AI Concept Research Articles

Ai Art In Mobile: Topic Modeling Of User Reviews For Generative Ai Applications

The generative AI concept, which enables users to create text, image, and video content through prompts, is revolutionizing the content side and AI applications in marketing. Despite the increasing popularity of generative AI applications, the market perception regarding generative AI remains underexplored. This study aims to explore the generative AI market perception through the context of mobile applications with the help of user reviews. The study follows a structured approach including identifying the generative AI mobile applications, assessing the context through rating scores and install amounts of mobile applications, and using a topic modeling approach (BerTopic) for online reviews to identify the topics included in the conversation. 8159 user reviews from 22 mobile applications are used as sample of the study and the average rating score for the sample found as 4,06 which signals a positive perception of market. The study concludes top ten topics as; “Dissatisfaction About Amount of Advertisements in app”, “NSFW Content and Moderation”, “Praise of Application”, “Functionality Problems & Crashes”, “Payment Necessity and Trial Problems”, “In-App Purchase Restoration Problems”, “Specific Feature in App”, “Chat Function”, “Credit System” and “Excess of Ads”. The study reveals the main issues of Ai Art mobile applications for marketing decision-making processes.

Teaching of reflection in higher education: a narrative review.

Healthcare professional students (HCPs) are encouraged to utilise reflection during and after their study programmes as a part of their life-long learning skill and professional competencies. However, the way in which the concept of reflection is taught and its' influence students' capacity to reflect have not been fully explored. This narrative review aimed to explore how the concept of reflection is taught in higher education and how the teaching of reflection influences HCP students' capacity to reflect. Articles that were published during 2014-2024 within three databases, PubMed, CINAHL and ERIC were searched. 1929 articles were eligible for screening. 93 articles were further assessed for eligibility. 18 articles were included. The included articles were geographically well-distributed in both the Global North and Global South countries, indicating universal interest in the topic. Eight articles had qualitative study designs; six had quantitative designs and four had mixed-method designs.Conceptually, the teaching of reflection could be categorised as a spectrum, ranging from: i) structured format (reflective templates or debriefing); ii) semi-structured format (physical/virtual small group discussions, video recordings, AI generative arts, in-verse reflection and concept mapping) and iii) flexible and creative (art-based pedagogy or narratives).All included articles indicated students actual and perceived better understanding of reflection. This claim is supported both quantitatively and qualitatively via either validated instruments or narratives and themes based on students' textual outputs. This review identified several teaching methods that help facilitating students' capacity to reflect. Findings are unable to recommend the most efficient way to teach reflection since it depends on students' progress in their programmes. However, a more relational approach to teaching of reflection is recommended. Students might begin their reflective journey with a structured format of teaching of reflection then gradually move to less-rigid format of the teaching to empower students' autonomy.

Human-Centered AI in Military Cyber Operations

Military Cyber Operations are an integral part of modern warfare and national security strategies as they crossed the science fiction realm, represent a real operational battlefield, and developed into an option in military toolboxes. Seeing ongoing technological advancements that allow the creation and use of complex mechanisms and technologies, the increasing digitalization of critical infrastructure, the growing abundance of data collected, generated, and exchanged between multiple parties, and the rise of stakeholders engaging in building and/or executing military Cyber Operations together with the increased number of such operations being conducted all over the globe reflects important lessons that need to be learned: the core element of such operations are humans: they build, acquire, execute, and assess them while also being impacted entities by them, e.g., through (psychological or physical) injury or death, or damage or destructions of human infrastructure. Moreover, the key process governing all the life cycle phases of military Cyber Operations is human decision-making or humanly assisted/augmented decision-making relying on advanced intelligent methods built with AI. Nevertheless, building and conducting military Cyber Operations should be done in a legal, responsible, and effective way implying a deep understanding of the context and adversary, proper target and cyber weapon selection, development, and use, and a clear overview of potential effects produced. These represent important aspects that should be properly defined and tackled in this domain. Hence, this research aims to introduce the Human-Centred AI concept and approach in the military cyber domain to illustrate ways to prioritize human involvement and interaction, human understanding, effective decision-making, and ethical considerations when building and conducting military Cyber Operations. To this end, an extensive literature review is conducted in the military, cyber, and AI domains together with instantiation on military Cyber Operations.

The Success of the K-pop Industry in Utilizing Social Media to Promote Korean Culture

This paper explores the role of social media theories in driving the expansion of K-pop and its effective promotion of South Korean culture. The study delves into two key theories: the S.N.S. Citizenship Behavior theory and the Selectivity, Transactionality, and Conditionality Paradigm. These theories are dissected to elucidate their functionality and the benefits they bring to both users and social media platforms. To illustrate these concepts, the paper presents case studies featuring Aespas AI concept, (G) I-DLEs girl power concept, New Jeanss Phoning application, and a hypothetical scenario involving YouTube. The findings suggest that while targeted social media strategies contribute to a broader appreciation of Korean culture, social media platforms must invest further in enhancing their promotional systems and algorithms. In conclusion, this study asserts that the synergy between K-pop and social media will continue to flourish, with the potential for further growth as social media platforms refine their support mechanisms.

A fine-tuned tourism-specific generative AI concept

A fine-tuned tourism-specific generative AI concept

Research on momentum strategy and contrarian strategy in AI stock prediction

The emergence of ChatGPT has significantly enhanced the recognition and acceptance of artificial intelligence concept stocks within the Chinese stock market. Nevertheless, the short- and long-term fluctuations in the prices of AI companies remain uncertain. Therefore, the purpose of this research is to determine optimal strategy for evaluating the suitability of the contrarian strategy versus the momentum strategy in predicting the stock prices of AI concept stocks in the Chinese stock market. Based on a cross-comparison of the Chinese financial data sources iFinD and Wind Economic Database (EDB), this study collects the price data of AI concept stocks over the past six months, starting from the date of ChatGPT's publication. This study employ Python to model stock price movements for both the momentum and reversal strategies. The goodness of fit is evaluated by comparing the modeled stock prices with the actual stock prices. This study demonstrates that the momentum strategy exhibits greater explanatory power than the contrarian strategy, accurately predicting 84.21% of artificial intelligence concept stocks. However, other studies suggest that while AI concept stocks continue to rise, momentum strategies remain effective, whereas when market sentiment cools down, contrarian strategies become more suitable for Chinese AI concept stocks. Hence, in China, the effectiveness of these strategies may vary depending on the prevailing market conditions.

Mutual Education Guidance by using AI

Well, we all know Artificial intelligence is blooming everywhere so, we tried to use this wonderful Technology to enlight every student's life. Our software is an ultimate combo of AI and Assistance which gonna bring a revolution in educational field. This could be an alternative for the conventional educational system. We using the application form(login) in online for students based on the marks by using AI concept it used to analyze the student face of interest.The main scope of this project is reduce the student time and it is used to create flexible study schedule based on the time given by the user in recent technology. We used here “Buddy” concept with AI to growth of student study.

VISHIEN-MAAT: Scrollytelling visualization design for explaining Siamese Neural Network concept to non-technical users

The past decade has witnessed rapid progress in AI research since the breakthrough in deep learning. AI technology has been applied in almost every field; therefore, technical and non-technical end-users must understand these technologies to exploit them. However existing materials are designed for experts, but non-technical users need appealing materials that deliver complex ideas in easy-to-follow steps. One notable tool that fits such a profile is scrollytelling, an approach to storytelling that provides readers with a natural and rich experience at the reader’s pace, along with in-depth interactive explanations of complex concepts. Hence, this work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical users. As a demonstration of our design, we created a scrollytelling to explain the Siamese Neural Network for the visual similarity matching problem. Our approach helps create a visualization valuable for a short-timeline situation like a sales pitch. The results show that the visualization based on our novel design helps improve non-technical users’ perception and machine learning concept knowledge acquisition compared to traditional materials like online articles.

Exploration on the Curriculum System and Mode of Innovative Talents Training in Big Data Specialty Based on AI Concept

The rapid development of big data and cloud computing technology, artificial intelligence (Artificial Intelligence, AI) technology, and wireless sensor technology provides support for the digital transformation of various industries and industrial economies in society, and also puts forward the comprehensive quality of big data professionals. Higher requirements. Starting from the “AI concept” of the integration and development of “big data + artificial intelligence”, this paper explores the problems of unclear educational objectives, imperfect teaching system, outdated teaching knowledge content and insufficient innovation of teaching mode in the current big data professional courses in colleges and universities, and puts forward the “1 + X” open teaching system with big data professional teaching materials as the main part and other subject teaching as the supplement, the establishment of network Massive Open Online Course (MOOC) platform, school-enterprise integration practice training base to deepen the orientation education of big data professional students, the formation of big data curriculum theory, employment application practice integration teaching mode, so as to achieve the goal of comprehensive application ability training of big data professionals.

중등 예비교사의 AI 교육에 대한 인식, 요구 및 AI 교수효능감 분석

Objectives The purpose of this study is to suggest direction for the AI education plan at the university of education through the analysis the perception, requirement for AI education and AI teaching efficacy of secondary pre-service teachers.
 Methods The survey was conducted by sampling students from K University's College of Education in Gyeongsang nam-do by grade and major. As a research tool, development questions in the areas of AI interest, understanding, and need through review of previous studies, and artificial intelligence teaching efficacy measurement tools (AI-TEBI) were used. Finally, 198 respondents were analyzed. Chi-square test, independent t-test, and one-way ANOVA were used to analyze differences by gender, major, and grade.
 Results First, the level of understanding of AI education was low, and the level of awareness of interest and necessity was high. Second, the demand for AI utilization education was higher than understanding education, and there were differences by major and grade. Third, among the sub-elements of AI teaching efficacy, individual teaching efficacy on AI and interaction with AI were low, and expectations for AI concept recognition and AI teaching results were high.
 Conclusions The AI education experience of secondary pre-service teacher affected the positive perception of AI education. According to this result, it is necessary to operate an appropriate systematic curriculum of AI understanding and utilizing education to enhance AI teaching efficacy by major and grade. The results of this study will be used as basic data to set a plan how to conduct AI education for all majors in college of education.

Capital or Technology? Which Is Better at Promoting the Value of AI Companies—Theoretical Analysis and Empirical Test

Capital and technology gradually replace land and labor factors to become the important production factors of enterprises, especially for artificial intelligence enterprises. How to use capital and technology factors to enhance enterprise value becomes an important topic when artificial intelligence enterprises are affected by internal and external factors. Using data from 55 AI concept companies, a multiple regression model was constructed to verify the mediating role of capital and technology, judge the dynamics of the two elements dialectically, and explore the paths that shape the value enhancement of AI companies. The study found that both degree of enterprise attention and government support positively affect firm value, and that the mediating role of both capital and technology is more pronounced. However, compared to the technology element, the capital element plays a leading role in the value enhancement mechanism of AI companies, with physical capital being the most effective, and strengthening the capital element is a more sensible path choice to increase the value of companies in the short term. However, in the long term, as the business cycle and the external environment change, the technology element is a driving force that cannot be underestimated.

Effective use of AI search and concept search in patent search

Effective use of AI search and concept search in patent search

Wearable Edge AI Applications for Ecological Environments.

Ecological environments research helps to assess the impacts on forests and managing forests. The usage of novel software and hardware technologies enforces the solution of tasks related to this problem. In addition, the lack of connectivity for large data throughput raises the demand for edge-computing-based solutions towards this goal. Therefore, in this work, we evaluate the opportunity of using a Wearable edge AI concept in a forest environment. For this matter, we propose a new approach to the hardware/software co-design process. We also address the possibility of creating wearable edge AI, where the wireless personal and body area networks are platforms for building applications using edge AI. Finally, we evaluate a case study to test the possibility of performing an edge AI task in a wearable-based environment. Thus, in this work, we evaluate the system to achieve the desired task, the hardware resource and performance, and the network latency associated with each part of the process. Through this work, we validated both the design pattern review and case study. In the case study, the developed algorithms could classify diseased leaves with a circa 90% accuracy with the proposed technique in the field. This results can be reviewed in the laboratory with more modern models that reached up to 96% global accuracy. The system could also perform the desired tasks with a quality factor of 0.95, considering the usage of three devices. Finally, it detected a disease epicenter with an offset of circa 0.5 m in a 6 m × 6 m × 12 m space. These results enforce the usage of the proposed methods in the targeted environment and the proposed changes in the co-design pattern.

Searching for Common Ground: Conceptualizing Artificial Intelligence

Artificial Intelligence is all around us. Many legal scholars have taken up to the challenges posed by AI technology and its interaction with our laws and regulations. In addressing the legal issues, one area seems to be less developed than others – the concept of AI and how the development of the AI term might affect the legal debate. In this paper, I outline significant milestones in the development of the AI term looking for answers and reasons for the path that was laid out for us in the early 1940s and 1950s by Alan Turing and others. I further argue for rethinking the AI concept for the legal debate offering some conceptual changes that would befit our technological advancements and social change.

A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks

Cognitive computing, a revolutionary AI concept emulating human brain's reasoning process, is progressively flourishing in the Industry 4.0 automation. With the advancement of various AI and machine learning technologies the evolution toward improved decision making as well as data-driven intelligent manufacturing has already been evident. However, several emerging issues, including the poisoning attacks, performance, and inadequate data resources, etc., have to be resolved. Recent research works studied the problem lightly, which often leads to unreliable performance, inefficiency, and privacy leakage. In this article, we developed a decentralized paradigm for big data-driven cognitive computing (D2C), using federated learning and blockchain jointly. Federated learning can solve the problem of “data island” with privacy protection and efficient processing while blockchain provides incentive mechanism, fully decentralized fashion, and robust against poisoning attacks. Using blockchain-enabled federated learning help quick convergence with advanced verifications and member selections. Extensive evaluation and assessment findings demonstrate D2C's effectiveness relative to existing leading designs and models.

Artificial Intelligence as an Object of Statistical Study

The article addresses the questions regarding organizing a statistical study of one of the most dynamic sectors of the economy, based on the use of artificial intelligence AI. Given the novelty and fundamental nature of the topic, the paper focuses on the subject of artificial intelligence, understanding its essential aspects, economic nature, and its driving force for development in order to have clear guidelines for their reflection in current statistical accounting and reporting. The article demonstrates the contradictions of AI development, duality, and the variability of building strong and weak AI models.The methodological basis for the statistical study of artificial intelligence is provided by the basic OECD Guidelines for big technologies such as biotechnology, nanotechnology, information, and communication technologies. To be specific, the paper elaborates on science and research, technology and technology base. The author outlines the main technical questions related to statistical observation: basic and list-based definitions of artificial intelligence, identifies characteristics of enterprises and organizations that are subject to statistical accounting, including start-ups. Specific statistical indicators are indicated; are shown drivers and growth segments of the AI market.Specific attention is aimed at the topic of interdisciplinarity. Particularly the author touches upon brief historical background of the origin of the AI concept, nature of weak and strong artificial intelligence, and also shows major trends in worldview transformations. The paper examines areas of concern for the formation of the research potential of universal (strong) artificial intelligence.The author describes the technological aspects of the progress in artificial intelligence, the relevance of its analysis from the perspective of a complex of big technologies, the basic contours of interaction between various fundamental and applied technologies in building the single technology platform for the creation and study of artificial intelligence.With the use of specific statistical materials, the article presents forms of the global and Russian market of artificial intelligence technologies and demonstrates its key growth drivers.

Research on the “Stickiness” Relationship between R & D and Profitablity of “Unicon” Company—Data Based on the AI Concept Stocks

In this paper, based on the sample of the listed company of AI concept stocks from 2015 to 2017 as the panel data, research shows that R & D capital investment is negatively correlated with the profitability of the AI enterprise, and there is a lag effect. And R & D personnel inputs have nothing to do with profitability. AI listed companies have the problem of “investing more and output less” and limiting role of technical personnel. In this case, It’s necessary for AI enterprise to strengthen their core technical capacity, allocate resources reasonably and play a long-term role in promoting profitability through R & D.

Augmented Intelligence and Nursing.

With each new year, numerous reports, columns, and blogs are written to project the upcoming technologies and trends in health care. Several 2017 technology trends point to the growing use of intelligence (AI) in health care. Gartner's Top 10 Strategic Technology Trends for 2017 (Cearley, Walker, & Burke, 2016) highlight three top trends: A and advanced machine learning, apps, and intelligence things. Let's look at each.First, certain technologies and specific techniques, such as deep learning, neural networks, and natural language processing, are encompassed within the AI and machine-learning concept. These techniques create software programs that are more than just rulebased systems. Rather, these can understand, learn, predict, adapt and appear intelligent (Cearley et al., 2016). Their ability to learn is key to their functionality. For example, machine-learning system can analyze numerous electronic health records (EHRs) and recommend potential effective treatments. As more datasets are added, the system can learn and adapt the recommendations, for example, adding genomic data to the EHR database.Intelligent apps, the second trend, refer to virtual personal assistants (VPAs). VPAs help users with everyday tasks, for example, sorting email or answering simple questions (just as Siri™ and Cortana™ do on our smartphones). VPAs become more available in health care in the coming year. Intelligent things, the third trend, break down into three distinct categories: robots, drones, and autonomous vehicles.In discussing health information technology trends for 2017, Health Data Management (2016) also named AI as the first trend, stating that, although AI exploded in health care in 2016, applications were typically very specialized. More general use is projected, which will mean better access to actionable intelligence. Padmanabhan (2016) echoed this notion: In 2017, we are more likely to hear terms such as 'cognitive computing' and 'artificial intelligence' „.and less likely to hear the term 'big data analytics,' which now seems to be limiting in its description of the actual work being done in advanced analytics.AI BASICSThe term intelligence is not new. It dates back to the 1940s and 1950s, and Turing (1950) asked if machines can think. AI was even used in health care in the 1970s, for example, with system called MYCIN, developed by Stanford University, that identified bacterial infections and recommended treatments (Shortliffe, 1976). MYCIN contained three components: knowledge base created by experts, an inference engine with rule-based algorithms, and user interface. Although there were other instances of expert system designed in that era, there was never critical mass of users to adopt them for clinical practice. Most health care professionals did not see the need for machines to tell them how to practice.Circumstances have changed. According to the Executive Office of the President, National Science and Technology Council Committee on Technology (2016, p. 6), the current motivation for AI was driven by three mutually reinforcing factors: the availability of big data.. .dramatically improved machine learning approaches and algorithms „and the capabilities of more powerful computers.COGNTIVE COMPUTINGCognitive computing (CC) is an emerging term that some view as subset of AI. Kelly (2016, p. 1) believes that the future oftechnology be and not artificial and defines CC in terms of systems that learn at scale, reason with purpose and interact with humans naturally. One other distinguishing factor is that CC can handle unstructured data, whereas AI applications are typically based on structured or numeric data. Marr (2016) summarizes CC as a mashup of cognitive science - the study of the human brain and how it functions - and computer science, and the results have far-reaching impacts on our private lives, healthcare, business, and more. …

Welcome to AI matters

Welcome to AI Matters , the new quarterly newsletter for SIGAI, the ACM Special Interest Group on Artificial Intelligence. This newsletter features ideas and announcements of interest to the AI community. These include: Book Announcement: Description of a newly published book and its major contributions. Dissertation briefings: Extended abstracts from new Ph.D.s. Event reports: Technical conference or workshop reports, policy forums, or community events on topics of general interest to an AI audience. AI Impact: Description of an AI system or method that has had a tangible impact on the world outside of the AI research community. AI News: Innovations, open source AI software, course materials, challenges and competitions, and other news of broad interest to AI researchers and practitioners. Opinion: Discussion of thought-provoking issues and responses to previous items. Paper Précis: Short summary of the major contributions of a recently published AI paper, written for the general AI audience. Tutorial: Short introduction or explanation of an AI concept or technique. Videos and Images: Audio-visual materials with content of general interest to an AI audience.

A Multi-Agent View of Strategic Planning Using Group Support Systems and Artificial Intelligence

The strategic planning process is dynamic and complex. Including a Group Support System (GSS) in the problem-solving process can improve the content quality of the strategic plan by allowing increased participation by more members of the organization. However, it can also add to the complexity of the problem by increasing the quantity of textual information that can result from group activity. Added complexity increases cognitive overload and frustrations of those participants negotiating the contents of the strategic plan. This article takes a multi-agent view of the strategic planning process. It considers group participants as multiple agents concerned with the content quality of the strategic plan. The facilitator agent is responsible for guiding groups in the strategic plan construction process as well as for solving process problems such as cognitive overload. We introduce an AI Concept Categorizer agent, a software tool that supports the facilitator in addressing the process problem of cognitive overload associated with convergent group activities by synthesizing group textual output into conceptual clusters. The implementation of this tool reduces frustrations which groups encounter in the process of classifying textual output and provides more time for discussion of the concepts themselves. Because of the large amount of convergent activity necessary for strategic planning, the addition of the AI Concept Categorizer to the strategic planning process should increase the quality of the strategic plan and the buy-in of the participants in the strategic planning process.