AI Technology Research Articles

AI-driven Predictive Analytics for Enhancing Cybersecurity in a Post-pandemic World: a Business Strategy Approach

Due to heightened technological advancement that has been ignited by the COVID-19 pandemic, the use of technology in running businesses has surged and therefore making businesses prone to cybersecurity threats. The present paper explores the potential of AI as a tool for advancing cybersecurity in the world after a pandemic, and how business organizations can organize its application for protection against new emerging forms of threats. The research draws on secondary industry data and primary case studies of the businesses with AI-based cybersecurity in place. The outcome demonstrates that emerging AI technologies, including machine learning and neural networks, have cut threat detection time by 40% and enhanced the precision of recognizing potential security threats by 30%, beyond conventional cybersecurity solutions. The new and distinct feature of this paper is to elucidate that new threats can be predicted with the help of AI, while there is a continuous need for new training for AI in relation to cyber threats. Drawing from the literature, this paper, therefore, posits that incorporation of AI-based predictive analytics into business cybersecurity models and systems is not only vital for the defense of data and infrastructure against hackers but equally important for the achievement of sustainable, long-term business continuity during the current day, age of technology and globalization.

Deep Learning Models for Early Detection of Ovarian Cancer: A Systematic Review of Ultrasound-Based Diagnostic Tools

Ovarian Cancer (OC) remains one of the most lethal gynecological malignancies, with a global 5-year survival rate of only 45%. This project aimed to assess the potential of deep learning-based diagnostic tools in improving early detection of ovarian cancer, particularly through ultrasound imaging. The primary research problem lies in the challenge of accurately diagnosing OC at an early stage, as traditional imaging techniques often rely on subjective interpretations, leading to inconsistent results. To address this issue, a systematic review was conducted following PRISMA guidelines, evaluating studies published between January 2018 and May 2023 in the Scopus and PubMed databases. These studies employed deep learning or artificial intelligence models for the diagnosis, prognosis, or identification of OC from ultrasound images. Of the 101 studies screened, 9 met the inclusion criteria. The included studies reported diagnostic accuracies of deep learning models ranging from 75% to 100%, with sensitivities between 85% and 99%. The conclusions indicate that deep learning models significantly enhance the diagnostic accuracy of ovarian cancer, offering a promising non-invasive tool for early detection. This research underscores the importance of integrating AI technologies into clinical practice to improve survival outcomes for OC patients.

Advancing AI-Driven Linguistic Analysis: Developing and Annotating Comprehensive Arabic Dialect Corpora for Gulf Countries and Saudi Arabia

This study harnesses the linguistic diversity of Arabic dialects to create two expansive corpora from X (formerly Twitter). The Gulf Arabic Corpus (GAC-6) includes around 1.7 million tweets from six Gulf countries—Saudi Arabia, UAE, Qatar, Oman, Kuwait, and Bahrain—capturing a wide range of linguistic variations. The Saudi Dialect Corpus (SDC-5) comprises 790,000 tweets, offering in-depth insights into five major regional dialects of Saudi Arabia: Hijazi, Najdi, Southern, Northern, and Eastern, reflecting the complex linguistic landscape of the region. Both corpora are thoroughly annotated with dialect-specific seed words and geolocation data, achieving high levels of accuracy, as indicated by Cohen’s Kappa scores of 0.78 for GAC-6 and 0.90 for SDC-5. The annotation process leverages AI-driven techniques, including machine learning algorithms for automated dialect recognition and feature extraction, to enhance the granularity and precision of the data. These resources significantly contribute to the field of Arabic dialectology and facilitate the development of AI algorithms for linguistic data analysis, enhancing AI system design and efficiency. The data provided by this research are crucial for advancing AI methodologies, supporting diverse applications in the realm of next-generation AI technologies.

Battle Simulator for Multi-Robot Mission Simulation and Reinforcement Learning

As AI technology advances, interest in performing multi-robot autonomous missions for manned-unmanned teaming (MUM-T) is increasing. In order to develop autonomous mission performance technology for multiple robots, simulation technology that reflects the characteristics of real robots and can flexibly apply various missions is needed. Additionally, in order to solve complex non-linear tasks, an API must be provided to apply multi-robot reinforcement learning technology, which is currently under active research. In this study, we propose the campaign model to flexibly simulate the missions of multiple robots. We then discuss the results of developing a simulation environment that can be edited and run and provides a reinforcement learning API including acceleration performance. The proposed simulated control module and simulated environment were verified using an enemy infiltration scenario, and parallel processing performance for efficient reinforcement learning was confirmed through experiments.

A New Right to Procedural Accuracy: A Governance Model for Digital Evidence in Criminal Proceedings

This paper motivates and studies the feasibility of a new digital right to procedural accuracy (RPA) for digital evidence processing in criminal investigations. The need to guarantee a new principle of procedural accuracy under Art. 6 of the European Convention on Human Rights (ECHR) is based on the concern that digital forensic science and AI technology have a significant impact on individuals’ rights in criminal proceedings, which are neither coherently nor comprehensively addressed. The personal and material scope of RPA are examined and include: (i) protection against unreliable digital evidence processing; (ii) right to access the chain of evidence, explanation, and forensic assistance; and (iii) the right to participate in the determinative stages of the digital evidence processing. Limitations of the RPA are also discussed.

Behavioral observation and assessment protocol for language and social-emotional development study in children aged 0–6: the Chinese baby connectome project

BackgroundThe global rise in developmental delays underscores the critical need for a thorough understanding and timely interventions during early childhood. Addressing this issue, the Chinese Baby Connectome Project (CBCP)’s behavior branch is dedicated to examining language acquisition, social-emotional development, and environmental factors affecting Chinese children. The research framework is built around three primary objectives: developing a 0–6 Child Development Assessment Toolkit, implementing an Intelligent Coding System, and investigating environmental influence.MethodsUtilizing an accelerated longitudinal design, the CBCP aims to enlist a minimum of 1000 typically developing Chinese children aged 0–6. The data collected in this branch constitutes parental questionnaires, behavioral assessments, and observational experiments to capture their developmental milestones and environmental influences holistically. The parental questionnaires will gauge children’s developmental levels in language and social-emotional domains, alongside parental mental well-being, life events, parenting stress, parenting styles, and family relationships. Behavioral assessments will involve neurofunctional developmental evaluations using tools such as the Griffiths Development Scales and Wechsler Preschool and Primary Scale of Intelligence. Additionally, the assessments will encompass measuring children’s executive functions (e.g., Head-Toe-Knee-Shoulder), social cognitive abilities (e.g., theory of mind), and language development (e.g., Early Chinese Vocabulary Test). A series of behavior observation. experiments will be conducted targeting children of different age groups, focusing primarily on aspects such as behavioral inhibition, compliance, self-control, and social-emotional regulation. To achieve the objectives, established international questionnaires will be adapted to suit local contexts and devise customized metrics for evaluating children’s language and social-emotional development; deep learning algorithms will be developed in the observational experiments to enable automated behavioral analysis; and statistical models will be built to factor in various environmental variables to comprehensively outline developmental trajectories and relationships.DiscussionThis study’s integration of diverse assessments and AI technology will offer a detailed analysis of early childhood development in China, particularly in the realms of language acquisition and social-emotional skills. The development of a comprehensive assessment toolkit and coding system will enhance our ability to understand and support the development of Chinese children, contributing significantly to the field of early childhood development research.Trial registrationThis study was registered with clinicaltrials.gov NCT05040542 on September 10, 2021.

Education 5.0: The development of the Ukrainian educational system in the conditions of artificial intelligence

The article presents conceptual research on the trends, vectors, perspectives, and challenges of introducing AI technologies into education system. Both worldwide trends and Ukrainian specifics are considered. One of the core findings of research is a claim that while there is transition already from Education 4.0 to Education 5.0, and rapid growth of AI solutions for education is observed, motivation of teachers themselves, their continuous advancement in these technologies based on their own interest is crucial factor.

Capturing pharmacists’ perspectives on the value, risks, and applications of ChatGPT in pharmacy practice: A qualitative study

ObjectivesTo investigate the pharmacists' perspectives on benefits and risks of using ChatGPT in pharmacy practice and explore how this disruptive and ground-breaking technology can be effectively integrated. Methods and materialsA qualitative approach that draws data from licensed pharmacists using semi-structured interviews. ResultsMost participants felt ChatGPT could enhance the compliance, use, management, safety, adherence to medication, medication counseling, minimize medication errors, and streamline medication dispensing. However, when Chat-GPT has limited information and relies on obsolete medication databases, it risks providing inaccurate recommendations and inadequate medication details. Also, most participants highlighted the difficulty in interpreting ambiguous patient input or drug descriptions when using the application. ConclusionsDespite its potential, utilizing ChatGPT in pharmacy practice must be dependent on evidence-based results that offer profound insight into AI technology.

B-119 Comparative performance of GPT-4 and CNV-ETLAI in extracting copy number variations from medical journals: Bridging the gap between large language models and specialized NLP tools in genomic data interpretation

Abstract Background Copy Number Variations (CNVs) are critical genetic markers in diversity and disease, yet their accurate extraction from medical literature remains challenging due to the complexity of genetic data. While specialized NLP models like CNV-ETLAI have been developed for this task, the advent of Large Language Models (LLMs) such as GPT-4 presents a potential alternative with broader applicability. This study evaluates the efficacy of GPT-4 against CNV-ETLAI in extracting CNVs from medical journal articles, aiming to enhance genetic research and clinical decision-making. Methods We configured GPT-4 to process and interpret medical journal PDFs, designing custom prompts for CNV information extraction. The performance of GPT-4 was benchmarked against CNV-ETLAI using a dataset of 146 true positive CNVs extracted from 23 journal articles. Performance metrics focused on accuracy in extracting CNVs from both text and tables, recognizing the importance of structured data interpretation in genomic analysis. Results CNV-ETLAI demonstrated superior accuracy, achieving a 98% success rate in CNV extraction, compared to GPT-4’s 49%. Specifically, CNV-ETLAI outperformed GPT-4 in table extraction accuracy (99% vs. 41.2%) and context extraction accuracy (96% vs. 63.2%). Despite GPT-4's lower performance, its capacity for improvement and adaptability was noted, indicating potential future applicability in medical data extraction. Conclusions The study highlights CNV-ETLAI's current superiority in extracting CNVs from medical texts, particularly in interpreting structured data like tables. However, the adaptability and potential for growth in LLMs like GPT-4 suggest they could soon become valuable tools for medical data extraction, offering a more versatile and powerful solution across a broader range of applications. The promise of LLMs, despite their current limitations, underscores the need for continued research and development in AI technologies for genomic data interpretation.

Technological optimism surpasses fear of missing out: A multigroup analysis of presumed media influence on generative AI technology adoption across varying levels of technological optimism

Drawing upon the influence of presumed media influence (IPMI) model, this study investigates indirect media effects on individuals' intentions to adopt generative AI technology, with a further examination of how variations in technological optimism impact the relationships within the IPMI model. Findings from a national survey of 1061 respondents confirmed the existence of presumed media influence in the context of generative AI technology, as individuals’ attention to generative AI technology related media content influenced their presumptions about how others use it, subsequently affecting their adoption intentions through social norm perception, attitude change, and the fear of missing out. Furthermore, a multigroup comparison uncovers divergent presumed media effects on generative AI technology adoption between subgroups characterized by their differing levels of technological optimism, in which high technological optimism was found to trump fear of missing out in driving its adoption.

Analysis of virtual simulation training teaching problems of intellectual property law based on real working situations

This article aims to take AI intelligent training software and real business drills of intellectual property as the training mode to carry out intellectual property-related training in fields such as data intellectual property creation, application, management, transformation, and education, and truly achieve the educational goal of integrating science, education and industry. First, innovate the intellectual property training platform. Through AI question-and-answer led by digital human images, introduce front-line practical experts in the field of intellectual property into the virtual simulation training teaching process, build a training scene for the entire chain protection of intellectual property and real working situations, and improve the practical effect of training and cultivating intellectual property talents. Second, deepen the social service system of intellectual property. Project team members guide students and trainees to actively participate in patent navigation, patent group projects, pilot projects, intellectual property protection projects, etc., exercise and promote the continuous improvement of the social service capabilities of various intellectual property talents. At the end of the article, it is proposed that while intellectual property law teaching adapts to the development needs of the digital age, it should take the construction and teaching application of a virtual simulation experiment platform in real work situations as the core, respond to the practical needs of social practice work, and improve students' ability to adapt to society. The organic integration of AI technology, digital human technology and practical training teaching of intellectual property law has certain reference significance for the realization of the metaverse vision.

Parasitic Platforms and the Crisis in News

ABSTRACTThe viability of news has been in rapid decline since the mid‐2000s. This poster presents a critical analysis of how news publishers themselves helped precipitate the crisis by enthusiastically adopting Big Tech platform technologies and audience‐building strategies. I show how search and social media platforms disrupted publishers' relationships with audiences and advertisers by appropriating control over news distribution and revenue. I use Anthony Giddens' structuration theory and Bruno Latour's actor‐network theory to explore the restructuring of the news industry by the sociotechnical practices and surveillance economics of today's dominating platforms. Leveraging Michel Serres' concept of social parasitism, I present a research framework for assessing symbiotic and parasitic relationships in sociotechnical systems using historical, quantitative, and qualitative methods, and to identify where news publishers still have agency to begin resolving the crisis. And I suggest the urgency of this research framework as publishers rapidly adopt new AI technologies.

A hybrid data-driven optimization and decision-making approach for a digital twin environment: Towards customizing production platforms

In the Industry 4.0 era, advanced technologies are transforming manufacturing processes and systems. Additionally, the increasing prevalence of big data and AI technologies have made decision-making using manufacturing data increasingly important. However, Small and Medium-sized Enterprises (SMEs) have encountered significant obstacles in adopting these technologies due to resource limitations and constraints. For SMEs, selecting an appropriate production strategy is challenging due to the complexity of manufacturing systems. As a response, this paper proposes a hybrid Simulation-Optimization with Multi-Criteria Decision-Making (SOMCDM) framework for SMEs to identify effective and customized production layouts. In the proposed approach, we model various production scenarios using a cellular manufacturing system. Surrogate models for different production layouts are created to basis functions using Multivariate Adaptive Regression Splines (MARS). Subsequently, the basis functions are used as fitness functions to identify optimal production parameters in a genetic algorithm. Then, optimized parameters are applied to production criteria and ranked using a multi-criteria decision-making technique. In a case study, the proposed framework is applied to select the best production platform among three scenarios for a company assembling complex products. The selected production platform improves overall manufacturing performance by 11.95% compared to the existing one. This study demonstrates the effectiveness of the proposed framework in identifying the best production platform for labor-intensive SMEs manufacturing high-mix, low-volume products using SOMCDM for a digital twin environment. The proposed framework is further detailed through a case study of a 3D printer assembly factory.

Generative artificial intelligence usage by researchers at work: Effects of gender, career stage, type of workplace, and perceived barriers

The integration of generative artificial intelligence technology into research environments has become increasingly common in recent years, representing a significant shift in the way researchers approach their work. This paper seeks to explore the factors underlying the frequency of use of generative AI amongst researchers in their professional environments. As survey data may be influenced by a bias towards scientists interested in AI, potentially skewing the results towards the perspectives of these researchers, this study uses a regression model to isolate the impact of specific factors such as gender, career stage, type of workplace, and perceived barriers to using AI technology on the frequency of use of generative AI. It also controls for other relevant variables such as direct involvement in AI research or development, collaboration with AI companies, geographic location, and scientific discipline. Our results show that researchers who face barriers to AI adoption experience an 11 % increase in tool use, while those who cite insufficient training resources experience an 8 % decrease. Female researchers experience a 7 % decrease in AI tool usage compared to men, while advanced career researchers experience a significant 19 % decrease. Researchers associated with government advisory groups are 45 % more likely to use AI tools frequently than those in government roles. Researchers in for-profit companies show an increase of 19 %, while those in medical research institutions and hospitals show an increase of 16 % and 15 %, respectively. This paper contributes to a deeper understanding of the mechanisms driving the use of generative AI tools amongst researchers, with valuable implications for both academia and industry.

UNDERSTANDING THE ROLE OF ENHANCED PUBLIC HEALTH MONITORING SYSTEMS: A SURVEY ON TECHNOLOGICAL INTEGRATION AND PUBLIC HEALTH BENEFITS

Introduction: Various changes in technologies have impacted the public health monitoring systems especially in the United States. AI and IoT, Big Data, block chains have enhanced the ways that diseases are monitored, vaccinations are administered and emergency responses are coordinated. Issues like the infrastructure constraints, privacy issues and variability in technological implementation graduality’s limit the extent to which these technologies can be scaled up in U.S. public health systems. Objective: The aim of this study is to evaluate the impact of technological improvement on the improvement of the public health monitoring systems in United States. It assesses the utilization of superior technologies and considers the observed enhancement of general health status together with the problems identified in the application of technologies. Methods: A quantitative cross-sectional survey was filled by 400 participants from the PH and technology workers working throughout the USA in the public health sector. Community and purposive sampling techniques were used and data were self-administered from the participants through an online structured questionnaire. The survey included questions about participants’ degrees of awareness of AI, IoTs, Big Data and blockchain, concerning their applicability to public health surveillance and the potential challenges to their application. Descriptive statistics, chi-square, independent sample t test, one way analysis of variance, Pearson correlation analysis and multiple regression analysis were conducted in SPSS V 26. Results: The current study revealed that the use of AI, IoT, Big Data and blockchain technologies has accelerated the enhancement of public health surveillance, including disease detection and vaccination management. Awareness of these technologies was directly related to perceived impact on public health to the extent of r = 0.75, p < 0.001. Other issues like lack of funding, lack of technical knowledge and issues surrounding privacy were cited as major reasons for inadequate scaling up. Conclusion: The results point out that new technology has a potential for advancing the system for monitor public health in the United States but there are some barriers. Challenges that prevent expansion and growth of these technologies include inadequate infrastructure, funding and data protection must be overcome in order to enhance the efficiency of the technologies to deliver on their intended health benefits. More work is necessary to expand understanding of longevity of these phenomena and ways of preventing these challenges.

Social Risks in the Era of Generative AI

ABSTRACTGenerative AI (GAI) technologies have demonstrated human‐level performance on a vast spectrum of tasks. However, recent studies have also delved into the potential threats and vulnerabilities posed by GAI, particularly as they become increasingly prevalent in sensitive domains such as elections and education. Their use in politics raises concerns about manipulation and misinformation. Further exploration is imperative to comprehend the social risks associated with GAI across diverse societal contexts. In this panel, we aim to dissect the impact and risks posed by GAI on our social fabric, examining both technological and societal perspectives. Additionally, we will present our latest investigations, including the manipulation of ideologies using large language models (LLMs), the potential risk of AI self‐consciousness, the application of Explainable AI (XAI) to identify patterns of misinformation and mitigate their dissemination, as well as the influence of GAI on the quality of public discourse. These insights will serve as catalysts for stimulating discussions among the audience on this crucial subject matter, and contribute to fostering a deeper understanding of the importance of responsible development and deployment of GAI technologies.

Streamlining Radiology Reporting: A Hands-Free Approach With Voice to Text and Generative Artificial Intelligence

ObjectiveIn radiology, report generation is critical for patient management, though can be time-consuming and labor-intensive. This study aims to investigate the feasibility of a hands-free radiology reporting system, leveraging voice-to-text technology and generative AI to improve time efficiency, report standardization, and reduce radiologist workload. Methods100 chest and musculoskeletal radiographs reports were generated using both traditional methods and an automated process using generative AI. To use generative AI, a radiologist dictated their findings into a voice-to-text software (Dragon), creating a raw text transcript. GPT-4 was then used to develop a report based on our institution’s template. Completion time for each traditional report and voice-to-text transcript was recorded. All reports were then evaluated for comprehensiveness, clarity, factual accuracy, and conciseness on a 5-point Likert scale. ResultsThe AI-driven system demonstrated considerable proficiency in transcribing and generating radiology reports, albeit with occasional inaccuracies. While it achieved a high level of precision in standard scenarios, its performance varied in more complex cases. Notably, the system significantly reduced the time taken to generate reports by 38% (p < 0.0001), signifying a potential increase in overall radiological workflow efficiency. ConclusionThis study highlights the potential of voice-to-text and generative AI integration in radiology reporting. Increased time efficiency, even in standard scenarios, can allow radiologists to focus more on complex cases and patient care. However, further development and refinement are required to address comprehensiveness and occasional inaccuracies. Ongoing advancements in AI and speech recognition technologies suggest a positive outlook for future implementations in medical reporting.

Special issue on next‐gen AI and quantum technology

Special issue on next‐gen AI and quantum technology

Examining the effect of ChatGPT usage on students’ academic learning and achievement: A survey-based study in Ajman, UAE

This research examines the use of ChatGPT among university-level students in the United Arab Emirates (UAE) and its effects on their learning experiences. The precise focus remains on the effects of ChatGPT usage on Student Engagement, Critical Thinking Abilities, and Academic Achievement. Using the cross-sectional design, the Constructivism Learning Theory supports this research. Data gathered using 353 structured questionnaires is analyzed using Partial Least Square-Structural Equation Modelling (PLS-SEM). Results showed that ChatGPT usage positively affects student engagement in the learning process. The effect of ChatGPT usage on Critical Thinking Abilities also remained significant. Finally, the findings indicated the positive effect of ChatGPT usage on the Academic Achievement of Emirati students. These results imply a robust, positive, and constructive role of AI technology, particularly ChatGPT, in the education and learning journey of university students in the UAE. It is concluded that ChatGPT is a useful tool that helps students by providing resources and suggestions throughout their learning process. It increases engagement, effort, and ambition in academic tasks, enhancing academic achievement. ChatGPT supports educational progress and motivates students to obtain knowledge by improving their interest in learning. Finally, the study's implications and limitations are discussed. Also, recommendations for future studies are proposed.

The Transformative Impact of AI Technology on Physical Education

The Transformative Impact of AI Technology on Physical Education