Artificial Intelligence Capabilities Research Articles

Artificial intelligence for enhancing resilience

In an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial intelligence (AI) in enhancing resilience across multiple domains. We explore how AI technology can be utilized to develop resilient infrastructure, providing advanced predictive maintenance and real-time monitoring capabilities that ensure robustness and longevity. The study examines the role of AI in improving disaster response, offering rapid data analysis and decision-making support to enhance emergency management outcomes. In climate change, AI-driven strategies are assessed for their effectiveness in fostering climate resilience, including predictive modeling of extreme weather events and optimizing resource allocation. The paper also discusses AI applications in healthcare resilience, such as enhancing diagnostics, patient care, and operational efficiency during crises. Business continuity and crisis management are examined, highlighting AI's capability to anticipate disruptions and maintain operational stability. The paper emphasizes the importance of strengthening cybersecurity resilience using AI to detect and mitigate threats proactively. AI's role in enhancing community and social resilience is analysed, particularly in supporting vulnerable populations and fostering social cohesion. Additionally, we explored AI-powered solutions for urban resilience, focusing on smart cities and sustainable development. The study also covers AI's contributions to environmental and ecological resilience, resilient supply chain management, and resilience in the hospitality and tourism industry. Finally, we investigated AI's potential in fostering psychological resilience, providing personalized mental health support and stress management tools. Through these diverse applications, the paper underscores AI's critical role in building a resilient future.

THE ROLE OF AI AND WEB 2.0 TOOLS IN DEVELOPING PRAGMATIC COMPETENCE OF L2 ENGLISH LEARNERS

Artificial Intelligence (AI) and web 2.0 tools has transformed various sectors, like healthcare and finance, by imitating human intelligence and decision-making. However, AI still struggles with understanding and effectively participating in human communication, which is a crucial challenge. Pragmalinguistics, a branch of linguistics focused on the relationship between language and context, addresses this issue. It examines pragmatic aspects such as speaker intention, context, and shared knowledge to grasp the intended meaning behind communication. By integrating pragmalinguistics into AI systems, researchers and developers aim to narrow the gap between machines and human communication. This article explores how AI and web 2.0tools are helpful in pragmalinguistics collaborate to improve AI capabilities.

Place identity: a generative AI’s perspective

Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies.

Understanding Student Perceptions of Artificial Intelligence as a Teammate

AbstractThis article examines students' opinions regarding the use of artificial intelligence (AI) as a teammate in solving complex problems. The overarching goal of the study is to explore the effectiveness of AI as a collaborative partner in educational settings. In the study, 15 groups of grade 9 students (59 students total) were assigned a challenging problem related to space exploration and were given access to an AI teammate. Following the task, the students participated in focus group discussions to gain insight into their perspectives on collaborating with AI. These discussions were analysed using thematic analysis to identify key themes. Epistemic Network Analysis was then used to quantify and visualise this data. The results suggest that students perceive AI with regard to two main themes: Trust in AI and the Capability of AI. The study's outcomes shed light on how students perceive AI and provide practical recommendations for educators to effectively incorporate AI into classrooms. Specifically, the recommendations include strategies for building student trust in AI systems through Explainable AI processes. This, in turn, encourages collaboration between humans and AI and promotes the development of AI literacy among students. The findings of this study are a valuable addition to the ongoing discussion on AI in education and offer actionable insights for educators to navigate the integration of AI technologies in support of student learning and growth. The scientific contribution of this study lies in its empirical investigation of student-AI interaction, providing evidence-based insights for enhancing educational practices.

Remixing Special Education Practices with Artificial Intelligence: UDL, EBP, and HLPs

This article presents engaging and practical methods of helping educators to “remix” the evidence-based and high-leverage practices they are already familiar with to include the new capabilities of artificial intelligence (AI). Transformational modern technologies can be powerful and disruptive, possessing the potential to impact multiple areas of society, including education. One of the best ways for educators to implement AI in their teaching is using it to help support and extend their current practices (Mishra et al., 2023). Similar to how a new remix on the radio can make an old favorite song fresh again, educators can use AI to uphold and enhance their existing instructional strategies and skills. However, adapting to the new paradigm of AI in education may be challenging for teacher preparation programs in special education. The authors of this article apply some of the strategies from “Leveraging Emerging Technology to Design an Inclusive Future with Universal Design for Learning” (McMahon & Walker, 2019) to effectively implement AI in education. Rather than needing to start fresh or relearn how to teach while incorporating AI, teachers can view this article as a foundation for how to apply AI tools to support current practice. The recommendations are based on Universal Design for Learning (UDL) and strategies for adapting AI tools to support high-leverage practices and established evidence-based practices. The authors aim to inspire special educators to start using AI to help “remix” and innovate the implementation of their existing instructional strategies.

Managing workplace AI risks and the future of work.

Artificial intelligence (AI)-the field of computer science that designs machines to perform tasks that typically require human intelligence-has seen rapid advances in the development of foundation systems such as large language models. In the workplace, the adoption of AI technologies can result in a broad range of hazards and risks to workers, as illustrated by the recent growth in industrial robotics and algorithmic management. Sources of risk from deployment of AI technologies across society and in the workplace have led to numerous government and private sector guidelines that propose principles governing the design and use of trustworthy and ethical AI. As AI capabilities become integrated in devices, machines, and systems across industry sectors, employers, workers, and occupational safety and health practitioners will be challenged to manage AI risks to worker health, safety, and well-being. Five risk management options are presented as ways to assure that only trustworthy and ethical AI enables workplace devices, machinery, and processes. AI technologies will play a significant role in the future of work. The occupational safety and health practice and research communities need to ensure that the promise of these new AI technologies results in benefit, not harm, to workers.

Research of Artificial Intelligence’s Impact on Innovation and Entrepreneur

In a time of innovation, Artificial Intelligence (AI) is leading the way exploring frontiers, w in innovation and business. This engaging story delves into the impact of AI portraying it as a driver of opportunities while also recognizing the significant challenges that it brings. By examining how AI transforms customer interactions streamlines processes, enhances decision making this exploration delves deep into AIs capabilities and the ethical and technical dilemmas it raises. Through analysis this research paper aims to shed light on how to navigate AIs potential in the business world showcasing its transformative role across various industries existing. This article not only highlights AIs influence but also encourages readers to think about its future path paving the way for new discoveries, new opportunities. In doing it promotes a discussion, on using AIs power responsibly and in a secure way to ensure its benefits are maximized while addressing its challenges thoughtfully.

AI-based R&D for frozen and thawed meat: Research progress and future prospects.

Frozen and thawed meat plays an important role in stabilizing the meat supply chain and extending the shelf life of meat. However, traditional methods of research and development (R&D) struggle to meet rising demands for quality, nutritional value, innovation, safety, production efficiency, and sustainability. Frozen and thawed meat faces specific challenges, including quality degradation during thawing. Artificial intelligence (AI) has emerged as a promising solution to tackle these challenges in R&D of frozen and thawed meat. AI's capabilities in perception, judgment, and execution demonstrate significant potential in problem-solving and task execution. This review outlines the architecture of applying AI technology to the R&D of frozen and thawed meat, aiming to make AI better implement and deliver solutions. In comparison to traditional R&D methods, the current research progress and promising application prospects of AI in this field are comprehensively summarized, focusing on its role in addressing key challenges such as rapid optimization of thawing process. AI has already demonstrated success in areas such as product development, production optimization, risk management, and quality control for frozen and thawed meat. In the future, AI-based R&D for frozen and thawed meat will also play an important role in promoting personalization, intelligent production, and sustainable development. However, challenges remain, including the need for high-quality data, complex implementation, volatile processes, and environmental considerations. To realize the full potential of AI that can be integrated into R&D of frozen and thawed meat, further research is needed to develop more robust and reliable AI solutions, such as general AI, explainable AI, and green AI.

GPT-4o vs. Human Candidates: Performance Analysis in the Polish Final Dentistry Examination.

Background This study aims to evaluate the performance of OpenAI's GPT-4o in the Polish Final Dentistry Examination (LDEK) and compare it with human candidates' results. The LDEK is a standardized test essential for dental graduates in Poland to obtain their professional license. With artificial intelligence (AI) becoming increasingly integrated into medical and dental education, it is important to assess AI's capabilities in such high-stakes examinations. Materials and methods The study was conducted from August 1 to August 15, 2024, using the Spring 2023 LDEK exam. The exam comprised 200 multiple-choice questions, each with one correct answer among five options. Questions spanned various dental disciplines, including Conservative Dentistry with Endodontics, Pediatric Dentistry, Dental Surgery, Prosthetic Dentistry, Periodontology, Orthodontics, Emergency Medicine, Bioethics and Medical Law, Medical Certification, and Public Health. The exam organizers withdrew one question. GPT-4o was tested on these questions without access to the publicly available question bank. The AI model's responses were recorded, and each answer's confidence level was assessed. Correct answers were determined based on the official key provided by the Center for Medical Education (CEM) in Łódź, Poland. Statistical analyses, including Pearson's chi-square test and the Mann-Whitney U test, were performed to evaluate the accuracy and confidence of ChatGPT's answers across different dental fields. Results GPT-4o correctly answered 141 out of 199 valid questions (70.85%) and incorrectly answered 58 (29.15%). The AI performed better in fields like Conservative Dentistry with Endodontics (71.74%) and Prosthetic Dentistry (80%) but showed lower accuracy in Pediatric Dentistry (62.07%) and Orthodontics (52.63%). A statistically significant difference was observed between ChatGPT's performance on clinical case-based questions (36.36% accuracy) and other factual questions (72.87% accuracy), with a p-value of 0.025. Confidence levels also varied significantly between correct and incorrect answers, with a p-value of 0.0208. Conclusions GPT-4o's performance in the LDEK suggests it has potential as a supplementary educational tool in dentistry. However, the AI's limited clinical reasoning abilities, especially in complex scenarios, reveal a substantial gap between AI and human expertise. While ChatGPT demonstrates strong performance in factual recall, it cannot yet match the critical thinking and clinical judgment exhibited by human candidates.

Recent advances and applications of artificial intelligence in 3D bioprinting.

3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues and organs. Meanwhile, recent advances in 3D bioprinting enable researchers to build in vitro models with finely controlled and complex micro-architecture for drug screening and disease modeling. Recently, artificial intelligence (AI) has been applied to different stages of 3D bioprinting, including medical image reconstruction, bioink selection, and printing process, with both classical AI and machine learning approaches. The ability of AI to handle complex datasets, make complex computations, learn from past experiences, and optimize processes dynamically makes it an invaluable tool in advancing 3D bioprinting. The review highlights the current integration of AI in 3D bioprinting and discusses future approaches to harness the synergistic capabilities of 3D bioprinting and AI for developing personalized tissues and organs.

Advancements in artificial intelligence for omnichannel marketing and customer service: Enhancing predictive analytics, automation, and operational efficiency

Gone are the days of impersonal customer service and generic marketing. Artificial intelligence (AI) is changing how businesses interact with customers, making interactions personalized and smooth across every platform. This transformation is powered by AI's capability to predict what customers want, automate routine tasks, and analyze large volumes of data. The Predictive Power of AI examines customer data to predict their preferences. Imagine receiving targeted ads and product recommendations that seem tailored exactly to your preferences! This personalized marketing approach enhances engagement and boosts sales significantly. AI-powered 24/7 chatbots provide instant customer support, eliminating the need for waiting on hold. They handle common inquiries and offer real-time assistance, continuously improving their ability to understand natural language and provide increasingly accurate help with each interaction. AI provides efficiency boost by automating repetitive tasks, allowing human teams to dedicate more time to cultivating relationships with customers, this includes qualifying leads, building targeted email campaigns, and analyzing data to inform business decisions, the outcome? More efficient operations and a happier team. It is imperative for businesses to understand customer sentiments and integration of AI analyzes customer interactions across various channels to assess their emotions and attitudes. This process, known as "sentiment analysis," enables businesses to detect dissatisfaction early, manage online brand perception, and gather valuable insights to enhance products and refine marketing strategies. There are also concerns about Ethical considerations, as AI becomes more integrated, businesses must prioritize customer privacy, eliminate bias in algorithms, and maintain a human touch to build genuine relationships. The future of AI and its possibilities are limitless! Natural Language Processing (NLP) advancements will lead to chatbots that sound and act human, while the integration of AI with virtual reality (VR) and augmented reality (AR) could create immersive and personalized experiences. In summary, AI is not here to replace humans; it is here to collaborate as your customer experience partner.

The role of artificial intelligence in revolutionizing personalized medicine: A comprehensive review of techniques and applications

This comprehensive review explores the transformative role of Artificial Intelligence (AI) in revolutionizing personalized medicine, focusing on techniques, applications, and future directions. Personalized medicine, which tailors medical interventions to individual characteristics, has significantly advanced through the integration of AI technologies. AI's capabilities in data integration, genomic and molecular data analysis, predictive modeling, and personalized treatment development are highlighted. The review also addresses the challenges and limitations of AI in personalized medicine, such as data privacy, bias, and the need for seamless clinical integration. Furthermore, it discusses future trends, including advances in AI technologies, the integration of multi-omics data, and the importance of ethical and regulatory considerations. The historical evolution of personalized medicine is traced, emphasizing key milestones that have led to current innovations. This review underscores AI's pivotal role in enhancing the precision and effectiveness of personalized healthcare, offering insights into how AI-driven approaches can shape the future of medicine.

Unleashing offensive artificial intelligence: Automated attack technique code generation

Artificial Intelligence (AI) technology is revolutionizing the digital world and becoming the cornerstone of the modern digital systems. The capabilities of cybercriminals are expanding as they adopt new technologies like zero-day exploits or new business models such as hacker-as-a-service. While AI capabilities can improve cybersecurity measures, this same technology can also be utilized as an offensive cyber weapon to create sophisticated and intricate cyber-attacks. This paper describes an AI-powered mechanism for the automatic generation of attack techniques, ranging from initial attack vectors to impact-related actions. It presents a comprehensive analysis of simulated attacks by highlighting the attack tactics and techniques that are more likely to be generated using AI technology, specifically Large Language Model (LLM) technology. The work empirically demonstrates that LLM technology can be easily used by cybercriminals for attack execution. Moreover, the solution can complement Breach and Attack Simulation (BAS) platforms and frameworks that automate the security assessment in a controlled manner. BAS could be enhanced with AI-powered attack simulation by bringing forth new ways to automatically program multiple attack techniques, even multiple versions of the same attack technique. Therefore, AI-enhanced attack simulation can assist in ensuring digital systems are bulletproof and protected against a great variety of attack vectors and actions.

Navigating the New Frontier: Recommendations to Address the Crisis and Potential of AI in the Classroom

Introduction The integration of artificial intelligence (AI) into higher education ushers in a new era of teaching and learning possibilities for both educators and students. Statement of the Problem These technologies not only offer unparalleled opportunities for personalized learning and instructional support, but also introduce challenges related to academic integrity. Literature Review The literature highlights AI's versatile applications in education, from facilitating research to acting as a tutor. Teaching Implications Educators can create AI-informed assignments that encourage active learning, critical thinking, and creativity. AI tools can be integrated as supports rather than substitutes for learning and foster an ethical framework within which students can explore AI's capabilities. Conclusion This review of AI's impact on education and the discussion of teaching strategies underscore the need for a nuanced approach to integrating AI in the classroom. By acknowledging both the challenges and opportunities presented by AI, educators can enhance teaching and learning while upholding academic standards.

The Role of Artificial Intelligence in Supporting Radiologists in Detecting Lung Lesions Caused by COVID-19 - A Scoping Review

The COVID-19 pandemic has posed significant challenges to healthcare systems, particularly in the realm of medical imaging and the diagnosis of COVID-19 pneumonia lung lesions. Artificial intelligence (AI) has become essential in assisting radiologists by swiftly analyzing extensive volumes of computed tomography (CT) scan data to identify lung abnormalities. Radiologists, who typically conduct thorough examinations of CT scans, benefit from AI's capability to pre-screen images, flag potential issues, and prioritize urgent cases, thereby enhancing efficiency during times of high demand. AI, especially through deep learning, can recognize subtle patterns in lung images that may be overlooked by human eyes, offering valuable second opinions and improving diagnostic accuracy and consistency. The early detection of COVID-19 lung lesions by AI facilitates prompt treatment, helping to prevent disease progression and improve patient outcomes. This review article aims to consolidate current knowledge on the topic by examining existing literature and discussing the advantages and disadvantages of employing CT imaging and AI tools for diagnosing COVID-19.

Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine.

Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role. The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated. The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM. The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside. The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).

Analysis and Prediction of Bird Migration Using RFA and GBR

The project "Analysis and Prediction of Bird Migration Using RFA and GBR" utilizes an extensive eBird dataset with approximately 100,000 observations to analyze and forecast bird migration patterns. The primary goals involved data collection, preprocessing, and feature extraction, followed by the progression of predictive models Gradient Boosting in use and Random Forest algorithms. Data preprocessing encompassed cleaning, normalization, and encoding to ensure the dataset’s quality and relevance. Through rigorous training and validation, our models demonstrated high accuracy and precision. The resulting analysis uncovered notable migration patterns and seasonal trends that correspond to known bird behaviors. These findings highlight the capabilities of artificial intelligence in deepening our knowledge of bird migration and contributing to effective conservation efforts. It also highlights the value of incorporating additional environmental data to further refine predictive models, offering a robust framework for analyzing and predicting migration patterns and other ecological phenomena. Furthermore, the project demonstrated the critical role of machine learning in environmental science, demonstrating how sophisticated algorithms can uncover complex ecological connections previously challenging to identify. The findings from This project could aid policymakers and conservationists in developing targeted conservation efforts, ensuring better protection of migratory bird species amidst changing climatic conditions. Future work could expand on this foundation by integrating real-time data streams and leveraging more sophisticated ensemble methods to enhance the migration forecasts.

Conquering Digital Marketing: Powerful Strategies for Accelerating Business Growth

Understanding digital marketing tactics is now crucial for company expansion in the digital age due to the quickly changing technology landscape. This study investigates the efficacy of modern digital marketing strategies with an emphasis on how they support significant company growth. This study is significant because it offers practical advice on how to use data analytics, customization, and cutting-edge technology like machine learning and artificial intelligence to maximize digital marketing initiatives. The main goal of this study is to determine and evaluate the best digital marketing tactics that spur company expansion. Using a thorough literature review methodology, the study assesses the effects of different digital marketing methods on business performance by looking through contemporary academic sources. Important results show that cross-channel and multi-channel marketing techniques greatly improve brand coherence and consumer engagement. Enhancing targeting accuracy and campaign efficacy requires the use of data analytics and artificial intelligence capabilities. While ethical principles and digital transformation are essential for maintaining consumer trust and competitive advantage, personalization and customer feedback integration play a critical role in driving engagement and conversion rates. Furthermore, social media, influencer marketing, and strong content tactics work well to increase brand awareness and adherence. Strategic agility is ensured by remaining up to date with emerging trends, and marketing outcomes are further enhanced through optimization of conversion rates and user experience design. These findings' implications imply that companies should take a multifaceted strategy to digital marketing, including cutting-edge technologies and emphasizing ethical and personalized practices. It is advised to use multi-channel marketing techniques, make use of AI and data analytics, prioritize ethical marketing, and continuously improve user experience and content. Future studies ought to investigate how new digital trends—like gamification and behavioral targeting—affect the efficiency of marketing campaigns and the expansion of companies. In addition to offering a foundation for companies hoping to succeed in the cutthroat digital economy, this research advances our understanding of successful digital marketing tactics.

Sustainability risk management: Exploring the role of artificial intelligence capabilities through an information-processing lens.

The global sustainability movement is reshaping the operational requirements and managerial approaches of maritime firms, resulting in the emergence of unprecedented and complex risks in the sector. This has driven maritime firms to leverage digital tools, such as artificial intelligence (AI) capabilities, to enhance their sustainability risk management (SRM) endeavors. Drawing on the organizational information-processing theory (OIPT), this study proposes four AI capabilities: customer value proposition, key process optimization, key resource optimization, and societal good. It examines their influence on sustainability-related knowledge management capabilities (SKMC), stakeholder engagement, and SRM. A survey questionnaire was used to gather responses from 157 maritime professionals across various sectors of the industry, providing empirical data for analysis. Employing structural equation modeling, the findings reveal that AI capabilities can improve SKMC. These findings enhance existing literature by using OIPT concepts to investigate the interplay among the constructs that lead to better SRM in maritime firms. Furthermore, the study offers managerial guidance by providing insights into AI capabilities that maritime firms should incorporate into their operations, fostering best practices to effectively manage sustainability risks and ensure the firm's long-term survival.

Risks and Legal Governance of Generative Artificial Intelligence

Generative Artificial Intelligence (AI) represents a significant advancement in the field of artificial intelligence, characterized by its ability to autonomously generate original content by learning from existing data. Unlike traditional decision-based AI, which primarily aids in decision-making by analyzing data, generative AI can create new texts, images, music, and more, showcasing its immense potential across various domains. However, this technology also presents substantial risks, including data security threats, privacy violations, algorithmic biases, and the dissemination of false information. Addressing these challenges requires a multi-faceted approach involving technical measures, ethical considerations, and robust legal frameworks. This paper explores the evolution and capabilities of generative AI, outlines the associated risks, and discusses the regulatory and legal mechanisms needed to mitigate these risks. By emphasizing transparency, accountability, and ethical responsibility, we aim to ensure that generative AI contributes positively to society while safeguarding against its potential harms.