The proliferation of fake news poses a significant threat to democratic institutions, public trust, and crisis management, particularly as it becomes a central tactic in coordinated information operations. This paper explores the application of machi...
Industrial organisations increasingly rely on machine learning (ML) to improve quality, maintenance, and planning in Industry 4.0/5.0 ecosystems. However, turning experimental models into reliable services on the production floor remains complex due...
The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, th...
Machine Learning (ML) models developed for the Edge have seen a massive uptake in recent years, with many types of predictive analytics, condition monitoring and pre-emptive fault detection developed and in-use on Internet of Things (IoT) systems ser...
Dynamic muscular workload assessments of tractor operators are rarely studied or documented, which is critical to improving their performance efficiency and safety. A study was conducted to assess and model dynamic load on muscles, physiological vari...
Recent advances in Machine Learning have significantly improved anomaly detection in industrial screw driving operations. However, most existing approaches focus on binary classification of normal versus anomalous operations or employ unsupervised me...
Water utilities frequently perform pipeline-emptying operations for maintenance, repair, and operational management. This process involves transient flow conditions with entrapped air. It must be carefully controlled, as the expansion of air pockets...
This study addresses the integration of machine learning (ML) with supervisory control and data acquisition (SCADA) systems to enhance predictive maintenance and operational efficiency in oil well monitoring. We investigated the applicability of adva...
Machine learning is increasingly used for spacecraft telemetry monitoring, fault diagnosis, health assessment, mission planning, visual navigation, and execution review. In space missions, explainability is a foundation of trustworthy artificial inte...
Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., sig...
Service Level Agreement (SLA) compliance in hospital transport processes is essential in terms of patient safety, service continuity, and resource efficiency. However, transport requests occur as irregular events, limiting the applicability of equall...
Research on how corporate social responsibility (CSR) practices linked to sustainable operations (SO) affect corporate financial performance (FP) is still limited. This study presents a novel methodological proposal to measure the individual impact o...
Port decarbonisation requires integrated energy solutions capable of reducing fossil-fuel dependence while maintaining safety, reliability, and operational continuity. This paper presents a machine-learning-based energy management tool designed to ag...
Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis whe...
The conditions for the safe and stable operation of a large power grid are highly interdependent and difficult to predict. In order to accurately understand the operational state of a large power grid, an efficient assessment model for its safe opera...
Reservoir operation during flood events is a critical component of water resources management, requiring the balancing of flood risk mitigation, water supply, and system safety under significant uncertainty. Traditional rule-based and optimization me...
Next-generation space observatories for high-energy gamma-ray astrophysics will increase scientific return using onboard machine learning (ML). This is now possible thanks to today’s low-power, radiation-tolerant processors and artificial intel...
This study predicted soil classification using data gathered during the operation of an earth-pressure-balance-type tunnel boring machine (TBM). The prediction methodology used machine learning to find relationships between the TBM’s operating...
The plastics manufacturing sector has experienced remarkable growth, requiring more optimized operations through reduced repair times and product defects. In this context, the theoretical aim of this research is to prove that the integration of class...
Solar power systems, such as photovoltaic (PV) systems, have become a necessary feature of zero-energy buildings because efficient building design and construction materials alone are not sufficient to meet the building’s energy consumption nee...
Machine learning (ML) revolutionized traditional machine fault detection and identification (FDI), as complex-structured models with well-designed unsupervised learning strategies can detect abnormal patterns from abundant data, which significantly r...
Financial systems operate under strict requirements for availability, low latency, resilience, and regulatory compliance, yet infrastructure management in these environments remains largely reactive. This paper addresses that limitation by proposing...
The operation and maintenance (O&M) of buildings plays an important role in ensuring that the buildings work normally, as well as reducing the damage caused by functional errors. There are obvious problems in the traditional O&M modality, and...
Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning fr...
Accurate assimilation of satellite-derived precipitation data remains a critical challenge in regional numerical weather prediction (NWP), particularly for convective-scale rainfall. Conventional observation operators rely on radiative transfer model...
Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating r...
The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in man...
This study examined the potential value of operational records from the Driving Record Analysis System (eTAS) as supplementary information for future service life evaluations of 119 ambulances in South Korea.An international literature review was com...
Operation rule plays an important role in the scientific management of hydropower reservoirs, because a scientifically sound operating rule can help operators make an approximately optimal decision with limited runoff prediction information. In past...
To investigate the influencing factors on the operation of an offshore wind turbine installation ship, a neural network, as a machine-learning method, is built to predict and analyze the motion response of a floating derrick in the process of a lifti...
Machine tools, as an indispensable equipment in the manufacturing industry, are widely used in industrial production. The harsh and complex working environment can easily cause the failure of machine tools during operation, and there is an urgent req...
This research is based on a systematic review of machine learning (ML) approaches for the cognitive load (CL) assessment of applications for unmanned aerial system (UAS) operator training. The review synthesises evidence on how ML techniques have bee...
Water injection is widely recognized as one of the most important operational approaches for enhanced oil recovery in oilfields. However, this process faces significant challenges due to the formation of sulfate and carbonate mineral scales caused by...
The widespread introduction of functionally-smart inverters will be an indispensable factor for the large-scale penetration of distributed energy resources (DERs) via the power system. On the other hand, further smartization based on the data-centric...
Scientific machine learning (SciML) offers an emerging alternative to the traditional modeling approaches for wave propagation. These physics-based models rely on computationally demanding numerical techniques. However, SciML extends artificial neura...
The growing energy demand has increased the number of wind turbines, raising the need to monitor blade health. Since blades are prone to damage that can cause severe failures, early detection is crucial. Machine learning-based monitoring systems can...
Operating mode identification is an important prerequisite for precise deceptive jamming technology against synthetic aperture radar (SAR). In order to solve the problems of traditional spaceborne SAR operating mode identification, such as low identi...
Rock strength is a key parameter for mine planning and operational optimization, but conventional laboratory testing is costly and provides limited spatial coverage. This study develops a methodology for classifying operational rock-strength classes...
Currently, machine learning (ML) methods provide a practical approach to model complex systems. Unlike purely analytical models, ML methods can describe the uncertainties (e.g., hysteresis, temperature effects) that are difficult to deal with, potent...
The last-mile logistics is regarded as one of the least efficient, most expensive, and polluting part of the entire supply chain and has a significant impact and consequences on sustainable delivery operations. The leading business model in e-commerc...
Determining/predicting the environment dominates a variety of marine operations, such as route planning and offshore installation. Significant wave height (Hs) is a critical parameter-defining wave, a dominating marine load. Data-driven machine learn...
This study considers the significance of drones in various civilian applications, emphasizing battery-operated drones and their advantages and limitations, and highlights the importance of energy consumption, battery capacity, and the state of health...
Air-cooling technology has been widely used for its water-saving advantage, and the performance of air-cooled condensers (ACC) has an important impact on the operation status of the unit. In this paper, the performance of ACC in a typical coal-fired...
The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the...
Canada’s in situ oil sands can help meet the global oil demand. Because of the energy-intensive extraction processes, in situ oil sands operations also play a critical role in meeting the global carbon budget. The steam oil ratio (SOR) is an indicato...
Prediction of transformer load-rate at regional substations can provide information to support operating condition assessment, capacity margin analysis, and the identification of potential high-load risks. This study selected a 40 MVA main transforme...
Distributed quantum information processing protocols such as quantum entanglement distillation and quantum state discrimination rely on local operations and classical communications (LOCC). Existing LOCC-based protocols typically assume the availabil...
Nowadays, MLOps approaches are gaining popularity thanks to their ability to apply DevOps best practices to machine learning models. They enable the automation and optimization of model training, deployment, and monitoring in various environments, wh...
Accurate estimation of tractor performance under various soil conditions is essential for enhancing operational efficiency in precision agriculture. This study developed machine learning models to estimate tractor performance based on key soil physic...
Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects s...
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