in music classrooms. A machine data classification detection algorithm has been proposed to accurately determine the behavior and function of social entertainment robots. The social entertainment robot teaching module guides students to actively participate in music classroom activities through simulated conversations and interactive demonstrations. Interactive tests were conducted in actual music classrooms to evaluate the effectiveness of social entertainment robots in the classroom and student feedback. Through experiments, it has been proven that social entertainment robots based on artificial intelligence algorithms can effectively enhance the interactivity and fun of music classrooms. It not only promotes communication and cooperation among students, but also enhances learning participation.

A Fine-Grained Image Classification Method Built on MobileViT

With a rapid development of artificial intelligence technology, fine-grained image classification has gained widespread application. For mobile terminals, this paper introduces an image classification method built on MobileViT, and it can apply into fine-grained image classification. The original MobileViT model has been optimized in three ways. Initially, the h-swish activation function is used to enhance the network performance. Second, the cross-entropy loss function is used to further realize the parameter optimization and model accuracy improvement. Finally, a dropout layer is joined before the fully connected layer can effectively decrease the model recognition time and prevent over-fitting. Experimental data on public tomato disease datasets demonstrate that the improved fine-grained image classification method put forward in this paper exhibits higher classification accuracy, better stability and network generalization ability than other models.

Research on Architectural Design Based on Stable Diffusion

With the rapid development of artificial intelligence technology, its application in the field of architectural design has gradually become a research hotspot. In particular, Stable Diffusion technology shows its unique advantages and potential in the production of architectural renderings, which has attracted wide attention. In this paper, the application of Stable Diffusion technology in architectural creation is deeply discussed, aiming at providing new perspectives and ideas for architectural design and promoting the innovation and development of architectural design process.

Visual Recalibration and Gating Enhancement Network for Radiology Report Generation.

Automatic radiology medical report generation is a necessary development of artificial intelligence technology in the health care. This technology serves to aid doctors in producing comprehensive diagnostic reports, alleviating the burdensome workloads of medical professionals. However, there are some challenges in generating radiological reports: (1) visual and textual data biases and (2) long-distance dependency problem. To tackle these issues, we design a visual recalibration and gating enhancement network (VRGE), which composes of the visual recalibration module and the gating enhancement module (gating enhancement module, GEM). Specifically, the visual recalibration module enhances the recognition of abnormal features in lesion areas of medical images. The GEM dynamically adjusts the contextual information in the report by introducing gating mechanisms, focusing on capturing professional medical terminology in medical text reports. We have conducted sufficient experiments on the public datasets of IU X-Ray to illustrate that the VRGE outperforms existing models.

Understanding AI innovation contexts: a review and content analysis of artificial intelligence and entrepreneurial ecosystems research

PurposeAn emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.Design/methodology/approachWe first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.FindingsWe identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.Originality/valueWe increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.

An improved image texture synthesis based on algorithm convolution neural network

In recent years, texture synthesis technology has made great progress and has become one of the research hotspots in the fields of computer graphics, computer vision and image processing. Texture synthesis is proposed to solve problems such as seam aliasing in texture mapping. With the development of artificial intelligence technology, texture synthesis methods can achieve more effective results. Therefore, this paper improves the traditional texture synthesis method based on convolutional neural network. By using the VGGnet network model of the convolutional neural network and adding the Batch Normalization (BN) layer to individual convolutional layer affiliated to the network, the network training speed is improved. In this paper, the source texture image and an image initialized by white noise are respectively input into the convolutional neural network. After the forward propagation process, the texture features of the images are extracted, the loss function is constructed, and then the network is trained to update the composite image. Extensive results of experiments indicate that our proposed approach outperforms other texture synthesis methods in terms of image synthesis quality.

Bots and Books: How Artificial Intelligence is Shaping Contemporary Literature

The coming of the robotized millennium has brought about a period of time in which artificial intelligence (AI) has a significant impact on many facets of existence for humans, including the literary arts. This research paper explores the various ways that AI is changing the field of literature. AI has had a significant and all-encompassing impact on literature, from the development of new forms of literary expression to the improvement of conventional writing techniques. The examination of literature produced by AI takes up a large amount of the paper. It highlights noteworthy endeavors and projects that use AI to produce literary works, from poetry collections to novels in their entirety. The paper explores the distinctive features of AI-generated literature, such as its stylistic inclinations, thematic concerns, and structural patterns, through case studies of texts written by AI. It also discusses how these works have been received critically, illustrating the various viewpoints held by readers, academics, and literary critics regarding the significance and uniqueness of texts created by artificial intelligence. The collaborative potential of AI in the process of creating literature is further examined in this work. It looks at how modern poets and writers are using AI tools to improve their creative processes, from coming up with ideas and getting beyond writer's block to trying out novel narrative structures and styles. The study offers insights into the useful applications and advantages of artificial intelligence (AI) in literature through surveys and interviews with authors who have used AI into their creative processes It looks forward to more developments in AI technology and how they can transform the literary industry. It provides a nuanced view of the future of literary creativity in the robotized millennium by highlighting the opportunities and problems posed by AI in the literary arena. The research advances knowledge about AI's influence on the direction of literature by combining historical study, current case studies, and futuristic conjecture.

Artificial intelligence and internet of things in manufacturing decision processes

This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.

ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ ДЛЯ РОЗПІЗНАВАННЯ ТЕРОРИСТИЧНИХ ТА ВОРОЖИХ ВІЙСЬКОВИХ ОБ’ЄКТІВ

The development of artificial intelligence technologies and their application in solving military tasks is the main concept of scientific advancement in Ukraine. Despite the availability of existing solutions for classifying limited types of civilian objects, there is a need to develop new technologies to gain an advantage over the enemy. Existing artificial intelligence models available in the public domain are oriented towards diagnosing restricted types of civilian objects. This limitation prevents the use of these models for analyzing military enemy equipment. Automating the process of recognizing enemy military objects by constructing a deep learning classification model. The process of building a classification model for enemy military equipment using deep neural networks is considered. The Sequential model type is chosen as the basis, which includes batch normalization layers added after each convolutional layer before further processing. Accelerated hyperparameter search is provided by the Random Search tool. The quality of classification models was measured using classical accuracy metrics. The implementation of the proposed solutions is carried out using the Python programming language and the Python Imaging Library, TensorFlow, Keras Tuner, and Matplotlib libraries. A convolutional neural network classification model has been constructed capable of classifying enemy military tanks and vehicles. The model demonstrates the best accuracy metrics on training/testing datasets of 0.6/0.55 with errors of 0.658/0.705, respectively. A comparative analysis of classification models with different types of architectures showed the advantage of the model in terms of accuracy and error rate when using an architecture that includes batch normalization layers. The created classification model is recommended for further training on new data or for solving computer vision tasks.

Artificial Intelligence Ethics: A Dialogue between Technological Advances and Human Values

The rapid development of artificial intelligence technology has already had a profound impact in various fields, ranging from healthcare and education to transport and finance. However, accompanying these technological advances are a series of complex and profound ethical issues. These issues involve not only data privacy and security, but also challenges of algorithmic bias, fairness, and transparency in decision-making. In addition, the ‘black box’ nature of AI systems blurs the attribution of responsibility and increases society's distrust of the technology. As AI is increasingly used in society, the question of how to find a balance between technological innovation and human values has become an urgent one. While technological advancement can certainly bring efficiency and convenience, the lack of ethical constraints may lead to privacy leakage, unfair decision-making and moral hazard. Therefore, it has become particularly important to establish a sound AI ethical framework to regulate the application of the technology, protect individual privacy, and ensure fairness and transparency. The establishment of an AI ethical framework is not only to regulate the application of the technology, but also to protect social justice and core human values. Through systematic ethical guidelines, moral considerations can be integrated into all stages of technology development and application, providing clear guidelines to help all parties use AI technology under the premise of legal compliance. At the same time, such an ethical framework can also help enhance public trust in AI and promote the healthy development of the technology in a wider range of fields. In conclusion, the rapid development of AI brings unprecedented opportunities and raises profound ethical challenges. We need to ensure the coordinated development of technological progress and social values by establishing a sound ethical framework, and promote AI to move forward in a more responsible, fairer and transparent direction. The combination of ethics and technology will become an important force to lead the future development of science and technology, bringing more benefits and progress to human society.

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

EDUCATION IN THE ERA OF ARTIFICIAL INTELLIGENCE: AXIOLOGICAL STUDY

Education in the era of Artificial Intelligence (AI) presents new challenges in the axiological aspect, namely the moral and ethical values related to the use of AI technology in the learning process. This article aims to conduct an axiological study of education in the era of artificial intelligence with a focus on understanding the values that emerge along with the development of AI technology and their implications in the educational context. The research approach used is literature analysis to explore understanding of the use of AI in education and its implications for moral and ethical values. The results of the study show that the use of AI technology can provide significant benefits in increasing the effectiveness and efficiency of learning, but also raises several axiological problems. This research is qualitative research involving literature analysis, the data obtained will be processed thematically. In its conclusion, this article emphasizes the need to develop axiological awareness in education in the era of artificial intelligence. By paying attention to moral and ethical values, education can be an effective forum for developing a generation that is technologically intelligent and has a strong axiological awareness. This effort needs to be supported by critical thinking, reflection, and collaboration between educators, stakeholders and related parties to ensure that the application of AI technology in education runs ethically and responsibly.

Research on Copyright Recognition of Content Generated by Artificial Intelligence

With the rapid development of artificial intelligence technology, artificial intelligence has developed into "expressive artificial intelligence", artificial intelligence-generated content (AIGC) is more and more widely used in various fields. However, there are still some disputes and confusion about the copyright ownership of these machine-generated content. This paper first introduces the basic concepts and characteristics of artificial intelligence-generated content under the current background. Secondly, this paper discusses the positioning of AIGC in the copyright law and the difficulties in protecting the rights and interests through the different views and legislative practices on the copyright recognition of artificial intelligent-generated content in the world. Finally, in view of the current disputes, such as "creative requirements" and "human participation", this paper puts forward the possible ways to solve this problem in the future, including improving the copyright law to clarify the right ownership and responsibility of AIGC, learning from foreign experience, and establishing the copyright ownership system of AIGC, etc., which provides a useful reference for the formulation and practice of relevant laws and regulations.

Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety

BackgroundArtificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of health is also one of the areas where artificial intelligence technologies are widely used. For this reason, it is considered important that healthcare professionals be prepared for artificial intelligence and do not experience problems while training them. In this study, midwife and nurse candidates, as future healthcare professionals, were discussed. AimThis study aims to examine the effect of the artificial intelligence readiness on the artificial intelligence anxiety and the effect of artificial intelligence characteristic variables (artificial intelligence knowledge, daily life, occupational threat, artificial intelligence trust) on the medical artificial intelligence readiness and artificial intelligence anxiety of students. MethodsThis study was planned and carried out as a relational survey study, which is a quantitative research. A total of 480 students, consisting of 240 nursing and 240 midwifery students, were included in this study. SPSS 26.0 and AMOS 26 package programs were used to analyse the data and descriptive statistics (frequency, percentage, mean, standard deviation) and path analysis for the structural equation model were used. ResultsNo significant difference was found between the medical artificial intelligence readiness (p=0.082) and artificial intelligence anxiety (p=0.486) scores of midwifery and nursing students. The model of the relationship between medical artificial intelligence readiness and artificial intelligence anxiety had a good goodness of fit. Artificial intelligence knowledge and using artificial intelligence in daily life are predictors of medical artificial intelligence readiness. Using artificial intelligence in daily life, occupational threat and artificial intelligence trust are predictors of artificial intelligence anxiety. ConclusionMidwifery and nursing students' AI anxiety and AI readiness levels were found to be at a moderate level and students' AI readiness affected AI anxiety.

Born for marketing? The effects of virtual versus human influencers on brand endorsement effectiveness: The role of advertising recognition

The development of artificial intelligence technology has led to the creation of virtual influencers that have been embraced by marketing professionals. Starting with advertising recognition, a variable that directly affects marketing effectiveness, this study investigates whether virtual influencers lead to different levels of ad recognition among consumers from human influencers. The present study's three experiments demonstrate that (1) compared to human influencers, virtual influencers have a significantly greater impact on consumers' identification of advertisements; (2) the impact of influencer type on consumers' brand attitudes is mediated by the their advertising recognition; (3) the level of consumers' perceived sensory capacity of virtual influencers negatively moderates the effect of virtual influencer's post on consumers' advertising recognition. These research findings have significance for developing virtual influencer marketing in practice.

Research on the Concept, Architecture and Construction Strategy of Intelligent Language Laboratory

With the rapid development of artificial intelligence technology, smart language laboratory plays an increasingly important role as an important research platform in the field of artificial intelligence. This paper discusses the concept, characteristics and functions of the smart language laboratory, and analyzes the architecture of the smart language laboratory, including the construction of hardware facilities, software system, data resources and talent team. Meanwhile, the construction strategy of smart language lab is proposed, including construction planning, technology selection, team building and operation mode. The practical experience of smart language labs at home and abroad is analyzed through case studies, and the development trend and future outlook of smart language labs are summarized.

Characteristics of Victimological Problems Caused by the Development of Artificial Intelligence Technologies that Contribute to the Growth of Victims of Unfavorable Socialization Conditions

The relevance of the issue under consideration is due to the fact that the growth of new technologies, on the one hand, gives rise to a number of new opportunities to improve the quality of life, while exposing modern society to serious risks . The manifestation of these risks increases significantly with the use of artificial intelligence technologies, which are rapidly being implemented due to the high technocratization of many spheres of life in modern digital society . In this regard, the subject of the study was the potential risks arising from the introduction of these technologies . The goal was to study the impact of processes associated with the introduction of artificial intelligence technologies on society, to analyze the causal complex that contributes to the transformation of the population in the process of their use into victims of unfavorable conditions of socialization, as well as to make proposals aimed at minimizing the victimization of citizens . The methodological basis lies in the analysis of the most dangerous risks and vulnerabilities in the process of introducing artificial intelligence technologies, their causal complex, followed by extrapolation in the future of the identified negative trends . The results of the study indicate that the potential for harm caused during the implementation of these technologies can give rise to both completely calculated threats and a number of new risks and vulnerabilities, which, according to some experts, are unlikely, but the scale of the harm caused from which may be significant . The author has submitted for wide scientific discussion a completely new problem of an intersectoral nature, which is associated with the victimological aspects of the introduction of artificial intelligence technologies . The main emphasis is on the need to ensure the protection of victims arising after the introduction of new technologies. The author focuses on the fact that in the process of using the technologies under consideration, more and more people will be exposed to various risks and threats; accordingly, their implementation acts as one of the factors in the victimization of society . In this regard, it is necessary to resort to strict legal regulation of social relations related to artificial intelligence technologies . The area of implementation of the results of this research is such that the findings can be used in the development of program measures to counter the threats of the introduction of artificial intelligence, which contribute to the transformation of the population into victims of unfavorable conditions of socialization.