Role Of Artificial Intelligence Research Articles

The role of Artificial Intelligence in enhancing cybersecurity: A comprehensive review of threat detection, response, and prevention techniques

As cyber threats continue to grow in scale and sophistication, traditional cybersecurity solutions have become increasingly insufficient to mitigate evolving risks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by improving threat detection, automating response mechanisms, and preventing attacks before they occur. This review explores the intersection of AI and cybersecurity, focusing on AI-driven techniques in threat detection, automated response systems, and preventive measures. Furthermore, the paper discusses the challenges of deploying AI in cybersecurity, including adversarial attacks and ethical considerations, and provides future directions for research.

AI for Optimizing Supply Chain Management

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed various sectors, with supply chain management (SCM) being a notable beneficiary. This research paper explores the impact of AI on optimizing supply chain processes, focusing on its applications in predictive analytics, natural language processing, robotics and automation, blockchain, and the Internet of Things (IoT). Through a mixed-method approach involving quantitative surveys, qualitative interviews, and secondary data analysis, the study evaluates how AI-driven solutions enhance key performance indicators (KPIs) such as inventory turnover, lead time, order accuracy, and cost reduction. The results indicate substantial improvements in these KPIs post-AI implementation, highlighting AI's role in achieving greater operational efficiency, accuracy, and cost-effectiveness in SCM. Challenges associated with AI adoption, including data quality, integration with legacy systems, and skill gaps, are also discussed. The paper provides actionable insights and recommendations for organizations aiming to leverage AI technologies to optimize their supply chain operations. This research contributes to the growing body of knowledge on AI in SCM and offers practical guidance for industry practitioners

ЄВРОПЕЇЗАЦІЯ ПРОФЕСІЙНОЇ ПІДГОТОВКИ МАЙБУТНЬОГО ФАХІВЦЯ ГОТЕЛЬНО-РЕСТОРАННОЇ СПРАВИ: МОЖЛИВОСТІ ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ

The purpose of the study is possibilities and prospects of using artificial intelligence for Europeanization of professional training of future specialists in hotel and restaurant business. To achieve this goal, we set several tasks: to study European standards of professional training of specialists; to evaluate current trends in application of artificial intelligence in education; to identify opportunities and challenges of AI integration into educational process of professional training of future specialists in hotel and restaurant business. During the scientific search, a complex of methods was applied: bibliographic, analysis and synthesis for the study of psychological and pedagogical literature (theoretical). A monographic method was used for logical presentation of the results. Results. Europeanization of professional training of future specialists in hotel and restaurant business of Ukraine as an important stage of adaptation of national education system to international standards has been studied. Main standards are considered through partial characterization of a number of documents. Challenges of Europeanization are characterized, namely: standards and accreditation, updating of curricula and programs, human resources, financing. Possibilities of Europeanization of professional training of future specialists in hotel and restaurant business, including improving quality of education, possibility of international internships and exchanges, increasing prestige of the profession of future specialists in the field of hotel and restaurant business and likely attraction of investments, have been allocated. Role of artificial intelligence in professional education has been investigated. Importance of AI has been proved through the implementation of a number of innovative solutions that can significantly improve educational process, namely: adaptive learning systems, virtual simulators, learning management systems. Conclusions. Main results of the study show that use of artificial intelligence has great potential for Europeanization of professional training in the field of hotel and restaurant business. Prospects for further research may include studying the impact of AI on specific aspects of educational process, developing new technologies for educational purposes, and analyzing long-term effects of AI integration on the labor market. Key words: professional training, future specialists, hotel and restaurant business, higher education institution, artificial intelligence, Europeanization.

Investigating Adoption Determinants, Obstacles, and Interventions for AI Implementation in Emirati Media Organizations

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

The Future of Artificial Intelligence: Trends and Predictions

Artificial Intelligence (AI) has evolved rapidly, transforming diverse industries and societal functions. This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges. Key trends are explored, including innovations in machine learning and deep learning, AI’s expanding role across industries, and its potential for addressing climate change and sustainability. Furthermore, the paper highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities. Predictions for AI’s future are discussed, such as the emergence of artificial general intelligence (AGI), advancements in autonomous systems, the impact of quantum computing on AI, and innovations in AI-specific hardware. The paper also examines ethical and societal challenges, such as privacy, algorithmic bias, and the need for global governance, addressing the urgent call for responsible AI. In light of these trends, the paper emphasizes future research directions, encouraging interdisciplinary collaboration and a focus on explainable, robust, and resilient AI models. This work aims to shed light on the transformative potential of AI while advocating for ethical practices to ensure a positive and sustainable impact on society.

The Role of Artificial Intelligence in Scientific Writing

Artificial intelligence (AI) has evolved rapidly in the last century. Once a futuristic concept with negative connotations, AI has now begun to permeate various fields, reaching academic and scientific writing. In scientific writing, AI applications promise to significantly improve writing accuracy, provide quality control, and thereby enhance manuscript evaluation, among other possible contributions. It is generally agreed that AI and machine learning tools may become writing assistants if involved in writing tasks. Keywords: Artificial Intelligence, Scientific Writing

Artificial intelligence-enhanced biosurveillance for antimicrobial resistance in sub-Saharan Africa.

Antimicrobial resistance (AMR) remains a critical global health threat, with significant impacts on individuals and healthcare systems, particularly in low-income countries. By 2019, AMR was responsible for >4.9 million fatalities globally, and projections suggest this could rise to 10 million annually by 2050 without effective interventions. Sub-Saharan Africa (SSA) faces considerable challenges in managing AMR due to insufficient surveillance systems, resulting in fragmented data. Technological advancements, notably artificial intelligence (AI), offer promising avenues to enhance AMR biosurveillance. AI can improve the detection, tracking and prediction of resistant strains through advanced machine learning and deep learning algorithms, which analyze large datasets to identify resistance patterns and develop predictive models. AI's role in genomic analysis can pinpoint genetic markers and AMR determinants, aiding in precise treatment strategies. Despite the potential, SSA's implementation of AI in AMR surveillance is hindered by data scarcity, infrastructural limitations and ethical concerns. This review explores what is known about the integration and applicability of AI-enhanced biosurveillance methodologies in SSA, emphasizing the need for comprehensive data collection, interdisciplinary collaboration and the establishment of ethical frameworks. By leveraging AI, SSA can significantly enhance its AMR surveillance capabilities, ultimately improving public health outcomes.

Revolutionizing Sustainable Energy: Cutting-Edge AI Applications and Innovations

The intersection of Artificial Intelligence (AI) and sustainable energy is paving the way for groundbreaking innovations that promise to revolutionize how we produce, consume, and manage energy. This paper delves into cutting-edge applications of AI, including advanced predictive analytics, blockchain integration, and autonomous energy systems, highlighting their potential to transform the sustainable energy landscape. By showcasing real-world applications and future directions, we underscore AI's role in driving sustainable practices and addressing global energy challenges.

Smart Medicine: The Role of Artificial Intelligence and Machine Learning in Next-Generation Healthcare Innovation

The paper focuses on big data and healthcare pharmaceuticals, which pose both great promise and challenge as they can contribute to well-being and deliver innovation and knowledge to next-generation healthcare products. This paper provides an overview of the benefits that machine learning and big data technology contribute to healthcare systems. Realities such as patient and healthcare professional demands, the number of diseases, insufficient drugs, regulations, legal and ethical guideline restrictions, modus operandi, and technology development take place in the healthcare sector. In addition, big data opportunities and challenges for healthcare companies, research, and healthcare infrastructure are discussed. Finally, a vision of health innovation through personalized medicines as well as smart medicine is presented as a step closer to patients through regulatory, legal, and ethical guidelines, applications, and opportunities that need to be regulated and resolved.

Advancements in Multiple Myeloma Research: High-Throughput Sequencing Technologies, Omics, and the Role of Artificial Intelligence

Multiple myeloma is a complex and challenging type of blood cancer that affects plasma cells in the bone marrow. In recent years, the development of advanced research techniques, such as omics approaches—which involve studying large sets of biological data like genes and proteins—and high-throughput sequencing technologies, has allowed researchers to analyze vast amounts of genetic information rapidly and gain new insights into the disease. Additionally, the advent of artificial intelligence tools has accelerated data analysis, enabling more accurate predictions and improved treatment strategies. This review aims to highlight recent research advances in multiple myeloma made possible by these novel techniques and to provide guidance for researchers seeking effective approaches in this field.

Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward

Abstract Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Toward this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumor visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumors that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep-learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions and prognostic evaluations for TNBC.

The Role of Artificial Intelligence in Higher Education

The integration of Artificial Intelligence (AI) in higher education has sparked a transformative shift in pedagogical methodologies, student engagement, and academic integrity across the UK and Ireland (Dogru, et.al, 2023). This qualitative study delves into the multifaceted implications of utilising AI in academic assignments from the perspective of higher education students, drawing on semi-structured interviews carried out with higher education students. The findings reveal a variety of perceived benefits, including enhanced study efficiency, personalised learning support (Hanaba et al., 2020; Yang, 2021), and the potential of AI to level the academic playing field for students with diverse needs. However, these advantages are in contrast with students’ perceptions of a wide range of significant concerns and challenges, notably the ethical dilemmas surrounding academic honesty, the potential for student dependency on AI leading to diminished knowledge and skill development, as well as the issue of equity in relation to students’ access to AI resources. Moreover, the study stresses a critical gap in institutional guidance regarding AI use, with students voicing a need for clear, consistent guidance from universities. This paper highlights the complex landscape of AI in academia, advocating for a balanced approach that harnesses AI's potential while addressing ethical, educational, and equity challenges. The study emphasises a need for immediate and more in-depth research into the use of AI in higher education from the perspective of both students and university staff in Ireland/UK.

Pharma in The Digital Era: The Role of Artificial Intelligence in Drug Development

The integration of Artificial Intelligence (AI) in the pharmaceutical sector marks a transformative era, where technology reshapes traditional drug development processes. This paper delves into the pivotal role AI plays in enhancing efficiency and precision in drug discovery, testing, and approval. Existing methods in drug development often suffer from high costs, extended timelines, and high attrition rates in clinical trials, posing significant barriers to timely patient care. To address these issues, we propose the Drug Development based on Artificial Intelligence (DD-AI) framework, leveraging AI-driven algorithms and machine learning models. The DD-AI framework enhances the identification and optimization of drug candidates, predicts clinical trial outcomes, and personalizes patient treatment plans. AI algorithms analyze vast datasets to discover potential drug molecules, simulate their interactions, and streamline the clinical trial process by identifying the most promising candidates. Implementing the DD-AI framework has demonstrated substantial improvements in reducing drug development costs and timelines while increasing the accuracy of trial outcomes. AI's predictive capabilities also personalize treatments, optimizing efficacy and minimizing adverse effects for patients. The DD-AI framework offers a robust solution to existing challenges in drug development, fostering a more efficient, cost-effective, and patient-centered approach. The findings underscore AI's transformative potential in revolutionizing pharmaceutical practices, ultimately benefiting patients with faster and more precise medical solutions.

The Role of Artificial Intelligence in Shaping News Narratives: A Review of Global Case Studies

The use of artificial intelligence (AI) in journalism is changing how news is created, disseminated, and consumed in the quickly changing media landscape. Reviewing international case studies, this study examines how artificial intelligence (AI) influences news narratives. The study looks at how AI-powered algorithms affect the way news stories are framed, chosen, and distributed. Through examining case studies from various regions such as Europe, Asia, Africa, and North America, the research delves into how AI impacts journalism practices and the associated ethical considerations. Key findings show that although AI improves personalization and efficiency, it also raises questions about audience perceptions, media diversity, and bias. The results of this study also emphasize the necessity of ethical concerns and legal frameworks by highlighting the opportunities and difficulties that AI presents to the media landscape. The study ends with suggestions for additional research and possible approaches to reducing the ethical difficulties.

Integrating Artificial Intelligence Across Medical Clinics: Strengthening Collaborative Efforts for Improved Patient Outcomes

The integration of Artificial Intelligence (AI) across medical clinics holds transformative potential for enhancing patient outcomes through improved collaboration. This review examines the applications of AI in fostering inter-clinic connectivity, with a focus on diagnostics, treatment planning, and patient management. By utilizing AI-powered data-sharing platforms, predictive analytics, and telemedicine solutions, clinics can collaborate more efficiently, ensuring continuity of care and reducing diagnostic errors. Despite these advancements, challenges persist, including data privacy, interoperability, and resistance to AI adoption among clinical staff. This review highlights case studies showcasing successful AI-enabled collaborations and proposes future directions to address current limitations. Overall, AI's role in linking medical clinics underscores its potential to revolutionize patient care by bridging gaps in communication and decision-making processes.

Towards smart and sustainable transportation: the role of artificial intelligence and new technologies in mitigating passenger car CO2 emissions in European countries

Towards smart and sustainable transportation: the role of artificial intelligence and new technologies in mitigating passenger car CO2 emissions in European countries

Design of automatic sorting system based on capsule neural network

Abstract. This paper proposes a vegetable disease classification system based on Capsule Neural Network (CapsNet), which uses the visual recognition mechanism of deep learning algorithms to improve the efficiency and accuracy of agricultural disease recognition. The CapsNet algorithm outperforms traditional Convolutional Neural Network (CNN) in few-shot learning and generalization due to its unique capsule structure and dynamic routing mechanism. Experimental results show that the proposed system performs well in the accuracy, robustness, and model interpretation of image recognition, and can accurately identify diseases even in the case of poor image quality or noise and occlusion. In addition, the embedded system of this experiment includes an image acquisition module, an algorithm core module, and a logic control module, which realizes the automation of the whole process from image acquisition to recognition result display. This study also discusses the scalability and adaptability of the system, as well as the future application prospects in agricultural intelligence and automation. It is expected that CapsNet will play a more critical role in artificial intelligence, especially in agrarian automation and smart agriculture.

The Role of Artificial Intelligence in Revolutionizing Mental Health Services: A Data-Driven Approach

The potential of artificial intelligence (AI) to transform mental health care through individualized treatment approaches is significant. This research investigates the application of AI and extensive healthcare datasets to improve the precision of mental health diagnoses and treatments. The study employs various machine learning techniques, such as Random Forest, CatBoost, K-Nearest Neighbors, XGBoost, Convolutional Neural Networks, and Long Short-Term Memory networks, to analyze diverse data sources including electronic health records, neuroimaging, genetic information, and demographic data. Following thorough data preprocessing, model training, and evaluation using metrics like accuracy, precision, recall, F1 score, and Cohen's Kappa, the Random Forest algorithm emerges as the top performer with 98.7% accuracy. The research highlights the importance of addressing ethical considerations, such as protecting patient privacy, ensuring data security, and mitigating algorithmic bias when implementing AI in mental health services. The findings indicate that AI-based approaches can markedly improve diagnostic accuracy, provide tailored treatment suggestions, and facilitate early relapse detection, thus promoting proactive mental health management. Key Words: Artificial Intelligence, Mental Health, Data- Driven approach, Personalized Treatment, Machine Learning, Natural Language Processing.

Role of Artificial Intelligence in IoT

Artificial Intelligence (AI) and the Internet of Things (IoT) are two revolutionary technologies shaping modern industries. AI enables machines to learn from data and make autonomous decisions, while IoT connects devices, allowing them to communicate and exchange information. The convergence of these technologies, known as AIoT (Artificial Intelligence of Things), has immense potential to transform daily life and business operations. AI enhances IoT by adding intelligence to device networks, improving efficiency and automating processes. For example, AI-powered IoT systems can perform predictive maintenance in industrial settings, preventing costly machine failures. In smart buildings, AI can optimize energy usage, reducing both costs and environmental impact. However, integrating AI with IoT poses significant challenges. One major issue is the vast amount of data generated by IoT devices, which AI requires to function effectively. Managing this data efficiently is critical. Additionally, IoT devices are vulnerable to cyber-attacks, making security a key concern. Protecting these systems from potential threats is essential to safely realize the benefits of AIoT. Despite these challenges, the combination of AI and IoT promises to drive future advancements across industries, enabling smarter, more connected systems that optimize decision-making, reduce costs, and improve overall efficiency.

Assessing the need for coronary angiography in high-risk non-ST-elevation acute coronary syndrome patients using artificial intelligence and computed tomography.

This study aimed to evaluate the efficacy of the Chat Generative Pre-trained Transformer (ChatGPT) in guiding the need for invasive coronary angiography (ICA) in high-risk non-ST-elevation (NSTE) acute coronary syndrome (ACS) patients based on both standard clinical data and coronary computed tomography angiography (CCTA) findings. This investigation is a sub-study of a larger prospective multicentric double blinded project where high-risk NSTE-ACS patients underwent CCTA prior to ICA to compare coronary lesion by both modalities. ChatGPT analyzed clinical vignettes containing patient data, electrocardiograms, troponin levels, and CCTA results to determine the necessity of ICA. The AI's recommendations were then compared to actual ICA findings to assess its decision-making accuracy. In total, 86 patients (age: 62 ± 13 years old, female 27%) were included. ChatGPT recommended against ICA for 19 patients, 16 of whom indeed had no significant findings. For 67 patients, ChatGPT advised proceeding with ICA, and a significant lesion was confirmed in 58 of them. Consequently, ChatGPT's overall accuracy stood at 86%, with a sensitivity of 95% (95% confidence interval (CI) 0.76-0.92) and a specificity of 64% (95% CI 0.62-0.94). The model's negative predictive value was 84% (95% CI 0.44-0.79), and its positive predictive value was 87% 95% CI 0.86-0.97). Preliminary evidence suggests that ChatGPT can effectively assist in making ICA decisions for high-risk NSTE-ACS patients, potentially reducing unnecessary procedures. However, the study underscores the importance of data accuracy and calls for larger, more diverse investigations to refine artificial intelligence's role in clinical decision-making.