This study explores the transformative potential of artificial intelligence (AI) in revolutionizing earthquake risk mitigation across six key areas. Unlike traditional approaches, this paper examines how AI-driven innovations can uniquely enhance ear...
Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), learns by training and is restricted by the amount and quality of training data. Training involves a tradeoff between prediction bias and variance controlled by mod...
Background: Generative AI (GenAI) mental health chatbots are increasingly being developed to help address persistent barriers to mental healthcare. Unlike earlier rule-based and retrieval-based systems, GenAI chatbots generate open-ended outputs that...
The maritime industry is undergoing a digital transformation, integrating automation, artificial intelligence (AI), and the Internet of Things (IoT) to enhance operational efficiency and safety. However, this technological evolution has also increase...
Agile software project management (ASPM) serves modern industries to conduct iterative development of complicated code bases. The decision-making process in Agile environments regularly depends on individual opinions, creating ineffective results for...
The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the...
This study proposes a conceptual framework for applying artificial intelligence (AI) to sustainable development projects, emphasizing its role in mitigating risks, enhancing flexibility, and fostering resilience. A case study analysis demonstrates th...
Accurate subaqueous morphological modelling is crucial for sustainable aquatic ecosystem management, yet single-sensor hydrographic surveys remain constrained by environmental limitations. Through a systematic review, this study aims to evaluate the...
Emerging vector-borne zoonoses are driven by climate change, biodiversity loss and land-use change. Surveillance remains fragmented, largely reactive and often blind to the risk signals that precede spillover. This narrative review examines how artif...
Climate change disproportionately threatens low- and middle-income countries, yet integrated assessments combining socio-economic fragility with physical hazards remain limited. This study quantifies multi-dimensional climate vulnerability and derive...
Background: Despite the apparent importance of global catastrophe risks (GCRs), human society has invested relatively little to reduce them. One possible reason is that we do not understand the significance of reducing GCRs, especially when measured...
In rapidly urbanizing environments, environmental stressors—such as air pollution, noise, heat, and green space depletion—substantially exacerbate public health burdens, contributing to the global rise of non-communicable diseases, partic...
17 November 2025
Effectively managing risk is essential for fostering innovation in healthcare, especially with advancements like artificial intelligence (AI) and machine learning (ML). These technologies aim to enhance accessibility, efficiency, and equity in health...
In recent years, the rapid growth of cryptocurrency markets has highlighted the urgent need for advanced security solutions capable of addressing a spectrum of unique threats, from phishing and wallet hacks to complex blockchain vulnerabilities. This...
Radon exposure is the second leading cause of lung cancer worldwide, yet monitoring strategies remain limited, expensive, and unevenly applied. Recent advances in the Internet of Things (IoT) offer the potential to change radon surveillance through l...
Climate risk increasingly threatens regional industrial chain resilience, while artificial intelligence (AI) offers new tools for mitigating its effects. Using panel data for 167 Chinese prefecture-level cities from 2008 to 2023, this study examines...
Background: This study aims to systematically analyze the structural transition of AI in mental health, differentiating between passive tools and autonomous agents, and to propose a governance framework to facilitate responsible integration or mitiga...
This position paper proposes a conceptual framework (CF-BIAI-SXT) for integrating blockchain with AI to enhance ethical governance, transparency, and privacy in high-risk AI applications that ensure societal resilience through the mitigation of sexua...
AI predictive tools for migration management in the humanitarian field can significantly aid humanitarian actors in augmenting their decision-making capabilities and improving the lives and well-being of migrants. However, the use of AI predictive to...
Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failur...
18 March 2022
Artificial intelligence can be used to realise new types of protective devices and assistance systems, so their importance for occupational safety and health is continuously increasing. However, established risk mitigation measures in software develo...
This paper presents the AI Trustworthiness Assessment Framework (AI_TAF), a comprehensive methodology for evaluating and mitigating trustworthiness risks across all stages of an AI system’s lifecycle. The framework accounts for the criticality...
In the era of rapid technological advancement, understanding and confronting the challenges posed by AI systems are imperative. The concept of Superintelligence denotes the potential for AI to surpass the intellectual capacities of even the most bril...
Artificial intelligence (AI) and smart automation are revolutionizing the global supply chain ecosystem at an accelerated pace, providing tremendous potential for resilience, innovation, efficacy, and profitability. This paper examines how AI, machin...
Risk management in large-scale construction projects is a critical yet complex process influenced by financial, safety, environmental, scheduling, and regulatory uncertainties. Effective risk management contributes directly to project optimization by...
The aquaculture industry is growing rapidly. It is the fastest growing food industry in the world, with production expanding 16-fold between 1985 and 2018, according to the Food and Agriculture Organization FAO. The industry operates in an environmen...
Artificial Intelligence (AI) has strong potential in health monitoring systems to support high-quality healthcare while mitigating cybersecurity risks. AI-based solutions for health and wellness applications, particularly for cardiovascular disease m...
Risk management driven by AI has the potential to play an important role in sustainable decision-making by offering predictive insights and scenario modeling capabilities. This proactive approach empowers top management to align informed decisions in...
This study critically examines the trustworthiness of widely used AI applications, focusing on their integration into daily life, often without users fully understanding the risks or how these threats might affect them. As AI apps become more accessi...
Artificial intelligence is increasingly used in all fields, especially in the area of risk management within Integrated Management Systems (IMS). The paper aims to highlight the role of artificial intelligence (AI) in risk management, therefore provi...
AI technology is widely used in contemporary supply chain management; however, its asymmetric effects on upstream and downstream supply chain concentration risks remain underexplored. Based on information asymmetry theories, this study empirically ex...
This paper proposes the concept of risk-aware actual value as a pivotal metric for evaluating the viability and desirability of AI projects and services in accordance with the AI Act. The framework establishes a direct correlation between the level o...
The integration of artificial intelligence (AI) with medical genetics is transforming healthcare by addressing the analytical challenges posed by the vast complexity of multi-omics data. This review explores the synergistic convergence of these field...
Background: Discrepancies between endoscopic and pathological diagnoses commonly occur. From pre-malignant neoplasms to early gastric cancers (EGC), this inconsistency often leads to significant alteration of management. AI (artificial intelligence)...
23 March 2026
The rapid integration of AI customer service in e-commerce raises an important managerial question: Can AI effectively reduce product-related information asymmetry and improve sales performance across different product types? While prior research hig...
This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle...
This study advances understanding of public trust in artificial intelligence (AI) by distinguishing between overall trust in AI as a system and trust in specific AI components, and by disentangling the roles of perceived capacity, risk, and personhoo...
This review explores the transformative role of artificial intelligence (AI) in hypertension care, summarizing and analyzing published works from the last three years in this field. Hypertension contributes to a significant healthcare burden both at...
This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed...
Artificial intelligence (AI) has played a pivotal role in enhancing the efficiency of financial technology (Fintech), ultimately contributing to the stability of the banking sector. The advancements in Fintech driven by AI tools are significantly imp...
This study examines the transformative impact of artificial intelligence (AI) on tax auditing through a PRISMA-compliant systematic literature review and textometric analysis. By analyzing literature published between 2015 and 2025 using IRAMUTEQ, we...
Acute ischemic stroke (AIS) remains a major cause of mortality and long-term disability worldwide, driven by complex and multifaceted etiological factors. Metabolic dysregulation, gastrointestinal microbiome alterations, and systemic inflammation are...
The growth of Agentic AI systems in Industrial Automation has brought forth new cybersecurity issues which in turn put at risk the reliability and integrity of these systems. In this study we look at the cybersecurity issues in industrial automation...
Assistive technology (AT) is increasingly utilized across various sectors, including digital healthcare and sports education. E-learning plays a vital role in enabling students with special needs, particularly those in remote areas, to access educati...
Integrating artificial intelligence (AI) technologies into law enforcement has become a concern of contemporary politics and public discourse. In this paper, we qualitatively examine the perspectives of AI technologies based on 20 semi-structured int...
Due to the increased unpredictability and severity of weather patterns caused by climate change, traditional farming practices and risk management strategies are becoming increasingly inadequate. In this paper, we explore the literature to understand...
In the digital era, understanding the intention to use generative AI is critical, as it enhances productivity, transforms workflows, and enables humans to focus on higher-value tasks. Drawing upon the unified theory of acceptance and use of technolog...
The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxie...
This paper proposes SynergyAI, an AI–human pair programming tool that represents predictive models as dataflows composed of AI, input, and output nodes. By visualizing decision tree models and integrating them with dataflow diagrams, SynergyAI...
Geospatial Artificial Intelligence (GeoAI) has been advancing and altering geographic information systems and Earth observation by enhancing the computation and understanding capabilities of these systems. In this context, the application of GeoAI in...
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