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Background: The utilization of ChatGPT in writing scientific papers has sparked both pros and cons in Indonesia. Some studies reveal its great potential, while others highlight the negative impacts resulting from the use of ChatGPT. Objective: This research aims to analyze the area, impact, and trends in the use of ChatGPT in writing scientific papers in Indonesia through a systematic review. Methodology: Researchers use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to conduct the analysis. The sample consists of 19 selected studies collected from the Google Scholar and Scopus databases. Data analysis uses quantitative and qualitative descriptive methods. Result: The research results show that the areas in which ChatGPT is used in writing scientific papers include topic selection, reference search, data analysis, scientific grammar, and translation. The use of ChatGPT in writing scientific papers faces some serious challenges, especially those related to ethics and academic integrity, such as increasing rates of plagiarism and declining values of honesty and responsibility. Moreover, dependence on artificial intelligence technology has the potential to reduce the development of human intellectual abilities, such as critical thinking, analysis, interpretation, and logic. Until recently, the research trend related to the use of ChatGPT for writing scientific papers is increasing, with the quite low density of research topics; thus, there are opportunities for further research to be carried out. Conclusion: The utilization of ChatGPT in academic writing in Indonesia has both positive and negative aspects. Regulation and morality can be crucial keys to realizing a quality academic environment. Unique Contribution: This research contributes to understanding the opportunities and challenges of utilizing ChatGPT in writing scientific papers, as well as providing information regarding areas that have the potential for further research. Key Recommendation: An in-depth understanding of the appropriate regulations for the use of ChatGPT in writing scientific papers is needed to minimize risks while still maximizing its positive potential.

Objective In recent years, advancements in information technology, such as wireless EMG, high-resolution cameras, programs like OpenSim, innovations in textile sensors, and the emergence of artificial intelligence and smart mobile devices, have provided biomechanists with new tools and approaches. This study aims to investigate emerging trends in sports biomechanics, summarizing and providing practical insights from research conducted between 2015 and 2023. Methods A systematic search of research articles on new biomechanics approaches published between 2015 and 2023 was conducted. Specialized databases were queried, and 47 articles meeting the inclusion criteria were selected for analysis. Results Analysis of the selected studies revealed that the integration of information technology, artificial intelligence, smartphones, software, and wearable medical sensors in sports biomechanics has shown promising results in enhancing performance and reducing injury risks. Conclusion The findings of this study suggest that advancements in sports biomechanics technologies are pushing the boundaries of current research. Continued exploration and application of these technologies will likely shape the future of sports science and performance.

Regulation of health technologies must be rigorous, instilling trust among both healthcare providers and patients. This is especially important for the control and supervision of the growing use of artificial intelligence in healthcare. In this commentary on the accompanying piece by Van Laere and colleagues, we set out the scope for applying artificial intelligence in the healthcare sector and outline five key challenges that regulators face in dealing with these modernday technologies. Addressing these challenges will not be easy. While artificial intelligence applications in healthcare have already made rapid progress and benefitted patients, these applications clearly hold even more potential for future developments. Yet it is vital that the regulatory environment keep up with this fast-evolving space of healthcare in order to anticipate and, to the extent possible, prevent the risks that may arise.

AIM: To analyze research hotspots and trends of artificial intelligence in diabetic retinopathy(DR)based on bibliometrics and high-impact papers.METHODS: Papers on artificial intelligence in DR research published in the Web of Science Core Collection(WoSCC)from January 1, 2012, to December 31, 2022 were retrieved. The data was analyzed by CiteSpace software to examine annual publication number, countries, institutions, source journal, research categories, keywords, and to perform an in-depth analysis of high-impact papers.RESULTS: A total of 1 009 papers on artificial intelligence in DR from 79 countries were included in the study, with 272 papers published in 2022. Notably, China and India contributed 287 and 234 papers, respectively. The United Kingdom exhibited a centrality score of 0.31, while the United States boasted an impressive H-index of 48. Three prominent institutions in the United Kingdom(University of London, Moorfields Eye Hospital, and University College London)and one institution in Egypt(Egyptian Knowledge Bank)all achieved a notable H-index of 14. The primary academic disciplines associated with this research field encompassed ophthalmology, computer science, and artificial intelligence. Burst keywords in the years 2021～2022 included transfer learning, vessel segmentation, and convolutional neural networks.CONCLUSION: China emerged as the leading contributor in terms of publication number in this field, while the United States stood out as a key player. Notably, Egyptian Knowledge Bank and University of London assumed leading roles among research institutions. Additionally, IEEE Access was identified as the most active journal within this domain. The research focus in the field of artificial intelligence in DR has transitioned from AI applications in disease detection and grading to a more concentrated exploration of AI-assisted diagnostic systems. Transfer learning, vessel segmentation, and convolutional neural networks hold substantial promise for widespread applications in this field.

Objective: To assess the current trends in the field of artificial intelligence in medicine by analysing 100 most cited original articles relevant to the field. Methods: The systematic review was conducted in September 2022, and comprised literature search on Scopus database for original articles only. Google and Medical Subject Headings databases were used as resources to extract key words. In order to cover a broad range of articles, original studies comprising human as well as non-human subjects, studies without abstract and studies in languages other than English were part of the inclusion criteria. There was no specific time period applied to the search and no specific selection was done regarding the journals in the database. The screening was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to extract the top 100 most cited articles in the field of artificial intelligence usage in medicine. Data was analysed using SPSS 23. Results: Of the 11,571 studies identified, 100(0.86%) were analysed in detail. The studies were published between 1986 and 2021, with a median of 43 citations (IQR 53) per article. The journal ‘Artificial Intelligence in Medicine’ accounted for the highest number 9(9%)) of articles, and the United States was the country of origin for most of the articles 36(36%). Conclusion: The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.

The prospects for the use of artificial intelligence (AI) are one of the most discussed topics in medicine today. The very possibility of having an omniscient virtual assistant at hand soon seems incredibly tempting, so it seems quite normally to see numerous reports on the application of each newly emerging advanced neural network technology in various fields of medicine and biotechnology. Of course, the emergence of ChatGPT caused the greatest public outcry in recent times, because the new natural language processing algorithm underlying it has allowed human to bring communication between man and machine to a whole new level. Of course, despite the myriad benefits of using AI, the use of ChatGPT and other AI tools in medicine raises many ethical and legal questions. However, it is worth remembering the history of the emergence of any other breakthrough technology to accept the existing controversy as an integral part of progress. The desire of a person to make his work easier and shift part of the work onto a computer always makes him take a step forward in the development of technologies, which, in the end, do not allow a person to work less, but make him work in a new way.

The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. ‘Machine learning’ and ‘mastitis’ were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful.

Scientific manuscripts are the basis for the transmission of scientific data among physicians in all fields of medicine. To teach young oncologists the skills needed to author a paper, we decided to emulate how experienced clinicians perform this task. The first step is to create a spreadsheet with all the clinical data gathered and submit it to a statistical analysis using a statistical software package. The most important results are presented in the graphs and tables. The results should be explained in a logical and understandable manner. Writing the “Materials and Methods” section follows, with all the technical information that any other researcher may need to reproduce elsewhere the study in question. A critical-thinking stage, in which a review of the pertinent literature is conducted with the use of a reference management software, should provide all the knowledge and questions to write the “Introduction” and “Discussion” sections. The “Abstract” and “Title” are the final sections to be created. Following these steps, the author can correct the first draft of the manuscript for submission to a specific journal. Choosing the right journal and answering the reviewers' comments are also important steps in this process. Even if a young oncologist does not embark on an academic career, learning how to write a scientific manuscript is believed to be the best way to teach them how to read such manuscripts during their lifelong continuous self-education.

We study a three-species cyclic model whose organisms are vulnerable to contamination with an infectious disease which propagates person-to-person. We consider that individuals of one species perform a self-preservation strategy by reducing the mobility rate to minimise infection risk whenever an epidemic outbreak reaches the neighbourhood. Running stochastic simulations, we quantify the changes in spatial patterns induced by unevenness in the cyclic game introduced by the mobility restriction strategy of organisms of one out of the species. Our findings show that variations in disease virulence impact the benefits of dispersal limitation reaction, with the relative reduction of the organisms’ infection risk accentuating in surges of less contagious or deadlier diseases. The effectiveness of the mobility restriction tactic depends on the deceleration level and the fraction of infected neighbours which is considered too dangerous, thus triggering the defensive strategy. If each organism promptly reacts to the arrival of the first viral vectors in its surroundings with strict mobility reduction, contamination risk decreases significantly. Our conclusions may help biologists understand the impact of defensive strategies in ecosystems during an epidemic.

Abstract This paper presents the case of an adjunct university professor to illustrate the dilemma of using artificial intelligence (AI) technology to grade student papers. The hypothetical case discusses the benefits of using a commercial AI service to grade student papers—including discretion, convenience, pedagogical merits of consistent feedback for students, and advances made in the field that yield high-quality work—all of which are achieved quickly. Arguments against using AI to grade student papers involve cost, privacy, legality, and ethics. The paper discusses career implications for faculty members in both situations and concludes with implications for researchers within the discourse on academic integrity.

Background: The influence of AI and Big Data on the healthcare sector and health delivery systems provides a hopeful path to achieving the Sustainable Development Goals (SDGs) by way of improved decision-making and operational efficiencies. Methodology: A review of existing literature was carried out to evaluate the current landscape of AI applications in healthcare and its potential implications for SDG achievement. Results: Big data is at the center of health transformation, which, combined with artificial intelligence and Internet of Things, enables real-time processing and analysis of huge datasets. Big data enhances customization in marketing, streamlines operations in manufacturing and redefines healthcare by means of precision medicine. Big data also helps to drive changes in society through better public policy choices and steps taken to ensure sustainable development. It is through its analytic power that big data can identify obscure patterns and insights which can lead to pioneering solutions and strategic growth. Conclusion: Applying big data and AI strategically plays a crucial role in advancing the sustainable development agenda. However, we must exercise responsibility when utilizing the power of data. It is important to enforce strict measures of protecting data so as to avoid its misuse and also empower underdeveloped regions with relevant tools and capabilities for efficient data management. This way proactive moves will make sure that all people benefit from the information revolution, and it helps to drive global progress on SDGs.

Artificial Intelligence, and Machine Learning especially, are becoming increasingly foundational to our collective future. Recent developments around generative models such as ChatGPT, and DALL-E represent just the tip of the iceberg in new gadgets that will change the way we live our lives. Convolutional Neural Networks (CNNs) and Transformer models are at the heart of advancements in the autonomous vehicles and health care industries as well. Yet these models, as impressive as they are, still make plenty of mistakes without justifying or explaining what aspects of the input or internal state, was responsible for the error. Often, the goal of automation is to increase throughput, processing as many tasks as possible in a short a period of time. For some use cases the cost of mistakes might be acceptable as long as production is increased above some set margin. However, in health care, autonomous vehicles, and financial applications, the cost of a mistake might have catastrophic consequences. For this reason, industries where single mistakes can be costly are less enthusiastic about early AI adoption. The field of eXplainable AI (XAI) has attracted significant attention in recent years with the goal of producing algorithms that shed light into the decision-making process of neural networks. In this paper we show how robust vision pipelines can be built using XAI algorithms with the goal of producing automated watchdogs that actively monitor the decision-making process of neural networks for signs of mistakes or ambiguous data. We call these robust vision pipelines, squinting pipelines.

Artificial intelligence or AI is a hot topic. There are currently 100+ million users of ChatGPT (GPT = generative pre-trained transformer), which was designed and implemented by OpenAI. This is a significant portion of the entire world population. Numerous accolades have been given to the initiative. However, some suggest that AI could be used for nefarious purposes, it may eliminate jobs, provide erroneous information, and it might be used for cheating at work or school. Such events may have already occurred during the few months since the inception of recent AI chatbots. Now might be the point to address this issue from the perspective of what helpfulness can be incurred by AI in scientific paper writing. This discussion stems from the recent querying and probing ChatGPT-4.0 for this purpose.

Phishing is one of the major web social engineering attacks. This has led to demand for a better way to predict and stop them in a commercial environment. This paper seeks to understand the research done in the field and analyse the next steps forward. This is done by focusing on what goes into the selection of proper features, from manual selection to the use of Genetic Algorithms such as ADABoost and MultiBoost. Then a look into the classifiers in use, Neural Networks and Ensemble algorithms which were prominent alongside some novel approaches. This information is then processed into a framework for cloud-based and client-based phishing website detection, alongside suggestions for possible future research and experiments that could help progress the field.

Abstract To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.

The world is undergoing a thoughtful revolution with the arrival of the intelligent information era. The central realms accompanying smart living such as transportation, entertainment, healthcare and smart cities are projected to improve service quality assuring a high-end user experience. Future mobile networks are projected to foster the future of ubiquitously connected data-intensive intelligent society powered with complete automation by seamless integrating of all sorts of wireless networks spread over the ground, underwater, air and space. This special issue aims to bring together foremost researchers in academia and engineering from various backgrounds to disseminate to the technical community an outline of emerging technologies, advanced architectures, challenges, open issues and future directions of modern networks in artificial intelligence, internet of things, and blockchain-based applications.

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.

In this transformative study, machine learning (ML) and t‐distributed stochastic neighbor embedding (t‐SNE) are employed to interpret intricate patterns in colorimetric images of cold atmospheric plasma (CAP)‐treated water. The focus is on CAP's therapeutic potential, particularly its ability to generate reactive oxygen and nitrogen species (RONS) that play a crucial role in antimicrobial activity. RGB, HSV, LAB, YCrCb, and grayscale color spaces are extracted from the colorimetric expression of oxidative stress induced by RONS, and these features are used for unsupervised ML, employing density‐based spatial clustering of applications with noise (DBSCAN). The DBSCAN model's performance is evaluated using homogeneity, completeness, and adjusted rand index with a predictive data distribution graph. The best results are achieved with 3,3′,5,5′‐tetramethylbenzidine–potassium iodide colorimetric assay solution immediately after plasma treatment, with values of 0.894, 0.996, and 0.826. t‐SNE is further conducted for the best‐case scenario to evaluate the clustering efficacy and find the best combination of features to better present the results. Correspondingly, t‐SNE enhances clustering efficacy and adeptly handles challenging points. The approach pioneers dynamic and comprehensive solutions, showcasing ML's precision and t‐SNE's transformative visualization. Through this innovative fusion, complex relationships are unraveled, marking a paradigm shift in biomedical analytical methodologies.

Abstract Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

Abstract Searching relevant papers is a fundamental task for the elaboration of secondary studies. This task is known to be tedious and time‐consuming when it is made manually, especially with the presence of several academic repositories. Recently, Semantic Scholar has emerged as a new artificial intelligence‐based search engine enabling a set of valuable features. The present study investigates the role of Semantic Scholar in retrieving relevant papers for performing secondary studies in software engineering. For this sake, an examination is performed to check the ability of Semantic Scholar to locate included papers in recent and well‐established secondary studies. Afterwards, a hybrid and automatic search strategy is introduced making use of Semantic Scholar as a sole search engine and it incorporates: automatic search, snowballing, and use of Computer Science Ontology (CSO) and Software Engineering Body of Knowledge (SWEBOK) for refining queries. The proposed strategy is validated by replicating the search of high‐quality secondary studies in the software engineering field. To guarantee objectivity, a systematic search is conducted of recent secondary studies published in the field since 2015. For the coverage test, Semantic Scholar is examined to locate primary papers of selected secondary studies and identify missing venues. The proposed search strategy is used to check the ability to retrieve primary papers of each secondary study. The systematic search yielded 20 high‐quality secondary studies with 1337 distinct primary papers. The coverage test revealed that Semantic Scholar covers 98.88% of the papers. The proposed search strategy enabled the full replication of 13 studies and more than 90% for the 7 remaining studies.

This paper originates from empirical studies of translation process, from the epistemological perspective, we can relate the translation process to how subjective spirit can attach to the external world. To unfold the translation process ontologically and epistemologically, we integrate the logical abduction inference with semantic theory "influence the best explanation". After a large amount of TAPs data collections and theoretical discussions in translation process empirical studies (including our research), we conclude that a translator dynamically employ both of the subjective translation units and objective translation units during the translation process by combing subjectivity and objectivity physically, mentally and philosophically; As the translating conduction went on --- the proceeding of deverbalization translation strategies and mental lexical access strategies is on, the translators flexibly and frequently appeal to abduction hypothesis and abduction reasoning. We end this paper with a reflection on the experimental design of the translation process empirical study: the Survivor-ship bias points out the judge's bias to high-proficiency participants; and the limitation of inductive reasoning and analogical reasoning lead to the relevant rigor -skepticism. We call for a closer interdisciplinary and a diversity research on philosophy, logic and empirical studies on translation process.

Medical imaging is currently using artificial intelligence-based technologies to aid evaluate diagnostic information images, particularly in enforcing kidney stones. Artificial intelligence technology continues to develop, many studies show that deep learning is more widely used compared to traditional machine learning, so an Artificial intelligence system is needed to assist the accuracy of health diagnoses, thus helping in the field of radiology health. The aim of the research is to use artificial intelligence with deep learning models to help detect abnormalities in the kidneys. This research method is a literature review of Scopus data related to deep learning in medical imaging in detecting kidney stones. The results of using Artificial Intelligence in medical imaging can be used in diagnosing diseases including detecting Covid-19, musculoskeletal, calcium scores on Cardiac CT, liver tumors, urinary tract lesions, examination of the abdomen and kidney stones. Utilization of Artificial Intelligence in detecting kidney stones can be done with various classification models including XResNet-50, ExDark19, CystoNet, CNN, ANN. Using the right model and having a high accuracy value can help radiologists to accurately detect kidney stones.

In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series.

Abstract This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution. Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient’s protocol, tracking the patient’s dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.

Leveraging artificial intelligence (AI), this study revolutionizes examination management and education in universities by developing an intelligent system encompassing comprehensive management, pre-examination activities, scheduling, and preparation. The system also features a quality management component for educational outcomes. An enhanced genetic algorithm introduces an adaptation function to optimize intelligent grouping, facilitating effective exam paper distribution. Applied at Y University, our innovative approach significantly refines exam paper difficulty (ranging between 0.5016 and 0.5581) and differentiation (0.3845 to 0.4596), showcasing the intelligent algorithm’s effectiveness in exam management and contributing valuable insights to educational research.

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

This paper aims to expose and analyze the potential foundational role of Argumentation for Human-Centric AI, and to present the main challenges for this foundational role to be realized in a way that will fit well with the wider requirements and challenges of Human-Centric AI. The central idea set forward is that by endowing machines with the ability to argue with forms of machine argumentation that are cognitively compatible with those of human argumentation, we will be able to support a naturally effective, enhancing and ethical human-machine cooperation and “social” integration.

The research paper contends that Artificial Intelligence (AI) serves as a collaborative partner in architectural design, rather than merely a utility tool. To substantiate this argument, a three-phase, nine-test investigation evaluating the strengths and limitations of two prominent AI platforms: Midjourney AI and Stable Diffusion was undertaken. These platforms synergize human creativity and AI capabilities through features like text prompts and image references, thereby fostering innovative avenues in architecture. Our analysis indicates that Midjourney AI is proficient in generating initial design concepts, largely thanks to its extensive data libraries, but is deficient in design refinement and user control. Conversely, Stable Diffusion empowers designers with greater control via features like ControlNet but sacrifices visual clarity due to its smaller generative models. Both platforms share a common flaw: an overemphasis on aesthetics and shape at the expense of functional understanding. Building upon these empirical observations, the paper outlines strategies for designers to reasonably leverage AI in optimising workflows. It confirms two key hypotheses concerning the interplay of creativity, control, and collaboration, emphasising that both human architects and AI systems benefit from iterative feedback and continuous adaptation. In summary, the study posits that AI is not just an adjunct technology but a transformative force with the capacity to fundamentally alter architectural design processes, paving the way for a new paradigm where human expertise and machine capabilities converge for enriched design outcomes.

Unmanned aerial vehicles are a synergistic technology that complements other new technologies and is in constant development. The paper focuses on using artificial intelligence (AI) in security surveillance. This article aims to develop an unmanned aerial system to monitor border areas and detect human silhouettes in challenging environmental conditions. For this purpose, thermal imaging technology was used for remote sensing in combination with artificial intelligence, particularly Yolo algorithms. After testing various Yolo versions, the target algorithm was implemented on an NVIDIA Jetson Xavier edge device. Prototyping of the AI-based thermal detection system was carried out on the DJI S900 multi-rotor aircraft. The final solution was implemented on a vertical take-off and landing aircraft. A summary containing observations and conclusions, as well as perspectives for the development of future work, are included at the end of the paper.

The economic meltdowns across countries are exacerbated by COVID-19 and its aftermath, which bear the brunt of perennial devastations characterised by abject poverty. Although somewhat peculiar, higher institutions of education (HIE) have modified learning and teaching strategies to accommodate the deleterious effects of COVID-19. Conversely, while adapting, the emergence of artificial intelligence (AI) necessitates that HIE reprioritises a plethora of factors, including pedagogies. This approach catalyses the comprehension of volatile, uncertain, complex, and ambiguous (VUCA) environments in economic sectors. Hence, this paper aims to evaluate the use of pre-service teachers’ AI skills and digital pedagogy at universities to determine the need to reform the curriculum for the purposes of navigating the VUCA world. Likewise, critical theory is utilised to understand the necessity of digital skills for pre-service teachers to enable HIE’s adaptation to VUCA environments. This mixed-method study involves purposively sampling 12 participants for interviews and randomly sampling 78 participants for surveys. Using critical discourse analysis, the data is analysed to postulate nuances of perspectives. The findings depict that, despite universities adapting to innovative methods, particularly AI, there is a paucity of AI content and infrastructural development that enables pre-service teachers to acquire the digital competencies and skills needed to teach robotics, coding, and maritime studies. Overall, digital skills are below average; hence, the paper recommends that universities reform their curricula to stimulate digital skills that reflect the requisite capabilities of digitalisation and AI to withstand the VUCA world.

Abstract This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings.

Abstract Purpose Despite suffering from the same disease, each patient exhibits a distinct microbiological profile and variable reactivity to prescribed treatments. Most doctors typically use a standardized treatment approach for all patients suffering from a specific disease. Consequently, the challenge lies in the effectiveness of this standardized treatment and in adapting it to each individual patient. Personalized medicine is an emerging field in which doctors use diagnostic tests to identify the most effective medical treatments for each patient. Prognosis, disease monitoring, and treatment planning rely on manual, error-prone methods. Artificial intelligence (AI) uses predictive techniques capable of automating prognostic and monitoring processes, thus reducing the error rate associated with conventional methods. Methods This paper conducts an analysis of current literature, encompassing the period from January 2015 to 2023, based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results In assessing 25 pertinent studies concerning predicting neoadjuvant treatment (NAT) response in breast cancer (BC) patients, the studies explored various imaging modalities (Magnetic Resonance Imaging, Ultrasound, etc.), evaluating results based on accuracy, sensitivity, and area under the curve. Additionally, the technologies employed, such as machine learning (ML), deep learning (DL), statistics, and hybrid models, were scrutinized. The presentation of datasets used for predicting complete pathological response (PCR) was also considered. Conclusion This paper seeks to unveil crucial insights into the application of AI techniques in personalized oncology, particularly in the monitoring and prediction of responses to NAT for BC patients. Finally, the authors suggest avenues for future research into AI-based monitoring systems.

This paper explores the application and development trends of artificial intelligence (AI) technology, particularly machine learning and natural language processing in the field of failure analysis. Failure analysis is a crucial method for ensuring the reliability and safety of equipment, and is widely used in aerospace, automotive manufacturing, electronic devices, and other fields. Traditional failure analysis methods often rely on expert experience, which is time-consuming and laborious. By integrating AI’s powerful data processing capabilities with traditional methods, the accuracy and efficiency of analysis have been significantly enhanced. In terms of failure mode diagnosis, AI can rapidly and accurately identify various fault modes and provide precise diagnostic results. In failure cause diagnosis, AI integrates data from multiple sources to uncover complex failure factors and potential causal relationships, improving diagnostic reliability. In failure prediction, machine learning can accurately forecast material lifespan and strength, reducing experimental time and costs. In failure prevention, AI offers new approaches to effectively reduce the risk of failure and lower product maintenance costs. The paper also looks forward the future development prospects of AI in failure analysis and highlights challenges and recommendations in the areas, such as data quality improvement, model optimization, interdisciplinary collaboration, and ethical and safety issues.

[Purpose] This paper seeks to develop, from the current doctrine and regulations, some guidelines for the debate that the Law will have to give shortly, about a new type of personality and its attributes those humanoid robots that are capable of questioning their initial programming and take actions on your own. [Methodology] It is a work developed through a legal-hermeneutical and documentary analysis methodology. The authors used legal doctrine as sources, especially books and book chapters, research results, as well as articles in scientific journals evaluated by peers and indexed in legal databases. To develop the objective of the paper, the authors outlined several stages, first, the search and recovery of information, second a critical analysis, and finally the interpretation of all of the above. [Findings] The authors present their results, on the one hand, with an analysis of the personality of humanoid robots, and on the other, with some reflections on their attributes. The paper presents a critical study on the legal situation of the regulation of the autonomy of the will; which must be understood as the materialized faculty of self-determination that these robots could have in making their own decisions and concerning the rights, obligations, and responsibilities that the Law decides to recognize. All of the above, from artificial intelligence systems that are not subordinated to their initial programming, or the immediate and permanent control of a natural person.

Abstract Background Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent, and deadly type of liver cancer. With the advent of deep learning techniques, significant advancements have been made in simplifying and optimizing the feature selection process. Objective Our scoping review presents an overview of the various deep learning models and algorithms utilized to address feature selection for HCC. The paper highlights the strengths and limitations of each approach, along with their potential applications in clinical practice. Additionally, it discusses the benefits of using deep learning to identify relevant features and their impact on the accuracy and efficiency of diagnosis, prognosis, and treatment of HCC. Design The review encompasses a comprehensive analysis of the research conducted in the past few years, focusing on the methodologies, datasets, and evaluation metrics adopted by different studies. The paper aims to identify the key trends and advancements in the field, shedding light on the promising areas for future research and development. Results The findings of this review indicate that deep learning techniques have shown promising results in simplifying feature selection for HCC. By leveraging large-scale datasets and advanced neural network architectures, these methods have demonstrated improved accuracy and robustness in identifying predictive features. Conclusions We analyze published studies to reveal the state-of-the-art HCC prediction and showcase how deep learning can boost accuracy and decrease false positives. But we also acknowledge the challenges that remain in translating this potential into clinical reality.

Our paper aims to explore how doctoral EdD students in their thesis stage made use of digital technologies, social media (SM), and artificial intelligence (AI) tools. In this study, AI does not involve data on the use of the new generation of AI, which has been introduced in more recent years after this study took place. This paper refers to a 2nd stage qualitative analysis of semi-structured interviews collected from research undertaken in 2018 into student use of digital technologies in an online professional doctorate programme. The original study utilised an exploratory case study approach, an online survey (n = 28), and a series of semi-structured interviews (n = 9). This study will add further qualitative findings and perspectives to those that emerged in the previous study. This study will help to provide new insights into the interview data that was used to inform the initial paper resulting from the research in 2018. We argue that the unique characteristics of online doctoral students as both individuals and learners determine the popularity of some digital tools and that, in order to make the best use of the full range available they need to develop new skills and a better understanding of the pedagogy associated with those digital tools and the value they can add to an educational context. This can be achieved through the provision of more systematic coaching and support systems. This in turn will contribute to enhancing students’ feelings of belonging to a real academic learning community and their self-confidence and autonomy as online learners in general and in their performance in a Viva in particular.

Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, developed based on interdisciplinary research in linguistics, computer technologies, and artificial intelligence. It focuses on syntactic and lexical techniques employed by neural network models for text compression. The paper presents performance examples of such AI-powered services as QuillBot, Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT. The contemporary automated models proved effective in using extractive and abstractive methods to generate summaries of varying quality and length. The extractive approach relies on identifying the most significant sentences from the original text, while abstractive algorithms create new sentence structures that preserve the main idea of the original content. Automated summarizers effectively utilize text compression techniques that are inherent to human approach to text processing, e.g., they exclude redundant information, simplify complex structures, and generalize data. These technologies provide high accuracy and coherence in the generated summaries, though each summarization model has its limitations. Optimal results depend on the specifics of the task at hand: extractive models provide brevity and precision while abstractive ones allow for deeper semantic processing. Automated summarization is becoming an important tool in various fields that require effective analysis and processing of large text data.

Recently, testing and using micro-unmanned vehicles, such as unmanned aerial vehicles (UAVs), in scenarios such as supply transportation, agricultural management, and military operations have become more common. It is no longer sufficient to control a single UAV to accomplish all missions. With the increasing complexities associated with operating and task requirements, an unmanned swarm requires a series of algorithms with higher efficiency, greater generalization ability, and better adaptability than the earlier algorithms. A combination of unmanned swarms with artificial intelligence is becoming a common solution to manage the above requirements. Deep reinforcement learning (DRL) is a machine learning method that combines deep learning (DL) and reinforcement learning (RL); therefore, this method has the advantages of DL and RL. Using an RL method, an agent can learn from the environment by trial and error and make decisions that autonomously obtain high scores. However, when the given environment is complex, the decision function of the agent may be too difficult to implement and then the agent cannot make the correct decision. The DL method has strong fitting ability. A suitable deep neural network can simulate any linear or nonlinear function. If the DL method is used to simulate the decision function in RL, the hybrid method can solve the problem that an agent cannot solve and make a correct decision in a complex environment. The combination of an unmanned swarm and a DRL method has been widely studied. This paper introduces the concept of DRL from the perspective of principles and characteristics. This paper analyzes several typical DRL algorithms, discusses the various control requirements of a UAV swarm, and then focuses on the achievements of combining DRL and a UAV swarm control. Finally, this paper presents viewpoints on the application prospects and challenges related to landing and transformation in the combination field. The concept of an unmanned swarm originated from the study of the behavior of biological groups. Several species of bees, ants, birds, fish, and other creatures exhibit complex group behaviors. These clusters comprise many independent individuals in accordance with certain aggregation rules to form a coordinated, orderly group movement mechanism. Similar to biological clusters, in the field of robotics or UAVs, unmanned swarm systems are crowded intelligent systems. These systems consist of multiple homogeneous or heterogeneous unmanned equipment to achieve mutual behavior coordination and jointly complete specific tasks through interactive feedback and incentive response of information. In practical applications, an unmanned swarm system needs to meet the requirements of an open environment, a changeable situation, limited resources, and real-time responses. This system needs to have multicore collaborative capabilities such as distributed collaborative perception, intelligent collaborative decision-making, and robust collaborative control. The distributed intelligent collaborative control method based on DRL can fully meet the control requirements of high intelligence and robustness of unmanned cluster systems.

As artificial intelligence (AI) is becoming increasingly integrated into critical infrastructures, it brings about both transformative benefits and unprecedented risks. AI has the potential to revolutionize the efficiency, reliability, and responsiveness of essential services, but it can also offer these benefits along with the vulnerability to a growing array of sophisticated adversarial attacks. This paper explores the evolving landscape of adversarial threats to AI systems, highlighting the potential of nation-state actors to exploit these vulnerabilities for geopolitical gains. A range of adversarial techniques is examined, including dataset poisoning, model stealing, and privacy inference attacks, and their potential impact on sectors such as energy, transportation, healthcare, and water management is assessed. The consequences of successful attacks are substantial, encompassing economic disruption, public safety risks, national security implications, and the erosion of public trust. Given the escalating sophistication of these threats, this paper proposes a comprehensive security framework that includes robust incident response protocols, specialized training, the development of a collaborative ecosystem, and the continuous evaluation of AI systems. The findings of this study 11 underscore the critical need for a proactive approach to AI security in order to safeguard the future of critical infrastructures in an increasingly AI-driven world.

Integrating Coding, Robotics, and Artificial Intelligence (AI) into educational practices represents a paradigm shift in how knowledge is imparted and acquired. This paper explored the multifaceted impact of these advanced technologies on contemporary education, highlighting their potential to enhance engagement, foster personalized learning experiences, and cultivate essential skills for the future. The study aimed to provide a comprehensive overview of how Coding, Robotics, and AI reshape the educational landscape by delving into specific applications, such as interactive learning environments and intelligent tutoring systems. Additionally, the discussion addressed the challenges and ethical considerations associated with these technological advancements, emphasizing the importance of a balanced approach that harnesses the benefits while addressing potential concerns. This paper is underpinned by the Theory of Situated Learning. A sample of five secondary schools in the OR Tambo Coastal District was selected for this study, with a focus on the experiences, behaviours, and social interactions of 15 teachers. Based on the study’s interpretive paradigm, it was discovered that certain teachers were not aware of the importance of increasing their digital professional knowledge as we move toward the Fourth Industrial Revolution (4IR). In addition, infusing coding and robotics in educational practices required a shift to digital learning. The study recommends encouraging teachers to acquire new skills to avoid stagnation. Although not every teacher found updating their skills to be a motivating factor for continuing professional development, the study underscores the significance of continuous learning for personal growth and improvement.

Abstract Background Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist among the public and medical community. Given the rapid and transformative recent growth of AI in cardiovascular care, to inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients’ and healthcare providers’ perspectives when using AI in cardiovascular care. Methods In this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI-based medical devices (interventions of interest) in the context of cardiovascular care from patients’, caregivers’, or healthcare providers’ perspectives. The search was completed on May 24, 2022 and was not limited by date or study design. Results After reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues (n = 59, 40.7%); risk of healthcare inequity or disparity (n = 36, 24.8%); risk of patient harm (n = 24, 16.6%); accountability and responsibility concerns (n = 19, 13.1%); problematic informed consent and potential loss of patient autonomy (n = 17, 11.7%); and issues related to data ownership (n = 11, 7.6%). Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients’ interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights. Conclusion This review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients’ and healthcare providers’ perspectives. Successful integration of AI into cardiovascular care necessitates implementation of mitigation strategies. These strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.

Abstract The paper casts a spotlight on one of the manifold legal questions that arise with the proliferation of artificial intelligence. This new technology is attractive for many fields, including public administration, where it promises greater accuracy and efficiency, freeing up resources for better interaction and engagement with citizens. However, public powers are bound by certain constitutional constraints that must be observed, regardless of whether decisions are made by humans or machines. This includes the non-delegation principle, which aims to limit the delegation and sub-delegation of decisions affecting citizens’ rights in order to ensure governmental accountability, reviewability, and contestability. This puts some constraints on the automation of decision-making by public entities, as algorithmic decision-making entails delegating decisions to software development companies on the one hand, and to algorithms on the other. The present paper reveals and explains these constraints and concludes with suggestions to navigate these conflicts in a manner that satisfies the rule of law while maximizing the benefits of new technologies.

Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime.

This article investigates the processes of knowledge and information systems management from the position of the dataset approach, which focuses on the specifics of the digital economy and the role of Big Data and AI in the processes of labour intellectualisation. According to this, the paper discloses theoretical foundations and features of the key definitions of the research, such as knowledge, knowledge management, and Big Data, and analyses challenges and opportunities that are connected with the possibility of using digital technologies for the collection and processing of data and their usage in management. Special attention is paid to the ability of Big Data and tools of AI to transform non-structured data into a clear information flow or knowledge. The methodological basis of the research is formed by the dataset approach, which determines regularities and principles of work with large arrays of data, and the complex approach, which agrees theoretical concepts of knowledge management and the practical aspects of using digital technologies to raise the effectiveness of information systems of knowledge management. The paper uses the methods of analysis, comparative analysis, synthesis, empirical overview of literature, comparison, observation, and generalisation. The chief value of this work lies in the study of the practical aspects of knowledge management with the use of Big Data and AI, which is based on the identification of the effect of these technologies on the processes of knowledge management, determination of the sequence of operations, and generalisation of the practical experience of such systems’ functioning and their characteristics from the position of the ability to ensure deep processing of non-structured and other information.

(Series Information) European Papers - A Journal on Law and Integration, 2024 9(1), 105-116 | European Forum Insight of 20 May 2024 | (Table of Contents) I. Introducing the impact of Artificial Intelligence. – II. The AI Act: the risk architecture. – III. Further AI policies: development incentives and state regulatory frameworks. – IV. Proposals for legislation and other measures in Italy. – V. The human-centric approach. | (Abstract) The development of new emerging technologies, such as artificial intelligence, has sparked a scientific debate on their risks and benefits. This debate necessitates legal and regulatory considerations, particularly regarding the balance between technological growth and the protection of human rights. This Insight analyses the legal framework established by the European Union in its initial regulatory measures. The Insight highlights the importance of considering the human-centric perspective and adopting a risk-based methodology in the Artificial Intelligence Act. It also mentions the AI regulatory measures proposed by Member States, with a particular focus on Italy.

The role of artificial intelligence in shaping Islamic finance services reflects a fusion of modern technology and Islamic principles, offering innovative opportunities to enhance financial services in ways that are compatible with Islamic law. This research paper aims to study the role of artificial intelligence in shaping Islamic finance services by addressing key theoretical literature related to artificial intelligence and Islamic finance and examining their relationship. Using a descriptive-analytical approach, the paper reviews current and future applications of artificial intelligence in financial services, as well as the major challenges in applying artificial intelligence in Islamic finance. The study concludes that artificial intelligence can enhance innovation in the Islamic finance sector by providing advanced and innovative solutions that better meet customer needs.

In the context of global technological transformations and active penetration of artificial intelligence technologies and neural network algorithms into the digital space of socio-political communications, a new format of socio-political interactions is being formed today, defined by the widespread use of “smart” tools and mechanics of content generation and broadcasting in communication processes. In this regard, there is a demand for scientific research into the processes of the influence of artificial intelligence and neural networks on the content and functional parameters of modern socio-political communication interactions. The purpose of this study was to determine the relationship between “smart” technologies and the transformation of the contemporary space of sociopolitical communications in terms of the formation of risks, threats and challenges of a new type associated with the introduction of digital solutions based on the application of artificial intelligence technologies and neural network algorithms in the current practice of sociopolitical communications. The main research question is the extent and nature of the impact of the introduction of artificial intelligence technologies and neural network algorithms in the contemporary space of socio-political communications. To achieve the research goal and answer the research question, the authors applied the method of discourse analysis of scientific sources on the research topic, as well as conducted an international expert study, which allowed to identify key expert positions on the research topic and, based on the compilation of expert assessments, to identify the main risks, threats and challenges of socio-political nature formed in the processes of modern digital communications. The main conclusion of the paper is the thesis, substantiated and confirmed by the results of the research, that there is a wide range of socio-political risks, threats and challenges associated with the introduction of artificial intelligence technologies and neural network algorithms into the processes of contemporary digital communication. The paper provides a meaningful analysis of these risks, threats and challenges of a new type that can significantly affect the nature, dynamics and scenarios of the contemporary technological development of key spheres of life of the contemporary state and society.

Over the past two decades, language modeling (LM) has emerged as a primary methodology for language understanding and generation. This technology has become a cornerstone within the field of natural language processing (NLP). At its core, LM is designed to train models to predict the probability of the next word or token, thereby generating natural and fluent language. The advent of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers and GPT-3, marks a significant milestone in the evolution of LM. These LLMs have left a profound impact on the field of artificial intelligence (AI) while also paving the way for advancements in other domains. This progression underscores the power and efficacy of AI, illustrating how the landscape of AI research has been reshaped by the rapid advancement of LLMs. This paper provides a comprehensive review of the evolution of LLMs, focusing on the technical architecture, model scale, training methods, optimization techniques, and evaluation metrics. Language models have evolved significantly over time, starting from initial statistical language models, moving onto neural network-based models, and now embracing the era of advanced pre-trained language models. As the scale of these models has expanded, so has their performance in language understanding and generation. This has led to notable results across various sectors, including education, healthcare, finance, and industry. However, the application of LLMs also presents certain challenges, such as data quality, model generalization capabilities, and computational resources. This paper delves into these issues, providing an analysis of the strengths and limitations of LLMs. Furthermore, the rise of LLMs has sparked a series of ethical, privacy, and security concerns. For instance, LLMs may generate discriminatory, false, or misleading information, infringe on personal privacy, or even be exploited for malicious activities such as cyber-attacks. To tackle these issues, this paper explores relevant technical measures, such as model interpretability, privacy protection, and security assessments. Ultimately, the paper outlines potential future research trends of LLMs. With ongoing enhancements to model scale and efficiency, LLMs are expected to play an even greater role in multimodal processing and societal impact. For example, by integrating information from different modalities, such as images and sound, LLMs can better understand and generate language. Additionally, they can be employed for societal impact assessment, providing support for policy formulation and decision-making. By thoroughly analyzing the current state of research and potential future directions, this paper aims to offer researchers valuable insights and inspiration regarding LLMs, thereby fostering further advancement in the field.