Purpose The Fourth Industrial Revolution has accelerated technological advancements across industries, necessitating that countries, research institutions and enterprises enhance their technological competitiveness. A key challenge in this process is the ability to predict promising technologies and integrate them into strategic decision-making. However, existing methods predominantly rely on expert-driven qualitative assessments, which can be subjective and inconsistent. This study aims to address these limitations by proposing a quantitative, data-driven framework for technology foresight and strategic development in Korea’s railway industry, with a specific focus on emerging digital and control systems. Design/methodology/approach This research integrates autoregressive integrated moving average (ARIMA) time-series forecasting and weighs social network analysis (SNA) to systematically identify emerging technological trends. Using 4,352 railway-related patents from the Korean Intellectual Property Office (KIPO) from 1990 to 2023, technology keywords were extracted through text mining using TF-IDF scores. Promising technologies were identified by analyzing their temporal growth patterns (including forecast confidence intervals) and network influence, enabling a data-driven approach to forecasting technological developments and informing strategic planning. Findings The analysis demonstrates that the synergistic use of ARIMA-based forecasting and SNA-driven influence assessment provides a robust and systematic methodology for identifying emerging technologies. The results highlight that core technologies related to “control,” “signal,” “sensor,” “device” and “speed” are poised for significant growth and hold central positions within the technology network. This quantitative approach enhances technology management by reducing reliance on subjective expert opinions and providing objective, data-driven insights. Practical implications This study offers a structured methodology for organizations to enhance technology foresight and strategic planning. By leveraging predictive analytics, policymakers and industry leaders can proactively identify high-potential technologies, optimize resource allocation and foster innovation in the railway sector, particularly in the transition toward automated and intelligent transportation systems. Originality/value This research contributes to the field of technology forecasting by introducing a reproducible, quantitative framework that combines time-series analysis with network theory. By justifying the methodological choices and demonstrating their synergy, this framework offers a novel and robust alternative to traditional methods for strategic decision-making and technology development, particularly in mature, high-tech industries like the railway sector.

An accuracy-level method for robust evaluation in predictive analytics

Nano-AI synergy in food chemistry: smart analytical tools for quality, safety, and nutritional profiling.

The proposed review aims to provide a guide on current developments of artificial intelligence in Endourological practice, with an insight and descriptive guide on the potential integration of advanced technologies into Endourology. The purpose of this review article is also to gather the recent advances in artificial intelligence applications in urology, and to highlight the potential applications of novel trends and technologies being developed in artificial intelligence. Artificial intelligence is conquering the scientific landscape, and the medical field is not an exception. The work starts with a concise review of the state of the art and recent development of artificial intelligence and machine learning in urology and endourology. Moreover, an advanced description of novel technologies is presented in a clear manner, easy to follow by clinicians. The novel technologies include the causal artificial intelligence modeling, based on scientific constraints and directed acyclic graphs, as well as solving inverse problems and optimal decision making through reinforcement learning. The proposed manuscripts showcase potential applications of novel technologies in artificial intelligence, leading to democratizing its adoption. Theses novel technologies ease the explanation of the predictions performed by artificial intelligence algorithms, and follow causality and time sequencing constraints. Moreover, they can be useful to integrate expert's partial knowledge of complex medical phenomenon into their architecture by construction. The guide also showcases the potential applications and limitation in the field of urology. The proposed work ends in the current challenges hindering the democratization of artificial intelligence in Endourology.

Big Data and Predictive Analytics for Ambulatory and Inpatient Medicine: Utilizing Analytics for Population Health Management and Risk Stratification for Hospitalized Patients.

The role of digital twin technology in modern emergency care.

An integrated resampling and machine learning framework for predictive analytics of large wildfires

Application of blockchain-based digital twin technology in healthcare: A scoping review.

From filing to grant: Predicting patent outcomes in FinTech using a predictive analytics perspective

A cloud-enabled predictive analytics model for assessing health risks under climate variation

Predictive analytics for hydrogen–honge oil dual fuel engine using machine learning

This study introduces a novel, cloud-based predictive analytics framework tailored for pension fund performance management in Zimbabwe. Addressing limitations in traditional actuarial models, the proposed system leverages real-time data pipelines and explainable artificial intelligence (XAI) techniques to enhance forecasting accuracy and transparency. Using regression, classification, and deep learning models, it forecasts member contributions, identifies risks of contribution drops, and predicts member churn. The system’s cloud deployment ensures scalability and interactive integration with tools like Power BI for decision support. This solution significantly advances sustainable pension fund management for emerging economies.

Ensemble-based predictive analytics for demand forecasting in multi-channel retailing

Revolutionizing artificial intelligence enabled predictive analytics with smart consumer electronics for real-time healthcare monitoring

Modelling of enhanced predictive analytics and decision-making approach using recurrent autoencoder with attribute subset reduction for students’ academic performance

Organizations across sectors are rapidly adopting low-code development platforms to accelerate digital transformation, reduce time-to-market, and broaden participation in software creation. As these platforms increasingly incorporate artificial intelligence capabilities, including machine learning-driven decision logic, automation, and predictive analytics, new governance, security, and sustainability challenges emerge. From a broad perspective, AI-enabled low-code development reshapes traditional software engineering boundaries by abstracting code, decentralizing development responsibility, and embedding adaptive logic into business workflows. While these shifts deliver speed and flexibility, they also complicate oversight, risk management, and long-term system maintainability. This paper examines the governance, security, and technical debt implications of integrating AI into low-code environments. It analyzes how distributed development models challenge established accountability structures, policy enforcement, and auditability when decision-making logic is learned rather than explicitly defined. Security risks are explored across the data, model, and orchestration layers, including vulnerabilities related to data leakage, model misuse, inference manipulation, and overprivileged integrations. The study further investigates how AI components introduce new forms of technical debt, such as model drift, opaque dependencies, lifecycle misalignment, and hidden operational costs that accumulate over time. Narrowing the focus, the paper proposes a structured analytical framework that links governance mechanisms, security controls, and technical debt management practices to the architectural characteristics of AI-enabled low-code platforms. By synthesizing insights from software engineering, enterprise architecture, and responsible AI research, the study identifies design principles and mitigation strategies that support scalable, secure, and sustainable adoption. The findings provide practical guidance for organizations seeking to balance rapid innovation with long-term control, resilience, and trust in AI-augmented low-code development. These insights inform policy, design, and governance decisions across enterprise digital transformation initiatives.

Burnout has turned into one of the most pressing and measurable problems in the contemporary management of the workforce, specifically in those areas that are most exposed to emotional work-related stress and performance pressure. This review includes the use of scientifically proven behavioral models to predict and prevent burnout with sophisticated workforce analytics. Using the latest interdisciplinary literature, the paper has examined how behavioral science, artificial intelligence, data analytics, machine learning, and federated learning models could be combined to identify early signs of emotional exhaustion, workplace deviance, and disengagement. It identifies leadership styles, organizational culture, employee proficiency, and engagement measures as some of the factors that affect psychological well-being. In addition, the review explains how the job demands-resources theory and established clinical tools, including nomograms, can be used in stress management strategies. With the synthesis of evidence in different organizational and technological contexts, the paper provides a holistic evaluation of how predictive models are changing employee wellness and retention policies in modern organizations.

The rapid growth of digital services and interconnected systems has led to an increase in sophisticated cyber threats such as phishing, malware, network intrusions, ransomware, and deepfake-based attacks. Conventional security solutions are often limited to single-domain detection and lack adaptability, explainability, and user-centric intelligence. This paper presents CHRIS (Cyber Security Hub for Responsible Intelligence System), a unified, web-based cybersecurity platform that integrates Machine Learning (ML), Deep Learning (DL), and Generative AI to provide comprehensive and explainable threat detection. CHRIS incorporates six security modules: phishing detection, malware detection, network intrusion detection, password strength evaluation, deepfake detection, and ransomware detection, all accessible through a single interface. Random Forest, XGBoost, and Xception models are employed for predictive analysis, while Google Gemini is integrated to generate natural-language explanations, recommendations, and interactive assistance via an AI-powered chatbot. Experimental analysis demonstrates that the proposed system achieves high detection accuracy while significantly improving interpretability and usability. The results highlight the effectiveness of combining predictive security analytics with Generative AI, making CHRIS a practical and scalable solution for next-generation cybersecurity applications.

Fraud detection systems operate under strict latency requirements while processing large and dynamically varying transaction volumes. In such mission-critical environments, response time directly influences transaction success, customer experience and regulatory compliance. Traditional performance monitoring approaches are predominantly reactive and provide limited capability for anticipating performance degradation. This paper presents an AI-driven predictive analytics framework for estimating response time in a production-scale fraud detection system using real operational data. The proposed approach formulates response time estimation as a supervised regression problem based on system utilization metrics, workload intensity and error characteristics collected from a live production environment. Multiple machine learning models are evaluated to capture both linear and non-linear performance behavior. Experimental results demonstrate that ensemble-based models significantly outperform baseline approaches, highlighting the effectiveness of data-driven techniques for performance prediction. The framework further integrates predictive insights into a decision-support context, enabling proactive performance management, capacity planning and SLA risk mitigation. The study demonstrates the practical value of AI-driven predictive analytics for enhancing performance assurance in real-world fraud detection systems.

As the scale of modern service-based infrastructures grows beyond human ability to understand their functioning, the customary alerting frameworks (such as manually configured threshold alerts) become ineffective. Artificial intelligence and machine learning systems are being used in the reliability engineering field of cloud-native microservices to move from responding to problems after they happen to preventing and predicting them, especially because a failure can quickly impact other services. For instance, studies have demonstrated that neural network-based prediction systems can detect anomalous events before they affect a service's availability. This article discusses how SRE practices apply to AI-powered automation, as well as frameworks for anomaly detection algorithms, causal inference models and predictive analytics that enhance human decision-making. The article looks at machine learning models that help monitor infrastructure, tools for observing system performance, automated processes for handling incidents, smart ways to manage resource usage and updates, and systems that automatically spot changes in configuration while involving human oversight. Overall, the summary shows how smart systems find, examine, and connect the reasons for small drops in performance and make controlled fixes while following the principles of traditional reliability engineering.