Real-time Monitoring System Research Articles

Co-design of an electronic patient-reported outcome symptom monitoring system for immunotherapy toxicities.

Utilising electronic patient-reported outcomes (ePRO) to monitor symptoms can improve patient outcomes. However, ePRO systems are typically not co-designed with end-users which may limit their utility and long-term sustainability. We aimed to co-design a real-time ePRO symptom monitoring system for immune checkpoint inhibitor (ICI) toxicities. We conducted three co-design workshops at an Australian quaternary cancer centre. Participants were patients who had received/were receiving ICI or their caregivers, managing clinicians, administration staff, and electronic medical record (EMR) analysts. Workshop 1 identified preferences for an ideal ePRO system, informing the development of a prototype and generic workflow. Workshop 2 sought feedback on the prototype and workflow. Workshop 3 reviewed the updated prototype and adapted the generic workflow to create a site-specific workflow. Workshop transcripts were analysed thematically. Twenty-seven participants were included. Themes relating to system content and functionality included the need for customisation according to anticipated ICI toxicities; maximising patient accessibility, comprehension, and usability; providing ICI-specific self-management advice; and maximising clinician interpretability and usability. Themes relating to the model of care included the importance of maintaining the "human element" within the ePRO system, providing 24-hour support, alignment with existing clinical workflows, and automation of symptom monitoring processes using the EMR. Early, broad stakeholder engagement through co-design may improve the relevance, uptake, and sustainability of ePRO systems. Future work will involve usability and acceptance testing of the prototype, followed by implementation into routine care.

Strengthening environmental safety in oil and gas operations: Optimizing health, safety, and environmental (HSE) protocols

The oil and gas industry faces significant environmental challenges, including spills, emissions, and equipment failures, which pose serious risks to ecosystems and human health. This review explores strategies for optimizing Health, Safety, and Environmental (HSE) protocols in oil and gas operations, emphasizing technological advancements, proactive safety measures, and industry-regulator collaboration. It examines how real-time monitoring systems, predictive analytics, and automation improve risk management, while best practices such as comprehensive risk assessments and safety training mitigate potential hazards. The paper also highlights the need for flexible regulatory frameworks and enhanced transparency through data sharing. The findings underscore the importance of refining HSE protocols to ensure environmental safety and operational resilience. Recommendations include expanding the use of advanced technologies, strengthening safety cultures, and fostering closer cooperation between the industry and regulatory bodies. These strategies aim to reduce environmental risks, improve compliance, and enhance sustainability in the sector.

AI-driven fraud detection in banking: A systematic review of data science approaches to enhancing cybersecurity

The proliferation of sophisticated financial fraud and cybersecurity threats in the banking sector necessitates advanced detection and prevention strategies. This comprehensive review examines the current state of artificial intelligence and data science techniques in fraud detection systems within banking institutions, with particular emphasis on enhancing cybersecurity measures. Through systematic analysis of peer-reviewed literature, industry reports, and empirical studies from the past decade, we evaluate the effectiveness of various machine learning algorithms, deep learning architectures, and real-time monitoring systems in fraud detection. Meta-analysis of 47 studies indicates that contemporary AI-powered fraud detection systems achieve detection rates of 87-94% while reducing false positives by 40-60% compared to traditional rule-based methods. Furthermore, integrated AI approaches combining supervised and unsupervised learning techniques consistently demonstrate superior performance in detecting novel fraud patterns and adapting to emerging threats. This review synthesizes current research findings, identifies gaps in existing literature, and provides a comprehensive framework for implementing robust fraud detection systems in banking institutions.

Real-time AI Monitoring System for Wildfire using YOLO Model based on Deep-Learning

Real-time AI Monitoring System for Wildfire using YOLO Model based on Deep-Learning

Strategic approaches to enhancing credit risk management in microfinance institutions

Microfinance institutions (MFIs) play a critical role in providing financial services to underserved, low-income populations, yet face significant challenges in managing credit risk due to the nature of their clientele and loan structures. Enhancing credit risk management in MFIs is essential for ensuring financial stability, reducing default rates, and fostering sustainable growth. This review examines strategic approaches to credit risk management tailored to the microfinance context, where traditional credit assessment methods may be less effective. It explores innovative client profiling techniques, such as segmented risk analysis and customized credit scoring models, which leverage demographic and behavioral data to more accurately assess borrower reliability. The use of data-driven decision-making, particularly through predictive analytics and machine learning, is highlighted as a means to anticipate client repayment behaviors and improve risk forecasting. Further, real-time loan monitoring systems with early warning indicators offer MFIs proactive risk mitigation strategies, enabling prompt interventions to prevent defaults. Institutional improvements, including standardized risk frameworks, enhanced training for loan officers, and regular compliance checks, strengthen organizational resilience against credit risks. Portfolio diversification strategies, such as sectoral and geographic diversification, and the integration of group lending models, are also discussed as effective means of reducing risk concentration. By implementing these multifaceted, strategic approaches, MFIs can improve their credit risk management frameworks, better serve their clients, and achieve greater financial sustainability. The review emphasizes the importance of adapting to rapidly evolving risk factors and adopting innovative tools and practices to meet the unique challenges of microfinance. The findings advocate for continuous research and investment in advanced credit risk methodologies, ultimately contributing to the broader goal of financial inclusion.

Detecting insider threats in banking using AI-driven anomaly detection with a data science approach to cybersecurity

The banking sector faces an increasingly critical challenge in detecting and preventing insider threats, which account for significant financial losses and data breaches annually. This comprehensive review explores how artificial intelligence-driven anomaly detection, integrated with advanced data science approaches and cybersecurity frameworks, is transforming insider threat detection in banking institutions. By synthesizing current research in behavioral analytics, machine learning methodologies, and employee activity monitoring, the study examines how AI-driven technologies are revolutionizing traditional approaches to insider threat detection and risk management. The review critically analyzes emerging AI-driven methodologies, particularly focusing on unsupervised learning techniques, behavioral pattern analysis, and real-time employee activity monitoring systems. Through an extensive examination of behavioral analytics frameworks, privileged access monitoring, and user entity behavior analytics (UEBA), the research illuminates both the potential and challenges of AI-powered insider threat detection. The investigation reveals significant advancements in behavioral anomaly detection, predictive modeling of employee activities and network behavior analysis while simultaneously addressing critical privacy considerations and regulatory complexities specific to employee monitoring.

AI-enhanced predictive maintenance systems for critical infrastructure: Cloud-native architectures approach

Critical infrastructure (CI), such as power grids, transportation systems, and telecommunications networks, is becoming increasingly complex, requiring sophisticated maintenance strategies and procedures to guarantee optimal performance and system durability. This paper examines the transformational potential of AI-driven predictive maintenance systems, highlighting their ability to prevent system failures, minimize downtime, and enhance resource efficiency. Integrating machine learning algorithms with real-time data analytics allows predictive maintenance frameworks to accurately foresee equipment failures, facilitating timely interventions that reduce the risk of catastrophic infrastructure breakdowns. This study primarily examines the development of cloud-native architectures, which include containers, microservices, and orchestration tools like Kubernetes, to facilitate the scalability, flexibility, and resilience required for contemporary CI maintenance systems. These designs facilitate the seamless integration of predictive maintenance solutions across geographically dispersed infrastructure, enabling effective administration of extensive datasets produced by Internet of Things (IoT) sensors, operational logs, and edge computing nodes. The document examines the essential function of intelligent data orchestration in facilitating the prompt gathering, processing, and analysis of operational data, which is vital for AI models to provide precise predictions. The amalgamation of AI-driven predictive maintenance with 5G and forthcoming 6G networks is poised to transform real-time system monitoring, diminishing latency and enhancing decision-making efficacy. Utilizing AI and cloud-native technologies substantially enhances system reliability, cost-effectiveness, and comprehensive infrastructure optimization. This article thoroughly analyses how AI, cloud-native platforms, and intelligent data orchestration may be utilized to tackle the changing maintenance issues of critical infrastructure by examining real-world case studies from sectors like power grids, telecommunications, and transportation. Integrating AI, cloud computing, and IoT in predictive maintenance improves system reliability and prepares critical infrastructure for future autonomous management and optimization developments. The study finishes by discussing new trends, such as the integration of digital twins and the synergies between AI and cloud-native solutions, which will enhance predictive maintenance capabilities.

IoT based real-time water quality monitoring system in water treatment plants (WTPs)

This study presents the development and implementation of an Internet of Things (IoT)-based real-time water quality monitoring system tailored for water treatment plants (WTPs). The system integrates advanced sensor technologies to continuously monitor key water quality parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), and temperature. Data collected by these sensors is transmitted through a robust communication network to a centralized monitoring platform that utilizes cloud-based storage and analytics. The system's design includes a PLC-based control mechanism, allowing for flexible setup modifications and the easy addition of new monitoring parameters. The IoT-based system, powered by a low-energy 29-W configuration, offers accurate and reliable data with a minimal error margin of 0.1–0.2 across various parameters. The research highlights the system's ability to provide real-time alerts, historical data logging, and remote monitoring, all of which contribute to enhanced operational efficiency, proactive maintenance, and informed decision-making. This innovative approach to water quality management not only improves the effectiveness of WTP operations but also ensures environmental sustainability and public health safety. The study underscores the significant potential of IoT technologies in revolutionizing water quality monitoring practices.

Influence of Particulate Matter and Carbon Dioxide on Students’ Emotions in a Smart Classroom

The effects of air quality on health and cognition are well documented, but few studies have focused on its impact on emotions, leaving this area underexplored. This study investigates the influence of environmental factors—specifically particulate matter (PM1, PM2.5, and PM10) and carbon dioxide (CO2)—on students’ basic emotions in secondary school classrooms. For the collection of environmental data, we used low-cost sensors, which were carefully calibrated to ensure acceptable accuracy for monitoring air quality variables, despite inherent precision limitations compared to traditional sensors. Emotions were recorded via camera and analyzed using a custom-developed code. Based on these data, we found significant but modest correlations, such as the negative correlation between PM levels and happiness, and positive correlations of CO2 concentrations with fear and disgust. The regression models explained between 36% and 62% of the variance in emotions like neutrality, sadness, fear, and happiness, highlighting nonlinear relationships in some cases. These findings underscore the need for improved classroom environmental management, including the implementation of real-time air quality monitoring systems. Such systems would enable schools to mitigate the negative emotional effects of poor air quality, contributing to healthier and more conducive learning environments. Future research should explore the combined effects of multiple environmental factors to further understand their impact on student well-being.

Research on Key Technologies for Automated Control of Packaging Production Lines

This paper focuses on the key technologies of automated control in packaging production lines, systematically explaining the importance of automation control in improving production efficiency, quality stability, and flexible production. By analyzing core technologies such as sensor and detection technology, Programmable Logic Controllers (PLC), servo drive and motion control systems, and data acquisition and real-time monitoring systems, the paper explores the practical application effects of these technologies in different scenarios. Combined with typical case analyses, the paper summarizes the current status and challenges of automated control for packaging production lines and proposes insights on future development trends and integration with smart manufacturing, providing theoretical support and practical guidance for further enhancing the level of automation and intelligence of packaging production lines.

Integrating Deep Learning and CCTV for Real-Time Health Monitoring in Schools

Abstract—This project proposes a real-time health monitoring system for schools, integrating deep learning techniques with CCTV technology to enhance student well-being. By utilizing advanced computer vision algorithms, the system analyzes video feeds from cameras to continuously assess students' health, focusing on facial expressions and behaviors. The DeepFace model is employed to detect and recognize individual students and analyze their facial expressions for signs of distress or other health-related issues. When the system identifies concerning health indicators, it generates immediate alerts, which are sent to school administrators through a user-friendly web interface. This interface allows administrators to monitor health analytics, view real-time alerts, and access historical data for effective decision-making and intervention. The proposed framework offers a proactive approach to student health monitoring, ensuring a safer and more supportive school environment. Keywords—DeepFace, CCTV, Pattern recognition, colour intensity

Selection of Internet of Things-enabled sustainable real-time monitoring strategies for manufacturing processes using a disc spherical fuzzy Schweizer–Sklar aggregation model

The emergence of the Internet of Things (IoT) for monitoring in real-time is geared towards sustainable energy consumption practices by taking control over energy loss. The promising potential of current IoT real-time monitoring systems paves the way for future developments in monitoring devices with eco-friendly sensing capabilities. As a result, the creation of effective IoT real-time monitoring devices targeted at decreasing energy loss becomes crucial. This modeling procedure falls under the realm of multiple-attribute group decision-making (MAGDM), aiming to integrate the Schweizer–Sklar (SS) τ-norm and τ-conorm within the disc spherical fuzzy (D-SF) framework. The objective is to enhance the flexibility of D-SF in dealing with intricate and uncertain data. The core focus of this research is on deriving SS τ-norm and τ-conorm for D-SF data, consequently introducing innovative aggregation operators. The article offers the fundamental D-SF operations using SS aggregation operators in a systematic manner, with thorough theorem justifications. A new MAGDM tool is presented, created simply to manage ambiguous and imprecise data utilizing the suggested operators. Our model is specifically designed to tackle the critical issue of reducing energy loss in IoT real-time monitoring systems. The research not only focuses on model accuracy but also emphasizes its effectiveness in solving this pressing problem, demonstrating significant advancements in sustainable energy practices. Moreover, the proposed aggregation operators are subjected to a comparative analysis. This comprehensive comparison not only enhances the operators’ efficacy but also underscores their relevance in real-world decision-making scenarios.

Porous Core-Shell Yarn in Wearable Electrochemical Sensors for Real-Time Sweat Monitoring.

Wearable sweat sensors have garnered substantial attention owing to their actual significance in the noninvasive and real-time monitoring of health conditions. However, it remains significantly challenging to efficiently construct a high-sensitivity sweat sensor with stable long-term sensing capability. Herein, we report an effective methodology based on wet-spinning/acid-etching technology to construct a porous core-shell yarn-based wearable electrochemical sensor. This strategy increases the inductive surface area of the ion concentration and facilitates signal transmission. As a result, the sensor demonstrates high sensitivity for monitoring K+ and pH in sweat (54.89 mV/dec for K+ and 40.2 mV/pH for pH). Furthermore, the sensors exhibit outstanding sensing stability, good long-term stability (>16 h), and satisfactory bending resistance (>1000 cycles). More importantly, the sensing yarns could be prepared at speeds of up to 500 m/h with a continuous preparation strategy, which enabled mass fabrication of the electrochemical sensor. Electrochemical sensors could serve as sweat-sensing systems for real-time health monitoring and hold great potential for the commercialization of health-detection technology.

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption.

Using online negative emotions to predict risk-coping behaviors in the relocation of Beijing municipal government

This article explores the use of online negative emotions to predict public risk-coping behaviors during urban relocation. Through a literature review, the paper proposes hypotheses that anticipate advanced prediction of public risk-coping behaviors based on online negative emotions. The study’s empirical focus is on the relocation of the Beijing municipal government, using time series data for Granger causality analysis in EViews 10.0 software. Data on online negative emotions is sourced from Sina Weibo. After data cleaning, 1420 pieces of data related to the relocation policy of the Beijing Municipal Government within the period from June 9, 2015 to April 28, 2019 are retained. while risk-coping behaviors are measured through public information search behaviors and the incidence of violent crimes, the data coverage is also from June 9, 2015 to April 28, 2019. The results indicated that: (1) Online negative emotions regarding the relocation policy predict public risk-coping behaviors in advance. (2) Negative comments are more effective predictors than negative feelings; (3) Negative emotions about relocation policy formulation predict risk-coping behaviors better than those related to policy effectiveness and implementation; (4) Negative emotions from individuals better predict public risk-coping behaviors than those from institutions; (5) Negative emotions from key stakeholders better predict public risk-coping behaviors than those from non-key or marginal stakeholders. It is recommended that relevant departments establish a real-time monitoring system to track negative public opinions and emotions expressed online, adopt a stakeholder-centric approach to facilitate communication, and promote transparency and educational campaigns to address the challenges of urban relocation. In future studies, methods such as expanding the sample size and adding indicators will be used to address the limitations of potential bias in sample data.

Evaluation of noise pollution impact on health in Dhaka city, Bangladesh.

The purpose of this research was to look at the interrelation between adult health issues in Dhaka and noise pollution. The methodology involved a cross-sectional survey conducted in five different land use categories, with a sample size of 1,016 individuals. A validated questionnaire that focused on sources of perceived noise pollution and health issues related to noise was used to gather subjective data for the study. Objective noise pollution was evaluated using equivalent continuous sound pressure level (LAeq). Findings revealed noise generated from road traffic are the predominant source of noise pollution, with Thursday evenings during the end of office hours being the noisiest period in Dhaka. All areas in Dhaka exceeded permissible noise levels, posing significant health risks to residents and workers. The study identifies critical gaps in existing noise regulation policies and enforcement. Overall, this study underscores the urgent need for comprehensive noise pollution mitigation strategies, including innovative technologies, real-time monitoring systems, and public awareness campaigns. Further studies in diverse urban contexts are recommended to enhance the understanding of noise pollution's long-term impacts on vulnerable populations.

Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems.

Climate Change and Urban Flooding: Assessing Remote Sensing Data and Flood Modeling Techniques: A Comprehensive Review

This review critically examines the current understanding of climate change impacts on urban flooding, synthesizing recent findings to provide a comprehensive overview of how rising temperatures, sea-level rise, and increased extreme weather events influence urban floods. Additionally, it discusses various flood modeling techniques, outlining their advantages and disadvantages. Covering a broad spectrum of studies published between 2007 and 2024, selected for their relevance and methodology, this review highlights several key findings. Climate change significantly exacerbates urban flood disasters, with remote sensing emerging as an indispensable tool for climate change and flood monitoring and prediction. Despite these advancements, notable gaps remain in the literature. There is a conspicuous lack of long-term impact studies and limited discourse on the efficacy of existing mitigation strategies. Additionally, the review underscores the deficiency of real-time flood monitoring systems specifically designed for urban areas. These gaps highlight an urgent need for targeted research and the development of adaptive management practices to enhance flood prediction and management in urban settings. Future research must focus on advancing real-time monitoring systems, integrating remote sensing technologies more effectively, and developing comprehensive flood models. The review also emphasizes the importance of interdisciplinary approaches that merge hydrology, climatology, urban planning, and technology. By consolidating existing research, this review serves as a valuable resource for researchers and policymakers. It underscores the critical need for innovative flood modeling techniques and adaptive management strategies to tackle the challenges posed by climate change. This synthesis not only enriches the current body of knowledge, but also provides clear directions for future investigations, emphasizing the imperative for improved prediction, preparedness, and response mechanisms in urban flood management.

Utilization of Machine Learning and Explainable Artificial Intelligence (XAI) for Fault Prediction and Diagnosis in Wafer Transfer Robot

Faults in the wafer transfer robots (WTRs) used in semiconductor manufacturing processes can significantly affect productivity. This study defines high-risk components such as bearing motors, ball screws, timing belts, robot hands, and end effectors, and generates fault data for each component based on Fluke’s law. A stacking classifier was applied for fault prediction and severity classification, and logistic regression was used to identify fault components. Additionally, to analyze the frequency bands affecting each failed component and assess the severity of faults involving two mixed components, a hybrid explainable artificial intelligence (XAI) model combining Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) was employed to inform the user about the component causing the fault. This approach demonstrated a high prediction accuracy of 95%, and its integration into real-time monitoring systems is expected to reduce maintenance costs, decrease equipment downtime, and ultimately improve productivity.

Decision-Making Model for Life Cycle Management of Aircraft Components

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.