Search Results (29)

Smart cities are technologically advanced urban environments where interconnected systems and data-driven technologies enhance public service delivery and quality of life. These cities rely on information and communication technologies, the Internet of Things, big data, cloud computing, and other Industry 4.0 tools to support efficient city management and foster citizen engagement. Often referred to as digital cities, they integrate intelligent infrastructures and real-time data analytics to improve mobility, security, and sustainability. Ubiquitous sensors, paired with Artificial Intelligence, enable cities to monitor infrastructure, respond to residents’ needs, and optimize urban conditions dynamically. Given the increasing significance of Industry 4.0 in urban development, this study adopts a bibliometric approach to systematically review the application of these technologies within smart cities. Utilizing major academic databases such as Scopus and Web of Science the research aims to identify the primary Industry 4.0 technologies implemented in smart cities, assess their impact on infrastructure, economic systems, and urban communities, and explore the challenges and benefits associated with their integration. The bibliometric analysis included publications from 2016 to 2023, since the emergence of urban researchers’ interest in the technologies of the new industrial revolution. The task is to contribute to a deeper understanding of how smart cities evolve through the adoption of advanced technological frameworks. Research indicates that IoT and AI are the most commonly used tools in urban spaces, particularly in smart mobility and smart environments. Full article

This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies. Full article

Malaria, typhoid fever, respiratory tract infections, and urinary tract infections significantly impact women, especially in remote, resource-constrained settings, due to limited access to quality healthcare and certain risk factors. Most studies have focused on vector control measures, such as insecticide-treated nets and time series analysis, often neglecting emerging yet critical risk factors vital for effectively preventing febrile diseases. We address this gap by investigating the use of machine learning (ML) models, specifically extreme gradient boost and random forest, in predicting adult females’ susceptibility to these diseases based on biological, environmental, and socioeconomic factors. An explainable AI (XAI) technique, local interpretable model-agnostic explanations (LIME), was applied to enhance the transparency and interpretability of the predictive models. This approach provided insights into the models’ decision-making process and identified key risk factors, enabling healthcare professionals to personalize treatment services. Factors such as high cholesterol levels, poor personal hygiene, and exposure to air pollution emerged as significant contributors to disease susceptibility, revealing critical areas for public health intervention in remote and resource-constrained settings. This study demonstrates the effectiveness of integrating XAI with ML in directing health interventions, providing a clearer understanding of risk factors, and efficiently allocating resources for disease prevention and treatment. Full article

Smart city research often emphasises technology while neglecting how governance structures and resources influence outcomes. This study compares Tier 1 (Sydney, Melbourne, Brisbane, Adelaide) and Tier 2 (Geelong, Newcastle, Hobart, Sunshine Coast) Australian cities to evaluate how urban scale, economic capacity, governance complexity, and local priorities influence smart-enabled resilience. We analysed 22 official strategy documents using a two-phase qualitative approach: profiling each city and then synthesising patterns across technological integration, community engagement, resilience objectives and funding models. Tier 1 cities leverage extensive revenues and sophisticated infrastructure to implement ambitious initiatives such as digital twins and AI-driven services, but they encounter multi-agency delays and may overlook neighbourhood needs. Tier 2 cities deploy agile, low-cost solutions—sensor-based lighting and free public Wi-Fi—that deliver swift benefits but struggle to scale without sustained support. Across the eight cases, we identified four governance archetypes and six recurring implementation barriers—data silos, funding discontinuity, skills shortages, privacy concerns, evaluation gaps, and policy changes—which collectively influence smart-enabled resilience. The results indicate that aligning smart technologies with governance tiers, fiscal capacity, and demographic contexts is essential for achieving equitable and sustainable outcomes. We recommend tier-specific funding, mandatory co-design, and intergovernmental knowledge exchange to enable smaller cities to function as innovation labs while directing metropolitan centres towards inclusive, system-wide transformation. Full article

The digital transformation of public services has led to a surge in the volume and complexity of informatics-related complaints, often marked by ambiguous language, inconsistent terminology, and fragmented reporting. Conventional keyword-based approaches are inadequate for detecting semantically similar issues expressed in diverse ways. This study proposes an AI-powered framework that employs BERT-based sentence embeddings, semantic clustering, and classification algorithms, structured under the CRISP-DM methodology, to standardize and automate complaint analysis. Leveraging real-world interaction logs from a public sector agency, the system harmonizes heterogeneous complaint narratives, uncovers latent issue patterns, and enables early detection of technical and usability problems. The approach is deployed through a real-time dashboard, transforming complaint handling from a reactive to a proactive process. Experimental results show a 27% reduction in repeated complaint categories and a 32% increase in classification efficiency. The study also addresses ethical concerns, including data governance, bias mitigation, and model transparency. This work advances citizen-centric service delivery by demonstrating the scalable application of AI in public sector informatics. Full article

Agriculture and silviculture offer interesting opportunities for food, energy, and construction sectors, but to transform such raw materials into valuable products, multiple engineering works must be carried out within R&D, innovation projects, and programs. The classical official decision to promote or supervise such projects involves many agents and criteria but rarely considers engineering quality, reusability, or other valuable and measurable attributes considered in ISO 25.000 or in value engineering guidelines. Missing them would increase technological, business, and programmatic risks, potentially wasting public money or credibility. Large projects are not free from these risks, and it is not a kind of madness to derive R&D and innovation funds to enable access to such valuable knowledge comprehensively, with models. In this context, communications and services, construction, and renewables play a crucial role in smart rural environments. Model-Based Systems Engineering (MBSE) and generative Artificial Intelligence (AI), combined with Natural Language Processing (NLP), are expected to help Knowledge Management (KM) in engineering and governance to supervise value engineering and their relationship with other metrics. Starting with a motivational and multidisciplinary framework for a smart rural transformation for System of Systems (SoS), the authors conduct specific bibliographic research on MBSE-NLP-AI use for automatizing systems engineering supervision at program governance levels. Full article

Background/Objectives: Diabetes is a common public health disease that affects patients mentally, physically, and economically. It requires lifestyle changes such as blood sugar control and regular contact with healthcare services. Artificial intelligence has developed rapidly in many different areas in recent years, including healthcare and nursing. The aim of this study is to explore how artificial intelligence can be used as a tool for patients with diabetes mellitus. Methods: An integrative literature review design was chosen according to Whittemore and Knafl (2005). Electronic searches in databases were conducted across Pub-Med, CINAHL Complete (EBSCO), and ACM Digital Library until September 2024. A total set of quantitative and qualitative articles (n = 15) was selected and reviewed using a Mixed Method Appraisal Tool. Results: Artificial intelligence is an effective tool for patients with diabetes mellitus, and various models are used. Three themes emerged: artificial intelligence as a tool for blood sugar monitoring for patients with diabetes mellitus, artificial intelligence as a decision support for diabetic wounds and complications, and patients’ requests for artificial intelligence capabilities in relation to tools. Artificial intelligence can create better conditions for patient self-care. Conclusions: Artificial intelligence is a valuable tool for patients with diabetes mellitus and enables the district nurse to focus more on person-centered care. The technology facilitates the patient’s blood sugar monitoring. However, more research is needed to ensure the safety of AI technology, the protection of patient privacy, and clarification of laws and regulations within diabetes care. Full article

According to recent global public health studies, chronic kidney disease (CKD) is becoming more and more recognized as a serious health risk as many people are suffering from this disease. Machine learning techniques have demonstrated high efficiency in identifying CKD, but their opaque decision-making processes limit their adoption in clinical settings. To address this, this study employs a generative adversarial network (GAN) to handle missing values in CKD datasets and utilizes few-shot learning techniques, such as prototypical networks and model-agnostic meta-learning (MAML), combined with explainable machine learning to predict CKD. Additionally, traditional machine learning models, including support vector machines (SVM), logistic regression (LR), decision trees (DT), random forests (RF), and voting ensemble learning (VEL), are applied for comparison. To unravel the “black box” nature of machine learning predictions, various techniques of explainable AI, such as SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME), are applied to understand the predictions made by the model, thereby contributing to the decision-making process and identifying significant parameters in the diagnosis of CKD. Model performance is evaluated using predefined metrics, and the results indicate that few-shot learning models integrated with GANs significantly outperform traditional machine learning techniques. Prototypical networks with GANs achieve the highest accuracy of 99.99%, while MAML reaches 99.92%. Furthermore, prototypical networks attain F1-score, recall, precision, and Matthews correlation coefficient (MCC) values of 99.89%, 99.9%, 99.9%, and 100%, respectively, on the raw dataset. As a result, the experimental results clearly demonstrate the effectiveness of the suggested method, offering a reliable and trustworthy model to classify CKD. This framework supports the objectives of the Medical Internet of Things (MIoT) by enhancing smart medical applications and services, enabling accurate prediction and detection of CKD, and facilitating optimal medical decision making. Full article

In an era where public safety hinges on real-time intelligence and rapid response, this paper delves into the pivotal role of location-based services (LBSs) in empowering law enforcement and fire rescue operations. GPS tracking systems have revolutionized situational awareness and resource management, yet they come with critical security and privacy challenges, including unauthorized access, real-time data interception, and insider threats. To address these vulnerabilities, this study introduces an innovative framework that combines blockchain, artificial intelligence (AI), and IoT technologies to redefine emergency management and public safety systems. Voice-command virtual assistants powered by AI enable hands-free operations, enhance hazard detection, and optimize resource allocation in real time, while blockchain’s decentralized and tamper-proof architecture ensures data integrity and security. By integrating these cutting-edge technologies, the research showcases a system design that not only secures sensitive information but also drives operational efficiency and resilience. With applications spanning smart cities, autonomous systems, and fire rescue operations, this study offers a transformative vision for public safety, emphasizing technology integration, digital innovation, and trust-building. These advancements promise not only to protect responders and communities but also to redefine the standards of security and efficiency in modern emergency management. Full article

Medical laboratories (MLs) are vital in global healthcare delivery, enhancing diagnostic accuracy and supporting clinical decision-making. This systematic review examines the multifaceted contributions of ML, emphasizing their importance in pandemic preparedness, disease surveillance, and the integration of innovative technologies such as artificial intelligence (AI). Medical laboratories are equally crucial to clinical practices, offering essential diagnostic services to identify diseases like infections, metabolic disorders, and malignancies. They monitor treatment effectiveness by analyzing patient samples, enabling healthcare providers to optimize therapies. Additionally, they support personalized medicine by tailoring treatments based on genetic and molecular data and ensure test accuracy through strict quality control measures, thereby enhancing patient care. The methodology for this systematic review follows the PRISMA-ScR guidelines to systematically map evidence and identify key concepts, theories, sources, and knowledge gaps related to the roles and impact of MLs in public health delivery. This review involved systematic searching and filtering of literature from various databases, focusing on studies from 2010 to 2024, primarily in Africa, Asia, and Europe. The selected studies were analyzed to assess their outcomes, strengths, and limitations regarding MLS roles, impacts, and integration within healthcare systems. The goal was to provide comprehensive insights and recommendations based on the gathered data. The article highlights the challenges that laboratories face, especially in low- and middle-income countries (LMICs), where resource constraints hinder effective healthcare delivery. It discusses the potential of AI to improve diagnostic processes and patient outcomes while addressing ethical and infrastructural challenges. This review underscores the necessity for collaborative efforts among stakeholders to enhance laboratory services, ensuring that they are accessible, efficient, and capable of meeting the evolving demands of healthcare systems. Overall, the findings advocate for strengthened laboratory infrastructures and the adoption of advanced technologies to improve health outcomes globally. Full article

This study uses a literature review method to examine the social impact of technological advancements in sentiment analysis and its potential and consequences over the next 20 years. Key findings indicate that sentiment analysis technology significantly benefits customer service, business decision-making, and real-time reputation monitoring sectors. It enables more responsive policy design by understanding public emotions in political and social contexts. However, data privacy, misinformation, and diminished critical thinking persist. This study contributes to the existing literature by comprehensively analyzing ethical and regulatory needs and identifying integration opportunities with IoT, big data, and AI to maximize benefits while minimizing risks. Practically, it offers actionable policy recommendations for leveraging sentiment analysis responsibly to promote societal well-being and foster sustainable development. Full article

Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. Full article

With the rising incidence of traffic accidents and growing environmental concerns, the demand for advanced systems to ensure traffic and environmental safety has become increasingly urgent. This paper introduces an automated highway safety management framework that integrates computer vision and natural language processing for real-time monitoring, analysis, and reporting of traffic incidents. The system not only identifies accidents but also aids in coordinating emergency responses, such as dispatching ambulances, fire services, and police, while simultaneously managing traffic flow. The approach begins with the creation of a diverse highway accident dataset, combining public datasets with drone and CCTV footage. YOLOv11s is retrained on this dataset to enable real-time detection of critical traffic elements and anomalies, such as collisions and fires. A vision–language model (VLM), Moondream2, is employed to generate detailed scene descriptions, which are further refined by a large language model (LLM), GPT 4-Turbo, to produce concise incident reports and actionable suggestions. These reports are automatically sent to relevant authorities, ensuring prompt and effective response. The system’s effectiveness is validated through the analysis of diverse accident videos and zero-shot simulation testing within the Webots environment. The results highlight the potential of combining drone and CCTV imagery with AI-driven methodologies to improve traffic management and enhance public safety. Future work will include refining detection models, expanding dataset diversity, and deploying the framework in real-world scenarios using live drone and CCTV feeds. This study lays the groundwork for scalable and reliable solutions to address critical traffic safety challenges. Full article

This study aims to explore the transformative potential of Artificial Intelligence (AI) in international trade, focusing on its key roles in optimizing trade operations, enhancing trade finance, and expanding market access. In trade optimization, AI leverages advanced machine learning and predictive analytics to enhance demand forecasting, route optimization, and customs procedures, leading to more efficient logistics and inventory management. In trade finance, AI can automate document processing and risk assessment, increasing access to finance and enhancing transactional transparency, particularly through integration with blockchain technology. In terms of market access, AI-driven analytics can identify consumer trends and competitive dynamics, enabling personalized marketing and overcoming linguistic and cultural barriers. Due to the lack of quantitative data, this study employed qualitative research methods, specifically a multiple-case-study approach. The case studies of leading companies such as Alibaba, DHL, and Maersk showcase how they leverage AI to optimize their trade operations, improve customer service, and achieve greater efficiency. These real-world examples demonstrate AI’s practical applications and significant benefits in the global trade landscape. However, the adoption of AI in international trade is not without challenges. These include issues around data quality, ethical concerns, technological complexity, and public perception. Policy recommendations highlight the need for a robust data infrastructure, establishing ethical AI guidelines, and fostering international cooperation to align data protection regulations. Full article

This study investigates the critical success factors (CSFs) influencing the procurement of artificial intelligence (AI) systems within the United Arab Emirates (UAE) public sector. While AI holds immense potential to enhance public service delivery, its successful integration hinges on critical factors. This research utilizes Interpretive Structural Modeling (ISM) to analyze the CSFs impacting AI procurement within the UAE public sector. Through ISM, a structural model is developed to highlight the interrelationships between these CSFs and their influence on the procurement process, outlining the key elements for successful AI procurement within the UAE public sector. Based on the literature review and expert validation from the UAE public sector, ten CSFs were identified. This study found that clear needs assessment is the most influential CSF, while the long-term value of AI systems or services is the least influential. This study provides policymakers and public sector leaders with valuable insights, enabling them to formulate effective strategies to optimize the procurement process and establish a strong foundation for AI adoption. Finally, this will lead to an improved and more efficient public service delivery in the UAE. Full article