AI-powered data engineering: How machine learning is revolutionizing ETL and data pipelines

Soil piping, an insidious form of internal erosion within soil structures, presents substantial challenges in land stability and infrastructure integrity. Detecting and pre- emptively managing soil piping require robust and non- destructive methodologies to identify vulnerable areas. Ground- penetrating radar (GPR) has emerged as a valuable tool in this context, offering promising insights into subsurface conditions and potential erosion pathways. This paper presents an overview of recent advancements in the integration of artificial intelligence (AI) techniques with GPR data processing and interpretation. The recent speedy development of AI technologies (machine learning, deep learning, etc.) provides a great opportunity to develop reliable, accurate and time- effective processing solutions to advance most of the current and emerging Earth observation and remote sensing technologies. By combining the prowess of GPR's non-destructive subsurface imaging with the intelligence of AI-driven data interpretation, we can better understand the underlying complexities of different materials and develop more efficient, accurate, and reliable solutions for piping.

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.

Disclosure: H.M. Heshmati: None. N. Zameni: None.Background: Robotic surgery is quickly replacing traditional surgery performed by humans. It has significantly influenced the field of endocrine surgery. This review presents an update on the use of robotic surgery and artificial intelligence for adrenal tumors. Methods: A systematic search of literature was conducted using the search terms robotic surgery, artificial intelligence, adrenal tumors, safety, outcome, and cost. Results: Adrenal tumors are found in 10% of people who have imaging of the adrenal gland. They are typically unilateral and benign in the majority of cases. Robotic systems such as da Vinci Surgical System provide surgeons with enhanced dexterity and 3D visualization. Autonomous surgical robots can be categorized into various levels of autonomy including supervised autonomy (e.g., performance of specific tasks with human oversight), collaborative autonomy (e.g., robots working alongside human surgeons, sharing tasks and responsibilities), and fully autonomous systems (e.g., robots performing entire surgical procedures independently with human intervention when necessary). Robotic surgery allows access through tight spaces to remove adrenal tumors with small incision and minimal impact on surrounding tissues. The review of 1,162 patients who underwent adrenalectomy (robotic surgery in 747 patients, laparoscopic adrenalectomy in 415 patients) showed comparable blood loss and complications with the two techniques but patients with robotic surgery had significantly shorter hospital stay. The future of robotic surgery can be revolutionized by the integration of autonomous robots and artificial intelligence (AI), a field rapidly advancing. These innovations promise to further enhance the precision, safety, and effectiveness of surgery involving the adrenal glands. AI-powered systems provide real-time guidance and feedback to surgeons, enhancing decision-making and surgical precision. Autonomous systems can ensure consistent execution of surgical tasks, reducing variability and enhancing patient safety. AI algorithms can analyze extensive surgical data to refine techniques and predict potential complications, improving surgical outcomes. AI can customize surgical plans based on individual patient anatomy and pathology, potentially leading to better outcomes. The current high cost of these new procedures is a potential limitation for their use. Conclusion: Robotic surgery represents significant progress in minimally invasive techniques for the removal of adrenal tumors. It has improved dexterity and precision, and is associated with shorter recovery time compared to laparoscopic adrenalectomy. Collaboration between human surgeons and robotic systems can improve patient care and surgical outcomes. The precision, safety, and effectiveness of the adrenal surgery can be further enhanced by the integration of AI and robotics.Presentation: Saturday, July 12, 2025

The integration of data science and Artificial Intelligence (AI) into geospatial analysis has revolutionized Earth observation, driving progress towards the Sustainable Development Goals (SDGs). Recent developments in data acquisition technologies like high-resolution satellites and sensors have generated vast and diverse datasets for monitoring environmental changes and managing natural resources. Concurrently, innovations in Machine Learning (ML) and AI have significantly enhanced the processing, analysis and interpretation of this geospatial data. Techniques such as deep learning, spatial data mining and automated feature extraction are now essential to deriving actionable insights from complex geospatial datasets. This paper reviews the latest trends and breakthroughs in the application of AI/ML to geospatial data for Earth observation, emphasizing their role in advancing the SDGs. Key areas of focus include improved algorithms for land cover classification, disaster prediction and climate monitoring. These technologies enable more precise and timely responses to environmental challenges, such as deforestation, urbanization and natural disasters, thereby supporting sustainable management and policymaking. Furthermore, the integration of AI with geospatial data enhances predictive modelling, scenario planning and decision support systems, which are critical for achieving SDG targets related to environmental sustainability and resilience. The synthesis of recent research and technological developments highlights the potential of AI/ML approaches for geospatial analysis and their alignment with global sustainability goals. The outcomes underline the requirement for continued innovation and collaboration across disciplines to fully leverage these advancements for effective Earth observation and sustainable development.

The article examines the theoretical and practical aspects of implementing artificial intelligence (AI) technologies in staff recruitment and selection processes. It substantiates the necessity of digital transformation in HR management amidst dynamic labor market changes and increasing data volumes. An analysis is conducted on the key stages of recruitment—sourcing, pre-selection, competency assessment, decision-making, and employee onboarding—from the perspective of their optimization through AI. The main technologies utilized in modern recruitment are highlighted: machine learning, natural language processing (NLP), computer vision, and predictive analytics. Domestic and international practices of using intelligent systems are reviewed. Special attention is paid to issues of ethics, algorithmic transparency, and the legal regulation of AI application in HR processes. It is concluded that the integration of artificial intelligence into the recruitment system enhances selection accuracy, reduces costs, and facilitates the formation of a more flexible and adaptive HR policy for the organization.

Robotic Process Automation (RPA) has established its position as an innovative framework for simplifying repetitive and rule-based procedural tasks throughout the industrial sector. Traditional RPA systems face limitations because they cannot adjust or exhibit cognitive capabilities. The combination of Artificial Intelligence (AI) technology with RPA systems produced Intelligent Automation, which gives bots the ability to detect, think, learn, and make independent decisions. A review paper examines how RPA systems progressed into AI-powered RPA systems by examining major technological developments, obstacles, and modern patterns. The review paper dedicates specific attention to cognitive abilities in RPA through discussions about machine learning, natural language processing, computer vision, and reinforcement learning frameworks. The study analyzes the constraints of contemporary solutions and sets research priorities for creating self-healing bots with complete autonomy. The paper merges academic research with industrial approaches to deliver an extensive guidance system that benefits researchers and practitioners who aim to advance present-day intelligent automation systems.

The integration of Artificial Intelligence (AI) in the food industry is revolutionizing customer experience by enabling personalized services, real-time interactions, and operational efficiency. This chapter explores how AI-driven technologies such as machine learning, natural language processing, computer vision, and recommender systems are enhancing the customer journey across food delivery, restaurant services, and backend operations. The chapter explores applications like chatbots, voice assistants, visual recognition, and sentiment analysis, while addressing key challenges including data privacy, algorithmic bias, and regulatory compliance. The chapter also covers the topic of ethical issues and sustainability and such topics as generative AI, emotion recognition, and federated learning. Thus, this chapter aims to give a brief on how AI can be employed in the food industry to develop smart solutions that enhance customers' experiences in the changing industry environment.

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.

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.

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 postharvest agricultural practices has advanced considerably in recent decades, driven by substantial progress in scientific research and technological development. Tomato softening and quality degradation during postharvest storage present major challenges in minimizing food waste and maintaining market value. This review explores the integration of artificial intelligence (AI) and machine learning (ML) technologies with non-destructive methods such as computer vision, imaging, electrical signal analysis, to monitor and predict tomato ripening stages and shelf life. Recent studies demonstrate high prediction accuracies using advanced models, including artificial neural networks, ensemble learning, and fuzzy inference systems, which analyze features like firmness, color, lycopene content, and texture. These AI-driven approaches enable accurate classification of ripeness stages and optimization of storage conditions, offering significant advantages over traditional destructive techniques. For the future ML, integration of large and complex algorithms and AI-driven systems controlling smart ripening chambers by adjusting ethylene concentration, humidity, and temperature based on real-time sensor feedback, will support uniform ripening, precision postharvest handling. The potential of mobile applications and/or with the advent of recently developed smart glasses integrated with artificial intelligence, producers will be able to assess fruit ripeness in real time simply by visually inspecting the fruit, enabling rapid and informed harvesting decisions. Overall, the adoption of AI-based solutions in tomato postharvest management holds promise for improving quality monitoring, reducing spoilage, and enhancing sustainability in the agri-food sector.

The integration of Artificial Intelligence (AI) into Business-to-Business (B2B) marketing within the hospitality industry is revolutionizing traditional practices, enhancing efficiency, and fostering innovation. Leveraging tools such as machine learning, predictive analytics, and automation, AI enables businesses to optimize operations, personalize customer experiences, and drive strategic partnerships. AI applications, including dynamic pricing, customer segmentation, and event planning optimization, have proven instrumental in addressing complex industry challenges. However, adoption is met with barriers such as data privacy concerns, skill gaps, and integration with legacy systems. Through case studies like Marriott and Accor Hotels, this study explores AI's transformative role in B2B marketing, emphasizing its potential to reshape industry dynamics and achieve sustainable growth. Despite challenges, the findings highlight AI's capacity to foster competitive advantage and innovation, paving the way for future advancements in the hospitality sector.

The integration of Artificial Intelligence (AI) in library management is redefining how libraries organize, deliver, and personalize information services. Leveraging technologies such as machine learning, natural language processing, and chatbots, AI enables automation of routine tasks, enhances information discovery, and facilitates user-centered services. This article explores the key applications of AI in areas such as cataloguing, personalized recommendations, virtual assistance, and digital preservation. It highlights the significant benefits of AI, including improved operational efficiency, personalized user experiences, and data-driven decision-making. At the same time, it addresses the challenges libraries face in AI adoption, including ethical concerns, data privacy, skill gaps among staff, and the need for inclusive, accessible technologies. The paper emphasizes that for AI to be effectively and responsibly integrated, libraries must invest in infrastructure, staff training, and ethical frameworks. As libraries transform into intelligent, adaptive knowledge hubs, AI presents both a powerful opportunity and a responsibility to serve diverse user communities with fairness, transparency, and innovation. Keywords: - Artificial Intelligence, Library management, Virtual Assistants, Chatbots, Natural language processing

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into Enterprise Resource Planning (ERP) systems represents a paradigmatic shift in organizational digital transformation strategies. This research investigates the revolutionary impact of AI/ML integration within SAP ecosystems, examining performance improvements, operational efficiency gains, and strategic advantages. Through comprehensive analysis of implementation patterns across diverse industry sectors, this study presents empirical evidence demonstrating significant enhancements in predictive analytics, automated decision-making, and resource optimization. The findings reveal that organizations implementing AI-enabled SAP solutions experience an average 35% improvement in operational efficiency, 42% reduction in manual processing time, and 28% increase in predictive accuracy for demand forecasting. This research contributes to the growing body of knowledge on intelligent ERP systems and provides practical insights for enterprise digital transformation initiatives.

Abstract. Introduction. Today, characterized by the rapid digitalization and intellectualization of the global economy, the integration of artificial intelligence (AI) in transportation services is gaining ground. Consequently, maritime logistics, as part of international trade, places new demands on the training of highly qualified personnel capable of continuously acquiring professional competencies to optimize processes, improve safety, and enhance economic sustainability. This article examines the challenge of training future transport industry specialists capable of adapting to the continuous integration of innovative digital technologies and artificial intelligence in the maritime economic sector. The aim of this article is to develop a scientifically sound concept for training personnel for the transport industry that takes into account the specifics of integrating artificial intelligence into the maritime economic sphere. The methodological foundations of the study include comparative pedagogical and personal-activity approaches, systems analysis, and modeling methods. Advanced concepts and theories in the fields of artificial intelligence, pedagogy, and transport economics are used as a methodological basis. Research Results. The author attempts to present conceptual aspects of training specialists for the transport industry, taking into account the specifics of AI implementation in the maritime economic sector. Based on an analysis of the current state of AI integration in the maritime economic sector, a core set of competencies and skills necessary for working with AI in transport is identified and the structure of a training model for transport specialists is presented. The conclusion describes the potential impact of this model on the development of the maritime industry as a whole.