Optimizing Financial Data Workflows Using AI-Enhanced Cloud Migration Strategies

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

The effective integration of blockchain technology and artificial intelligence (AI) has the potential to change healthcare management. Two cutting-edge developments in the healthcare industry are blockchain and artificial intelligence. Blockchain, an open network for information sharing and permission, is being used in e-Health to apply artificial intelligence models. Healthcare workers will be able to view patient medical records on the blockchain. Artificial intelligence (AI) makes use of a wide range of suggested algorithms, decision-making power, and vast amounts of data. Health care services can be made more decentralized, transparent, safe, and impenetrable with the use of blockchain technology. AI needs cryptographic records to be stored, and blockchain makes this possible. Applications of artificial intelligence in healthcare management include chatbots for diagnosis and treatment, predictive analytics, personalized medicine, and more. Blockchain technology has applications in supply chain management, clinical trial management, interoperability, and data security and integrity in healthcare management. A more effective, safe, and patient-centered healthcare ecosystem can be created by integrating blockchain technology and artificial intelligence (AI) into healthcare management. It can encourage the development of a wide range of applications in radiology, cancer treatment, cardiology, dermatology, and fundoscopy that may save patients' lives.

Background. In the context of the continuous shifts in the modern labour market and the escalating competitiveness among students of maritime specialties, there is an increasing demand for the training of highly qualified specialists in the maritime industry, who are able to interact effectively with international partners at a professional level. In this respect, the use of generative artificial intelligence (AI) to improve foreign language teaching methods for students of maritime subjects is especially relevant. This is fully justified under the conditions of globalisation and the digital transformation of the educational sphere. Purpose. The purpose is to determine the effectiveness of generative AI application in the process of building foreign language professional communicative competence of maritime students by modelling realistic scenarios of job interviews in shipping companies. Materials and methods. The present study adopted a comprehensive approach, integrating theoretical and empirical methods of analysis. The theoretical basis was established through a comprehensive review of normative and educational documents, with the aim of identifying the fundamental requirements for the educational process. This was complemented by a thorough examination of scientific and educational literature, as well as methodological literature, on the application of AI in education and linguodidactics, thus enabling the identification of current trends and the potential of technologies for teaching maritime students. The empirical element of the study involved modelling, describing and analysing professional interview scenarios using generative AI models, the observation of students' learning activities, and the conduction of interviews to obtain feedback on the perception and effectiveness of the new techniques. Results. The research yielded an innovative methodology for training students of maritime specialities at Sevastopol State University for professional interviews in international shipping companies. The developed three-stage training model, combining the creation of a database of questions, simulation of interviews using generative (AI) and practising skills through business games, demonstrated high efficiency in the formation of both linguistic and professional competences of students, in addition to soft skills deemed integral for a successful career in the maritime industry. The integration of generative AI within the training framework of maritime students has been found to expand the scope of opportunities for effective preparation for professional interviewing. The implementation of this methodology has been identified as a significant contributor to the modernisation of the educational process, rendering it more personalised, effective and aligned with the precise requirements of the contemporary labour market.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Digital revolution and the resultant emergence of Industry 4.0 has driven the incorporation of Artificial intelligence (AI) in Industrial education to enhance skills development in the industry. However, there is a lack of adequate empirical evidence on the integration of Artificial intelligence in industrial education. To fill this gap, this study reviewed previous studies on the adoption of AI in Industrial education and examined the frequency of occurrence of variables obtained in the studies reviewed. Relevant literature was screened and reviewed to find empirical evidence to support findings. A systematic review of 14 studies provided insights into the current applications, benefit and challenges of AI integration in industrial education. The study found that the most cited applications of AI is Adaptive and personalised learning systems, which customise workers/learners’ information based on their interaction with learning content. Other applications are augmented simulators for real-time feedback, virtual mentors, and intelligent tutoring systems which replicate real-life interaction with professionals among others. Majority of the studies found increased engagement and improved learning outcomes and skills development as benefit of AI integration in Industrial education. Other benefits are promotion of early identification of learning challenges and timely intervention and feedback, improvement in administrative efficiency and support, personalisation of learning. Notable challenges were skills and capacity gaps, lack of infrastructure and AI resources, curriculum issues and difficulty in integrating AI into current curriculum, ethical and privacy concerns among others. Based on the findings of the study, it was recommended that the skill gap should be filled with training in AI applications and use, investment in AI infrastructural development should be explored, industry collaboration and partnership in the area of needs should be considered, AI marketing and literacy should be adopted in industries, all AI intervention should be a continuing and lifelong process to ensure sustainability.

Endoscopy remains the gold standard for gastrointestinal (GI) diagnostics, enabling direct visualization and intervention within the GI tract. However, diagnostic accuracy and procedural outcomes vary significantly depending on the endoscopist's skill and experience, leading to potential missed lesions and inconsistent patient care. The integration of artificial intelligence (AI) into endoscopic practice offers a promising solution to address these limitations and enhance diagnostic precision. This review explores the current applications of AI in endoscopy, focusing on image analysis, lesion detection, classification, and workflow optimization, while evaluating the impact on clinical practice and identifying implementation challenges. A literature search was conducted using PubMed, Google Scholar, and IEEE Xplore databases for studies published between January 2010 and December 2024. Keywords included "Artificial Intelligence," "Endoscopy," "Gastrointestinal Diseases," "Image Analysis," and "Lesion Detection." Studies were selected based on their focus on AI applications in endoscopy with quantitative or qualitative data on performance and clinical impact. AI demonstrates exceptional capabilities in polyp detection, achieving detection rates that often surpass those of human practitioners, with systems such as GI Genius showing high sensitivity and specificity. Convolutional neural networks excel in real-time lesion identification and classification, differentiating between benign and malignant growths with remarkable precision. AI also optimizes endoscopic workflows through automated reporting and advanced training tools. Although AI integration shows promise for enhancing endoscopic diagnostic accuracy and procedural efficiency, successful implementation requires careful consideration of current limitations, including reliance on industry-sponsored studies, and addressing challenges in data quality, clinical workflow integration, and regulatory considerations. Future developments in advanced algorithms, personalized medicine, and telemedicine may further advance endoscopic practice and improve patient outcomes.

A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff.

Introduction: Maternal and child health provide critical indicators of the health status of a country. Despite gains made over the decades, challenges continue to plague health care systems, especially in some developing countries. The integration of Artificial Intelligence (AI) into midwifery practice can potentially offer innovative opportunities which can revolutionize the way care is delivered. This review aims to identify evidence of the use of AI to enhance midwifery practice thereby reducing maternal morbidity and mortality. Objective: To determine the applicability of AI in midwifery practice to reduce maternal morbidity and mortality. Methods: Ninety-one articles were obtained using key words artificial intelligence, artificial intelligence in midwifery practice, maternal morbidity and mortality, midwifery practice. Sources of data include Google Scholar and EBSCO host. A total of eight (23) articles met inclusion criteria using key terms. Results: After inclusion/exclusion criteria was applied, 23 articles were selected. Seven (30. 4% addressed AI in midwifery, 7 (30.4 %) health care, 4 (17.3 %) use of AI in medicine, 2 (9%) AI, 1 (4.3) deep learning, I (4.3%) International Federation of Midwives and 1 (4.3%) World Health Organization Fact Sheet. Conclusion: The literature is sparse with evidence of the application of AI in midwifery practice to aid in the reduction of maternal morbidity and mortality as well as its application in low-resourced countries of the Caribbean region. Based on the use of artificial intelligence by other professions such as medicine to identify and prevent maternal morbidity, its use in by midwives is likely to enhance midwifery practice.

Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice

The Integration of Artificial Intelligence (AI) in Time Management represents a growing trend in the contemporary entrepreneurial landscape. This article explores the potential of AI to enhance time management practices for entrepreneurs in the context of rapid digital advancement. Through a synthesis of literature and empirical findings, it examines AI applications in activity planning, task prioritization, workflow optimization, and real-time data analysis. Additionally, the article evaluates the opportunities AI presents for increasing productivity, efficiency, and business competitiveness, while also addressing challenges such as data security, ethical considerations, and resistance to change. By offering a balanced perspective, this article aims to illuminate the implications of AI integration in time management for entrepreneurship and outline future research directions

This study explores the integration of Artificial Intelligence (AI) into the Reverse Logistics Process (RLP) to enhance decision-making, operational efficiency, and resource recovery. Through a systematic literature review, the applications of AI across critical RLP stages (planning, execution and control) are identified. Key findings highlight the transformative role of AI in optimizing network design, improving product collection and inspection, and supporting decision-making for the disposition of recovered items. AI technologies and functionalities such as machine learning, predictive analytics, and decision-support systems demonstrate significant potential for automating complex processes, reducing operational costs, and improving logistical precision. However, barriers to AI adoption in the RLP include high implementation costs, organisational resistance, lack of specialist personnel, and limited technological infrastructure. The review also identifies enablers such as advancements in the Internet of Things, blockchain, and big data analytics, which facilitate AI adoption and integration in RLP. The study concludes that AI is essential for establishing resilient and adaptive RL systems and offers substantial opportunities to address uncertainty while improving efficiency and decision-making. Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. This review fills a significant gap in the literature by offering a structured analytical framework that categorises AI applications based on decision types and RLP stages, providing an integrated perspective not previously addressed in the field.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Total Quality Management (TQM) represents a well-recognized management philosophy that underscores the importance of continuous improvement, customer satisfaction, and the pursuit of organizational excellence across diverse industries (Garcia & Patel, 2020). In recent years, there has been a notable trend among higher education institutions to adopt Total Quality Management principles with the aim of improving academic quality, administrative efficiency, and stakeholder engagement (Lopez & Kumar, 2021). Concurrently, swift progress in Artificial Intelligence (AI) has commenced the transformation of organizational processes, encompassing quality management systems. Artificial intelligence technologies empower organizations to conduct analyses of extensive datasets, automate repetitive tasks, and facilitate data-driven decision-making, thereby presenting considerable opportunities for enhancing Total Quality Management practices (Smith & Lee, 2021; Zhang et al., 2022). In the world of higher education, the applications of artificial intelligence are varied, encompassing the prediction of student performance, the personalization of learning experiences, the optimization of administrative workflows, and the monitoring of quality assurance (Nguyen et al., 2023). Nevertheless, in conjunction with these opportunities, there exist challenges, including ethical considerations, substantial implementation costs, technological complexity, and resistance from personnel who are unprepared for change (Kumar & Reddy, 2023; Patel & Garcia, 2022).