Artificial Intelligence Components Research Articles

Artificial Intelligence in Acoustics of Library Functioning in Ukraine

The purpose of the study is to outline achievements, prospects for transformation and identify obstacles to the implementation of systems, elements of artificial intelligence (AI) and the algorithm for overcoming them in national library and information institutions. Research methodology: the principles of systematicity, generalisation, objectivity, and general methods of scientific research – analysis, synthesis, comparison, analytical monitoring of AI elements in libraries of the country – are applied. The scientific novelty of the work is to highlight, with an emphasis on key aspects, the obstacles to the addition of AI intellectual resources to the work of libraries in the realities of today. Conclusions. The article reflects the developments, main areas of use and reasons for limiting the intensive integration of AI systems and elements into library practice. Features of the application of AI components, practical potential of ultra-high-frequency technologies in national libraries. The above-mentioned practice in libraries is especially relevant during martial law and the times of profound changes. It will facilitate public access to information and self-education; access to advanced technological developments; allow further steps in the formation of a new digitalisation focus of libraries and modification of library activities; thoroughly form an approach to the vectors of the industry's influence on society; innovations in the model of library-user relations, taking into account civilisation challenges. The studied trends in the addition of intelligent systems and elements of artificial intelligence will contribute to the voluntarisation of technological proposals in library and information institutions. As a result of the study, the issue of compatibility of adding intellectual components to the work of the library industry with the realities of the country and the corresponding algorithm of actions for the long-term is considered. Key words: library, systems and elements of artificial intelligence, innovative technologies, transformation of intellectual resources, challenges of AI implementation in library practice.

Using Artificial Intelligence to Predict and Prevent Future Food Insecurity

Abstract: The article explores the role and prospects of artificial intelligence (AI) in addressing global food insecurity. It provides an overview of machine learning (ML) techniques—the core learning component of AI—used to predict food security outcomes and discusses real-world examples as well as recent applications of ML. It further examines the challenges and limitations of ML, including concerns related to data quality and ethical considerations, followed by policy recommendations in crucial areas such as funding, cross-sector collaboration, education, and data standards. Finally, it underscores the importance of recognizing AI as a complementary tool, rather than a standalone solution, in the pursuit of the ultimate goal of achieving a world without hunger.

Unraveling the Significance of the Classification Tree Algorithm in Machine Learning: A Literature Review

Machine learning, an integral component of Artificial Intelligence (AI), empowers systems to autonomously enhance their performance through experiential learning. This paper presents a comprehensive overview of the Classification Tree Algorithm's pivotal role in the realm of machine learning. This algorithm simplifies the process of categorizing new instances into predefined classes, leveraging their unique attributes. It has firmly established itself as a cornerstone within the broader landscape of classification techniques. This paper delves into the multifaceted concepts, terminologies, principles, and ideas that orbit the Classification Tree Algorithm. It sheds light on the algorithm's essence, providing readers with a clearer and more profound understanding of its inner workings. By synthesizing a plethora of existing research, this endeavor contributes to the enrichment of the discourse surrounding classification tree algorithms. In summary, the Classification Tree Algorithm plays a fundamental role in machine learning, facilitating data classification, and empowering decision-making across domains. Its adaptability, alongside emerging variations and innovative techniques, ensures its continued relevance in the ever-evolving landscape of artificial intelligence and data analysis.

Utilize Fuzzy Delphi Method to Design and Develop T2IG Application for Primary Schools

Education has encountered multiple transformations in tandem with the globalisation of society. Technology is an essential tool for conducting teaching and learning activities. COVID-19 pre- and post-education also emphasize the importance of technology in accessing education. This study aims to develop a Text to Infographic (T2IG) application. T2IG is an application that developed on two platforms, namely in the form of a website and a smartphone application. Users (students) upload text, and the Google Cloud Platform Artificial Intelligence components will translate it into a graphical form containing information. In addition, Google Cloud Platform Artificial Intelligence will quote actual contents from the entire text uploaded by the user. In this study, the process of gathering experts' consensus to identify the application's elements and components that should be used as the application's primary function. The Fuzzy Delphi Method was used to identify the consensus of the experts through a questionnaire. Using the Fuzzy Delphi technique is to get concessions from experts on the development of instructional materials. This method involves the use of fuzzy set theory which has been integrated with the classic Delphi method. The use of this technique helps in determining the requirements of the elements that are important in the development of instructional materials in detail. A total of 12 experts were identified using the expert sampling method. These 12 experts are among lecturers and officials from the fields of education, technology and language who are currently working at the University, Teaching Institutions and the Department of Education. The study identified seven key components and 25 sub-constructs that should be used in the development of the T2IG application. The findings highlight the importance of using technology to enhance teaching and learning activities and the significance of experts' opinions in the development of instructional materials. Twelve study experts accepted a total of six components. The findings of this study have implications for the development of instructional applications that will be developed. This study's findings also emphasise the appropriate components for developing T2IG applications as proposed.

Artificial Intelligence In Cyber Security: Rescue Or Challenge

Objective: The objective of this research paper is to explore the potential of artificial intelligence (AI) in enhancing and fortifying cybersecurity measures. In light of the increasing cyber threats and the limitations of traditional computer security systems, the paper aims to shed light on the concept of AI and its components. The central goal is to demonstrate how AI can be leveraged to improve cybersecurity, focusing on key areas such as machine learning, data mining, in-depth learning, and expert programs.
 Method: This research paper utilizes a literature review approach to investigate the intersection of artificial intelligence and cybersecurity. It involves analyzing existing scholarly works and studies to understand the challenges posed by evolving cyber threats and the limitations of conventional security systems. The paper identifies various components of AI that have the potential to enhance cybersecurity measures and delineates their applicability in the digital landscape.
 Results: The analysis of past literature emphasizes the growing significance of adopting artificial intelligence techniques to bolster cybersecurity practices. The results underscore the inadequacies of traditional security systems in predicting and countering the ever-evolving tactics of cyber attackers and terrorists. Through an examination of various AI components, including machine learning, data mining, in-depth learning, and expert programs, the paper demonstrates the potential benefits that these technologies can offer to the field of cybersecurity.
 Conclusions: In conclusion, this research paper highlights the escalating threats to cybersecurity in the face of rapid technological advancement. The imperative of addressing these challenges through AI becomes evident as traditional security systems prove insufficient. By harnessing the power of artificial intelligence techniques such as machine learning, data mining, in-depth learning, and expert programs, institutions can significantly enhance their cybersecurity capabilities. The paper underscores the need for a paradigm shift towards AI-driven cybersecurity strategies to effectively predict, prevent, and counteract cyber threats in the digital age.

Verification strategy for artificial intelligence components in nuclear plant instrumentation and control systems

The application of artificial intelligence (AI) systems in nuclear power plants has the potential to facilitate significant advancements in the industry. However, nuclear system engineers and regulators have expressed concerns regarding the reliability of AI components owing to a lack of documentation and appropriate verification methods that differ from those used for traditional software. Accordingly, this study proposes a systematic approach for the verification and validation of AI components based on nuclear software regulatory guidelines. The proposed strategy includes process verification and functional testing activities specifically tailored to AI components for instrumentation and control systems. This approach serves as a foundation for fostering understanding and agreement among system engineers, operating licensees, and regulatory bodies on the use of AI components in nuclear power plants.

The future of advertising campaigns: The role of AI-generated images in advertising creative

Computational creativity is a growing component of new artificial intelligence (AI) technologies that allow a machine to render creative constructs such as music, text and images. A rapidly growing area of computational creativity is AI text-to-image engines capable of producing realistic imagery that can now meet the standard of human quality outputs. DALL-E 2, built by OpenAI, is a leader in the field and offers commercial access to AI-produced images. To understand the impact of engines such as DALL-E 2 on advertising agencies and their creative workflows, we conducted a series of focus groups with Aotearoa, New Zealand-based advertising agencies exploring creative practitioners’ considerations on the capability of the DALL-E 2 text-to-image technology. An existing Volkswagen advertising campaign called ‘Small but Ferocious’ that used ‘blended’ animals as a visual metaphor for their economical yet powerful ‘TSI’ engines was expanded in a ‘faux’ continuation of the campaign. Four new images produced by DALL-E 2 were presented to creatives attending the focus groups. Participants were then asked about these new creative-AI assets concerning image quality, creative production and collaborative models. A thematic analysis of the comments from the focus groups was conducted and elicited three themes: aesthetics, creative practice and human vs. machine. Participants’ responses revealed that they were both excited and concerned about DALL-E 2’s capability in image production, its effect on creative workflows and the role of the human vs. machine in generating creative outputs. The result was a clear sense of inevitability for how creative roles will change as computational creativity systems, such as DALL-E 2, advance and are adopted into agency workflows.

Palliative care as a digital working world (PALLADiUM) - A mixed-method research protocol

BackgroundIn Palliative Care, actors from different professional backgrounds work together and exchange case-specific and expert knowledge and information. Since Palliative Care is traditionally distant from digitalization due to its holistically person-centered approach, there is a lack of suitable concepts enabling digitalization regarding multi-professional team processes. Yet, a digitalised information and collaboration environment geared to the requirements of palliative care and the needs of the members of the multi-professional team might facilitate communication and collaboration processes and improve information and knowledge flows. Taking this chance, the presented three-year project, PALLADiUM, aims to improve the effectiveness of Palliative Care teams by jointly sharing available inter-subjective knowledge and orientation-giving as well as action-guiding practical knowledge. Thus, PALLADiUM will explore the potentials and limitations of digitally supported communication and collaboration solutions.MethodsPALLADiUM follows an open and iterative mixed methods approach. First, ethnographic methods – participant observations, interviews, and focus groups – aim to explore knowledge and information flow in investigating Palliative Care units as well as the requirements and barriers to digitalization. Second, to extend this body, the analysis of the historical hospital data provides quantitative insights. Condensing all findings results in a to-be work system. Adhering to the work systems transformation method, a technical prototype including artificial intelligence components will enhance the collaborative teamwork in the Palliative Care unit.DiscussionPALLADiUM aims to deliver decisive new insights into the preconditions, processes, and success factors of the digitalization of a medical working environment as well as communication and collaboration processes in multi-professional teams.Trial registrationThe study was registered prospectively at DRKS (Deutsches Register Klinischer Studien) Registration-ID: DRKS0025356 Date of registration: 03.06.21.

Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors

Today’s healthcare facilities require new digital tools to cope with the rapidly increasing demand for technology that can support healthcare operators. The advancement of technology is leading to the pervasive use of IoT devices in daily life, capable of acquiring biomedical and biometric parameters, and providing an opportunity to activate new tools for the medical community. Digital twins (DTs) are a form of technology that are gaining more prominence in these scenarios. Many scientific research papers in the literature are combining artificial intelligence (AI) with DTs. In this work, we propose a case study including a proof of concept based on microservices, the heart DT, for the evaluation of electrocardiogram (ECG) signals by means of an artificial intelligence component. In addition, a higher-level platform is presented and described for the complete management and monitoring of cardiac pathologies. The overall goal is to provide a system that can facilitate the patient–doctor relationship, improve medical treatment times, and reduce costs.

SA43 Uptake of the Prisma 2020 Statement and Its Artificial Intelligence Component in Recently Published Systematic Reviews

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, first published in 2009 by Moher et al., has been widely adopted by researchers for reporting of systematic literature reviews (SLRs). Since then, research methods and relevant technologies for SLRs, i.e., artificial intelligence (AI), have also evolved, becoming a key component of evidence to support clinical decision-making and guideline development. As such, the PRISMA statement was updated in 2020 to reflect these advances and ensure more comprehensive, detailed reporting of SLR methodology and findings to better facilitate replicability. This study aimed to assess the uptake of the PRISMA 2020 statement in recently published SLRs and determine whether AI-assisted screening is being reported and how it is being used.

Physiotherapeutic Aid using ML and AI

In the recent year 2020 due to corona many people were not able to have appointments with their doctors and they were locked in their house. The younger generation can possibly maintain their health even if they do not go outside for days, but the older generation have to do regular exercise to maintain their health and well-being; prescribed by the doctors. It is not just because of corona that we have experienced this but we have also come to a situation where older people cannot visit their doctors. So, as a solution for these problems/challenges, we came up with an idea so that they can do their exercise and maintain their health without visiting their doctors or physiotherapist. Artificial Intelligence is technically defined as the development of technology which is used to perform technology operations which require involvement of human intelligence. Machine learning is one of the key components of artificial intelligence and it provides us with the ability of both supervised and unsupervised learning for training our model. AI technology today can be in different forms such as software programs as well as hardware interfaces to develop a system which is capable of learning from their own datasets. In our project AI with machine learning can be used for posture detection and then assessment of patients. We provided physiotherapy using AI and ML. We used normal running feed and we got good frame rates. These older generations have to do live exercise in front of the camera, our software will detect their pose/position whether they are doing their exercises correctly or not. By comparing the poses obtained from the live feed to the images or the videos obtained from the dataset. If the pose is not matched to the dataset, it is terminated or denied and deemed as wrong exercise. Our software is going to tell them where they went wrong. Now we have to decide a perfect algorithm/method to detect/estimate the pose with much higher accuracy. We are going to compare all the algorithms present till date regarding pose estimation and select the algorithms which give best accuracy.

Increasing Participation in a TelePrEP Program for Sexual and Gender Minority Adolescents and Young Adults in Louisiana: Protocol for an SMS Text Messaging-Based Chatbot.

Sexual and gender minority (SGM) adolescents and young adults (AYAs) are at increased risk of HIV infection, particularly in the Southern United States. Despite the availability of effective biomedical prevention strategies, such as pre-exposure prophylaxis (PrEP), access and uptake remain low among SGM AYAs. In response, the Louisiana Department of Health initiated the LA TelePrEP Program, which leverages the power of telemedicine to connect Louisiana residents to PrEP. A virtual TelePrEP Navigator guides users through the enrollment process, answers questions, schedules appointments, and facilitates lab testing and medication delivery. To increase the participation of SGM AYAs in the program, the TelePrEP program partnered with researchers to develop a chatbot that would facilitate access to the program and support navigator functions. Chatbots are capable of carrying out many functions that reduce employee workload, and despite their successful use in health care and public health, they are relatively new to HIV prevention. In this paper, we describe the iterative and community-engaged process that we used to develop an SMS text messaging-based chatbot tailored to SGM AYAs that would support navigator functions and disseminate PrEP-related information. Our process was comprised of 2 phases: conceptualization and development. In the conceptualization phase, aspects of navigator responsibilities, program logistics, and user interactions to prioritize in chatbot programming (eg, scheduling appointments and answering questions) were identified. We also selected a commercially available chatbot platform that could execute these functions and could be programmed with minimal coding experience. In the development phase, we engaged Department of Health staff and SGM AYAs within our professional and personal networks. Five different rounds of testing were conducted with various groups to evaluate each iteration of the chatbot. After each iteration of the testing process, the research team met to discuss feedback, guide the programmer on incorporating modifications, and re-evaluate the chatbot's functionality. Through our highly collaborative and community-engaged process, a rule-based chatbot with artificial intelligence components was successfully created. We gained important knowledge that could advance future chatbot development efforts for HIV prevention. Key to the PrEPBot's success was resolving issues that hampered the user experience, like asking unnecessary questions, responding too quickly, and misunderstanding user input. HIV prevention researchers can feasibly and efficiently program a rule-based chatbot with the assistance of commercially available tools. Our iterative process of engaging researchers, program personnel, and different subgroups of SGM AYAs to obtain input was key to successful chatbot development. If the results of this pilot trial show that the chatbot is feasible and acceptable to SGM AYAs, future HIV researchers and practitioners could consider incorporating chatbots as part of their programs. PRR1-10.2196/42983.

Can artificial intelligence identify creativity?: An empirical study

This article reports an investigation of the extent to which a chess program with an artificial intelligence component (i.e., Stockfish with NNUE) can identify 10 chess moves that are recognized as outstanding chess moves. Stockfish with NNUE was able to identify seven of the ten moves. Although Stockfish with NNUE is a very powerful chess program, it has some limitations in identifying creative chess moves. There is a discussion of those limitations.

Requirements engineering framework for human-centered artificial intelligence software systems

Context:Artificial intelligence (AI) components used in building software solutions have substantially increased in recent years. However, many of these solutions focus on technical aspects and ignore critical human-centered aspects. Objective:Including human-centered aspects during requirements engineering (RE) when building AI-based software can help achieve more responsible, unbiased, and inclusive AI-based software solutions. Method:In this paper, we present a new framework developed based on human-centered AI guidelines and a user survey to aid in collecting requirements for human-centered AI-based software. We provide a catalog to elicit these requirements and a conceptual model to present them visually. Results:The framework is applied to a case study to elicit and model requirements for enhancing the quality of 360° videos intended for virtual reality (VR) users. Conclusion:We found that our proposed approach helped the project team fully understand the human-centered needs of the project to deliver. Furthermore, the framework helped to understand what requirements need to be captured at the initial stages against later stages in the engineering process of AI-based software.

Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.

Artificial Intelligence Accelerated Transformation in The Healthcare Industry

The healthcare industry was a pioneer in the deployment of artificial intelligence (AI) technology. Due to the nature of the services and the vulnerability of a sizable portion of end users, there has been a significant amount of research and discussion on the concept of artificial intelligence. A mixed-method approach has been used to pinpoint the components of moral AI in the healthcare sector and look into how it affects value creation and market performance. Since AI technology is still developing in India, analysis is conducted in an Indian context. The understanding of how various AI components supported healthcare organisations and deliver better patient-centered care and evidence-based medicine was aided by these in-depth studies and analyses of the patient perspective.

Addressing bias in artificial intelligence for public health surveillance

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been...

Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine.

Large Language Models (LLMs) are a key component of generative artificial intelligence (AI) applications for creating new content including text, imagery, audio, code, and videos in response to textual instructions. Without human oversight, guidance and responsible design and operation, such generative AI applications will remain a party trick with substantial potential for creating and spreading misinformation or harmful and inaccurate content at unprecedented scale. However, if positioned and developed responsibly as companions to humans augmenting but not replacing their role in decision making, knowledge retrieval and other cognitive processes, they could evolve into highly efficient, trustworthy, assistive tools for information management. This perspective describes how such tools could transform data management workflows in healthcare and medicine, explains how the underlying technology works, provides an assessment of risks and limitations, and proposes an ethical, technical, and cultural framework for responsible design, development, and deployment. It seeks to incentivise users, developers, providers, and regulators of generative AI that utilises LLMs to collectively prepare for the transformational role this technology could play in evidence-based sectors.

MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty.

Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., learning-enabled components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain and therefore cannot be assured as safe. Automated methods are needed for self-assessment and adaptation to decide when learned behavior can be trusted. This work introduces a model-driven approach to manage self-adaptation of a learning-enabled system (LES) to account for run-time contexts for which the learned behavior of LECs cannot be trusted. The resulting framework enables an LES to monitor and evaluate goal models at run time to determine whether or not LECs can be expected to meet functional objectives and enables system adaptation accordingly. Using this framework enables stakeholders to have more confidence that LECs are used only in contexts comparable to those validated at design time.

Multi-hop temporal knowledge graph reasoning with temporal path rules guidance

Temporal Knowledge graph (TKG) is a key component of artificial intelligence. In recent years, many large-scale temporal knowledge graphs have been produced and put into practical applications. At present, researchers have proposed many methods to reason facts that do not exist in temporal knowledge graph using the existing information. However, most traditional embedding-based reasoning methods of temporal knowledge graph lack interpretability, cannot get the reasoning paths. Therefore, the multi-hop path reasoning method of knowledge graph has gradually become a hot-spot. However, technologies related to multi-hop path reasoning in temporal knowledge graph are still in their infancy, and existing methods ignore the importance of logical rules in temporal knowledge graph and do not consider the rules between multi-hop paths. To solve this problem, we propose a multi-hop path reasoning method of temporal knowledge graph based on temporal path rules, named TPRG. TPRG further analyzes the multi-hop paths that may be formed in the multi-hop path reasoning task of temporal knowledge graph, deeply studies the logical connection between the multi-hop paths of temporal knowledge graph and the potential rules between these paths. We define a total of fourteen temporal path rules in TPRG, and these rules can be divided into three different lengths and five different types. The qualified rules are extracted from the datasets according to the proposed temporal path rules. Then, the temporal path rule confidence calculation strategy is proposed. Finally, we conduct temporal knowledge graph reasoning through temporal path rules confidence. Experimentally, our model outperforms other state-of-the-art reasoning methods in several aspects over four public temporal knowledge graph datasets.