AI Applications Research Articles

Advanced Embedding Techniques in Multimodal Retrieval Augmented Generation A Comprehensive Study on Cross Modal AI Applications

Our study presents significant advancements in the handling of multimodal data through an extended Retrieval-Augmented Generation (RAG) model. By integrating advanced embedding techniques, efficient retrieval mechanisms, and robust generative capabilities, our model demonstrates notable improvements in retrieval accuracy, real-time efficiency, generative quality, and scalability. The retrieval accuracy of our model reached 85%, showing a 10% improvement over existing benchmarks. Furthermore, the retrieval time was reduced by 40%, enhancing real-time application performance. The model's generative quality was also significantly improved, with BLEU and ROUGE scores increasing by 15% and 12%, respectively. These results validate the effectiveness of our approach and its applicability to various AI applications, including information retrieval, recommendation systems, and content creation. Future research directions include the integration of additional modalities and further optimization of retrieval mechanisms to broaden the applicability of our model.

The real ethical issues with AI for clinical psychiatry

This article explores the ethical issues arising from ordinary AI applications currently used in mental health care, rather than speculative future scenarios. AI tools are already in use for a variety of purposes, including data collection for screening and intake, documentation, decision support, non-clinical support, and, in limited cases, adjunctive treatment. After reviewing the range of and distinctions between those applications, including when those distinctions become blurred, the article discusses selected ethical considerations. The use of AI in psychiatry raises issues related to reflective practice, the seductive allure of AI, varieties of bias, data security, and liability. These examples highlight how seemingly simple AI applications can still present significant ethical implications, suggesting practical considerations for clinicians, professional organizations, treatment organizations, training programs, and policymakers.

Exploring AI Applications for Addressing Alternative Conceptions in Teaching Physics: Focus on Electrical Circuits at the Secondary Level

This article introduces our initial foray into a comprehensive, long-term study on the learning practices of high school students using chatbots as educational tools. In this section, we explore the correlation between the logic underlying the responses generated by two chatbots for problems related to electrical circuits and the well-known alternative conceptions prevalent in the field of science education. To achieve this, we employed a methodology involving the presentation of ten questions to the chatbots, followed by an analysis of their answers in conjunction with established knowledge in physics and the Theory of Conceptual Fields. The objective was to bridge the gap between AI-generated responses and human responses. Our primary findings reveal a close resemblance between these two groups. This initial endeavor lays the foundation for developing an investigative methodology that will facilitate a comprehensive understanding and categorization of the various forms of interaction between students and chatbots.

Advanced AI and ML techniques in cybersecurity: Supervised and unsupervised learning, reinforcement learning, and neural networks in threat detection and response

In the rapidly advancing field of AI and ML, this paper explores their pivotal role in transforming cybersecurity. Highlighting the integration of sophisticated techniques like deep learning for intrusion detection and reinforcement learning for adaptive threat modeling, it emphasizes the shift towards AI-driven cybersecurity solutions. The study meticulously analyzes supervised and unsupervised learning's impact on threat detection accuracy and the dynamic capabilities of neural networks in real-time threat identification. It reveals how these methodologies enhance digital defenses against complex cyber threats, underscoring the theoretical underpinnings and practical applications of AI and ML in cybersecurity. The paper also discusses the challenges and future directions, contributing significant insights into the evolving landscape of cybersecurity technologies. This comprehensive research background sets the stage for understanding the unique contributions and potential of AI and ML in strengthening cybersecurity measures.

Using green hydrogen as the supplementary fuel for a scheme of power/fresh water production: An application of AI for optimal energy management and emission reduction

The primary objective of the current research is to speed up the shift towards a carbon-neutral economy and reduce our reliance on fossil fuels by developing a novel, highly efficient, environmentally friendly, and economically feasible multi-generation system based on solar-biomass hybridization. The essential element of this concept involves the introduction of green hydrogen into the combustion chamber, aiming to enhance the quality of the incineration products and facilitate the implementation of a waste-to-power Rankine cycle. The practicality of the suggested model is compared with a system that does not incorporate hydrogen injection. As a part of artificial intelligence, a neural network model integrated with the grey wolf optimizer is applied to ascertain the most optimal energy conversion/management condition with reduced computational time. The results show that the injection of additional hydrogen exhibits superior performance compared to the base system from all facets. The optimization achieves higher exergy efficiency and drinkable water production of 8.7 % and 27.9 kg/s while reducing the total cost and emission index of 13.7 $/h and 410.7 kg/GWh. The results indicate that the combustion chamber and steam generator are the poorest parts in terms of exergy under ideal conditions. Potential future directions of this work are to explore alternate techniques for generating hydrogen and perform regional evaluations to comprehend the accessibility and fluctuation of biomass and solar resources in various geographical areas.

Healthcare leaders’ experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study

BackgroundArtificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation of AI systems for these purposes, particularly in the context of healthcare leadership. This study explores the experiences of healthcare leaders regarding the barriers to implementing an AI application for automating medical history-taking and triage in Swedish primary care, as well as the actions they took to overcome these barriers. Furthermore, the study seeks to provide insights that can inform the development of AI implementation strategies for healthcare.MethodsWe adopted an inductive qualitative approach, conducting semi-structured interviews with 13 healthcare leaders representing seven primary care units across three regions in Sweden. The collected data were subsequently analysed utilizing thematic analysis. Our study adhered to the Consolidated Criteria for Reporting Qualitative Research to ensure transparent and comprehensive reporting.ResultsThe study identified implementation barriers encountered by healthcare leaders across three domains: (1) healthcare professionals, (2) organization, and (3) technology. The first domain involved professional scepticism and resistance, the second involved adapting traditional units for digital care, and the third inadequacies in AI application functionality and system integration. To navigate around these barriers, the leaders took steps to (1) address inexperience and fear and reduce professional scepticism, (2) align implementation with digital maturity and guide patients towards digital care, and (3) refine and improve the AI application and adapt to the current state of AI application development.ConclusionThe study provides valuable empirical insights into the implementation of AI for automating medical history-taking and triage in primary care as experienced by healthcare leaders. It identifies the barriers to this implementation and how healthcare leaders aligned their actions to overcome them. While progress was evident in overcoming professional-related and organizational-related barriers, unresolved technical complexities highlight the importance of AI implementation strategies that consider how leaders handle AI implementation in situ based on practical wisdom and tacit understanding. This underscores the necessity of a holistic approach for the successful implementation of AI in healthcare.

The rise of artificial intelligence in vascular surgery: A bibliometric analysis (2020-2024)

Aim: This study aims to perform a comprehensive bibliometric analysis of academic publications on AI applications in vascular surgery, identifying key authors, influential journals, prevalent research themes, and international collaborations, focusing on infrastructure, conceptual structure, and social networks within the field. Material and Methods: The analysis covers 815 documents published from 2020 to 2024, retrieved from the Web of Science Core Collection database. Metrics analyzed include publication growth, citation rates, key contributors, leading journals, prevalent themes, and international collaborations. Results: The research output showed a 15% annual growth rate, peaking in 2023. Despite increasing publications, the average citation rate per article declined. The study identified 5039 contributors with significant international co-authorship. Leading authors included Lareyre F and Raffort J, and the "Journal of Vascular Surgery" was the most influential journal. The USA and China led in contributions, reflecting robust research infrastructure. Key themes include risk assessment, diagnostic methods, and patient management, highlighting AI's role in enhancing diagnostic accuracy, treatment planning, and patient outcomes in vascular surgery. Conclusion: The analysis highlights the rapid growth and collaborative nature of AI research in vascular surgery. Key contributors, influential journals, and emerging themes were identified, emphasizing AI's role in improving diagnostics and patient outcomes. Limitations include the focus on one database and a five-year period, suggesting future research should include more databases and a longer timeframe. Exploring high-impact studies and practical applications will further advance the field.

The Application of Generative AI for Nursing Education.

Building a nursing education platform through Microsoft Power Apps. The main purpose is to provide convenience, real-time access, and comprehensive knowledge in nursing, as well as to improve work efficiency.

Exploring ethical considerations in AI-driven cataloguing and classification with ChatGPT

Purpose. This study investigates the ethical dimensions surrounding the use of AI-driven cataloguing and classification systems, exemplified by ChatGPT, in library sciences. The research aims to fill a gap in the existing literature by exploring privacy, accountability, transparency, and bias issues associated with these technologies. Design/Methodology/Approach. The study employs a comprehensive literature review to analyze the current landscape of AI applications in libraries, emphasizing ethical considerations. It also assesses existing guidelines from associations like ALA and IFLA, and explores privacy concerns, biases, and the impact on job satisfaction. Findings. The study identifies gaps in current ethical frameworks and emphasizes the need for guidelines to evolve with technological advancements. Privacy concerns, biases in AI outputs, and challenges related to user trust and transparency are highlighted. The impact of AI on job satisfaction for librarians is discussed, acknowledging both opportunities and challenges. Originality/Value. This study contributes to the broader discussion on ethical AI by specifically addressing the underexplored area of AI-driven cataloguing and classification systems. It underscores the importance of aligning AI use with ethical standards, proposing strategies for mitigating biases and fostering user trust.

Generative AI and training employees with special needs

PurposeThe viewpoint paper aims to highlight the assistive role that Generative artificial intelligence (Gen AI) can play in the design of learning and development programs for employees with special needs. The article discusses the challenges, benefits and reasons why Gen AI should be used to manage diversity, equity and inclusion by creating personalized and customized training and development programs.Design/methodology/approachThe viewpoint paper is based on reviewing articles and videos on the application of Gen AI in learning and development.FindingsGen AI offers immense opportunities to design personalized learning solutions for employees with special needs due to disability that can be physical or cognitive. The AI-based solutions support special learners by customizing assistive technology-based solutions and content based on the level of disability and need of the learner. This paper also highlights the importance of synergy between the training department, government and technology solution providers.Originality/valueThe viewpoint paper fills in an important gap by discussing the role that Gen AI can play by facilitating the learning and development of employees with unique skills.

Examination of studies on artificial intelligence and chatbots in the context of secondary school science course: content analysis

This study aims to investigate the applications of artificial intelligence and chatbots in secondary school science lessons through descriptive content analysis. Using a qualitative case study approach, 20 theses from YÖKTEZ database and 10 international articles from Google Scholar were analysed, focusing on chatbots in science education. The results of the analysis revealed a notable increase in AI and chatbot-related theses between 2012 and 2023, especially in the last two years, highlighting the growing interest in e-learning and AI in STEM fields. Positive effects on student and teacher attitudes towards STEM education have been widely reported. However, besides the limited number of international studies, the discovery of only one secondary school thesis on chatbots unrelated to Science in Turkey points to a significant research gap in AI and chatbot integration into Science education. Addressing this gap may improve the application of AI and chatbot technologies in science teaching and reveal the need for further research to explore their potential benefits and applications in educational settings.

Computational Optimisation of Urban Design Models: A Systematic Literature Review

The densification of urban spaces globally has contributed to a need for design tools supporting the planning of more sustainable, efficient, and liveable cities. Urban Design Optimisation (UDO) responds to this challenge by providing a means to explore many design solutions for a district, evaluate multiple objectives, and make informed selections from many Pareto-efficient solutions. UDO distinguishes itself from other forms of design optimisation by addressing the challenges of incorporating a wide range of planning goals, managing the complex interactions among various urban datasets, and considering the social–technical aspects of urban planning involving multiple stakeholders. Previous reviews focusing on specific topics within UDO do not sufficiently address these challenges. This PRISMA systematic literature review provides an overview of research on topics related to UDO from 2012 to 2022, with articles analysed across seven descriptive categories. This paper presents a discussion on the state-of-the-art and identified gaps present in each of the seven categories. Finally, this paper argues that additional research to improve the socio-technical understanding and usability of UDO would require: (i) methods of optimisation across multiple models, (ii) interfaces that address a multiplicity of stakeholders, (iii) exploration of frameworks for scenario building and backcasting, and (iv) advancing AI applications for UDO, including generalizable surrogates and user preference learning.

EMI Teachers' Attitudes and Approaches towards Grammar Error and Professional Development Perspectives in Chinese Higher Education

In the context of Chinese higher education, there is still blank in the English Medium Instruction (EMI) system, particularly in terms of subject teachers' attitudes and behaviors toward teaching grammar in professional courses and their perspectives on the professional advancement of this competency. Based on the existing research, the current study mainly examined how subject teachers perceived their roles and responsibilities in language teaching especially grammar, what strategies they will take to solve students’ grammatical problems, and their feelings and preferences with teacher professional development related to this aspect. Data were obtained from three semi-structured online interviews with three teachers from Beijing Normal University-Hong Kong Baptist University United International College. The findings indicates that EMI lecturers declined to take on the role of language instructors. However, in pursuit of enhancing students' overall understanding of the subject, they did incorporate certain grammar adjustment techniques in their classes and while reviewing students' written work, which is the initial evidence of manifestation of the grammatical Language Related Episodes (LREs). Additionally, the participants recognized the utilization of AI tools to assist students in improving their grammar skills. Furthermore, regarding the professional growth of EMI teachers, they emphasized the importance for tertiary education to stay abreast with current trends that training programs should assist EMI teachers with AI usage for grammar correction, as well as guiding students on the appropriate application of AI in their language learning process.

Enhancing Engagement and Autonomy in Advanced Japanese Learning as the Second Language through AI-Assisted Video Production

This study delves into the integration of generative AI in second language acquisition, particularly focusing on advanced Japanese learners' autonomy and motivation enhancement through video production. Drawing from Japan's educational guidelines and prior research, the investigation examines the roles of generative AI in language education, emphasizing ethical considerations and strategies for effective integration (Mollick Mollick, 2023). The course implemented a hybrid approach, combining language instruction with media production, where students created YouTube videos in Japanese. Their productions were supported by AI tools for language correction, narration, and content generation. Results indicate that while AI facilitated practical language learning, instructor guidance remained crucial for ensuring accuracy and contextual appropriateness. The study highlights challenges such as overreliance on AI-generated content and the need for nuanced communication in prompt engineering. Despite these challenges, students demonstrated cultural understanding and improved nonverbal communication skills through video production. Future research aims to analyze students' reflections on their experiences, shedding light on their insights and future applications of AI in language learning.

Quantitative and Qualitative Analysis of Moral Foundations in Argumentation

This paper introduces moral argument analytics, a technology that provides insights into the use of moral arguments in discourse. We analyse five socio-political corpora of argument annotated data from offline and online discussions, totalling 240k words with 9k arguments, with an average annotation accuracy of 78%. Using a lexicon-based method, we automatically annotate these arguments with moral foundations, achieving an estimated accuracy of 83%. Quantitative analysis allows us to observe statistical patterns and trends in the use of moral arguments, whereas qualitative analysis enables us to understand and explain the communication strategies in the use of moral arguments in different settings. For instance, supporting arguments often rely on Loyalty and Authority, while attacking arguments use Care. We find that online discussions exhibit a greater diversity of moral foundations and a higher negative valence of moral arguments. Online arguers often rely more on Harm rather than Care, Degradation rather than Sanctity. These insights have significant implications for AI applications, particularly in understanding and predicting human and machine moral behaviours. This work contributes to the construction of more convincing messages and the detection of harmful or biased AI-generated synthetic content.

Acceptance of artificial intelligence in education: opportunities, concerns and need for action

The spread of AI text generators such as ChatGPT in education has reached an enormous reach in a short period, which has led to various questions regarding the acceptance of artificial intelligence among teachers and student teachers. This study examines the acceptance of AI among teachers and student teachers. In particular, it considers crucial aspects for planning teaching and teacher training. The results show that despite fundamentally positive attitudes towards AI applications, there are concerns regarding data ethics and legal standards. The correlation between the intention to use AI and trust in AI is significant. The findings should help gain a more comprehensive understanding of the acceptance of AI in the education sector and help teachers plan training and further education accordingly.

Emergence of Artificial Intelligence Art Therapies (AIATs) in Mental Health Care: A Systematic Review.

The application of artificial intelligence art therapies (AIATs) in mental health care represents an innovative merger between digital technology and the therapeutic potential of creative arts. This systematic review aimed to assess the effectiveness and ethical considerations of AIATs, incorporating robots, AI painting and AI Chatbots to augment traditional art therapies. Aligning with the Preferred Reporting Items for systematic reviews (PRISMA) guidelines, we meticulously searched PubMed, Cochrane Library, Web of Science and CNKI, resulting in 15 selected articles for detailed analysis. To ensure methodological quality, we applied the Joanna Briggs Institute (JBI) criteria for quality assessment and extracted data using the PICO(S) format, specifically targeting randomised controlled trials (RCTs). Our findings suggest that AIATs can profoundly enhance the therapeutic experience by providing new creative outlets and reinforcing existing methods, despite possible drawbacks and ethical challenges. This examination underscores AIATs' potential to enrich mental health therapies, emphasising the critical importance of ethical considerations and the responsible application of AI as the field evolves. With a focus on expanding treatment efficacy and patient expressiveness, the promise of AIATs in mental health care necessitates a careful balance between innovation and ethical responsibility. Trial Registration: PROSPERO: CRD42024504472.

Conceptualizing generative AI as style engines: Application archetypes and implications

The rise of generative AI has brought with it a surprising paradox: systems that excel at tasks once thought to be uniquely human, like fluent conversation or persuasive writing, while simultaneously failing to meet traditional expectations of computing, in terms of reliability, accuracy, and veracity (e.g., given the various issues with so-called ‘hallucinations’). We argue that, when generative AI is seen through a traditional computing lens, its development focuses on optimizing for traditional computing traits that remain in principle unattainable. This risks backgrounding what is most novel and defining about it. As probabilistic technologies, generative AIs do not store, in any traditional sense, any data or content. Rather, essential features of training data become encoded in deep neural networks as patterns, that become practically available as styles. We discuss what happens when the distinction between objects and their appearance dissolves and all aspects of images or text become understood as styles, accessible for exploration and creative combination and generation. For example, defining visual qualities of entities like ‘chair’ or ‘cat’ become available as ‘chair-ness’ or ‘cat-ness’ for creative image generation. We argue that, when understood as style engines, unique generative AI capabilities become conceptualized as complementing traditional computing ones. This will aid both computing practitioners and information systems researchers in reconciling and integrating generative AI into the traditional IS landscape. Our conceptualization leads us to propose four archetypes of generative AI application and use, and to highlight future avenues for information systems research made visible by this conceptualization, as well as implications for practice and policymaking.

L2A Cdus Performance and Considerations for Server Rooms Upgrade with Conventional Air Conditioning

Abstract As web-based AI applications are growing rapidly, server rooms face escalating computational demands, prompting enterprises to either upgrade their facilities or outsource to co-located sites. This growth strains conventional HVAC systems, which struggle to handle the substantial thermal load, often resulting in hotspots. Liquid-to-Air (L2A) Coolant Distribution Units (CDUs) emerge as a solution, efficiently cooling servers by circulating liquid coolant through cooling loops mounted on each server board. In this study, the performance of a 24-kW L2A CDU is evaluated across various scenarios, emphasizing cooling effect, stability, and reliability. Experimental tests involve a rack with three thermal test vehicles (TTVs), monitoring both liquid coolant and air sides for analysis. Tests are conducted in a limited air-conditioned environment, resembling upgraded server rooms with conventional AC systems. The study also assesses the impact of high-power density cooling units on the server room environment, measuring noise, air velocity, and ambient temperature against ASHRAE standards for human comfort. Recommendations for optimal practices and potential system improvements are included in the research, addressing the growing need for efficient cooling solutions amidst escalating computational demands.

Recent Trends of Customer Relationship Management in AI: A Scientometric Analysis

Theoretical background: The popularity of AI in recent years has led to its integration with various industrial sectors. This spectrum of AI applications has brought numerous topics that are underexplored and need to be taken up. One such area is the integration of AI with customer relationship management (CRM) systems. In consequence, this research is valuable for the scientific community. Purpose of the article: The purpose of the research is to conduct scientometric analysis in the field of AI in CRM and to identify the most crucial areas of research, identify motor and niche themes in the AI-CRM and indicate future research trends. Research methods: The methodology was divided into three parts: data collection, descriptive analysis, scientometric analysis. In the research, “R programming” and “Biblioshiny” were used to conduct scientometric analysis. This made it possible to generate charts and a table based on gathered data and then analyse the results. Main findings: The results show there is a growth in AI-CRM systems subject since 2019. Keywords like “CRM”, “public relationship” or “data mining” were often used in research articles. Future research trends can be define among others: acceptance and trust for AI powered technology, impact on companies’ environment or relationship with customer.