Real-time System Research Articles

Deep learning-based video surveillance system for suspicious activity detection

The need for reliable video surveillance systems to detect and prevent suspicious activities has become more important with the increase in crime and security threats. This paper proposes a real-time video surveillance system based on the Long-term Recurrent Convolutional Network (LRCN) model, which can automatically detect and alert the authority about suspicious activities, such as fighting, accidents, and robbery. Our system comprises two main components: LRCN-based activity recognition and real-time alert generation. We evaluated the performance of the proposed system on a custom dataset compiled from two publicly available datasets and achieved state-of-the-art results in terms of accuracy, precision, and recall. Our results demonstrate the effectiveness and scalability of the LRCN-based video surveillance system for real-time suspicious activity detection. We believe that our proposed system can be deployed in various public places, such as airports, train stations, and shopping malls, to enhance the security and safety of the public.

Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 kHz. This interface provides real-time data visualization and communication with the ML server, creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.

Integration of a Paper‐Based Supercapacitor and Flexible Perovskite Mini‐Module: Toward Self‐Powered Portable and Wearable Electronics

AbstractAn all‐flexible self‐powering unit, combining a flexible perovskite solar mini‐module for energy conversion and ultra‐flexible screen‐printed interdigitated carbon supercapacitors on paper for energy storage, is designed and developed. Through a hybrid bifurcated structure, the supercapacitors are connected in series and neatly layered on a paper substrate with the perovskite solar mini‐module strategically placed on top for seamless integration. The photosupercapacitor quickly reaches saturated voltage under various light intensities (1 sun, 1000 lx, 500 lx, and 200 lx) and displays a self‐discharge time of over 2 minutes. It delivers peak overall and storage efficiencies of 2.8% and 23%, respectively, and exhibits an extensive potential window of 3.8 V, making it a prominent choice for real time applications in electronic systems. In a nutshell, the inherent flexibility of both the solar module and the storage system, coupled with the space‐saving design, and the extensive potential window of the hybrid photosupercapacitor paves the way for implementation in portable and wearable electronics operating in indoor environments, ushering a new era of more versatile and adaptable energy solutions.

On formal modeling, analysis and optimization of reconfigurable manufacturing systems

ABSTRACT This paper deals with the formal specification and multi-objective optimization of reconfigurable manufacturing systems (RMSs). RMSs are characterized by their dynamic structure, crucial for responding to functional variations, adjusting production capacity and overcoming operational dysfuntions promptly and cos-effectively. The effective utilization of this dynamic capability depends on thorough study and optimization of RMS configurations. To tackle this challenge, a novel formalism called genetic reconfigurable object nets (Gen-RONs) is introduced. Gen-RONs formalism integrates the modeling, verification and reconfiguration capabilities of reconfigurable object nets with advanced multi-objective techniques from genetic algorithms. Within Gen-RONs, Petri nets serve to: (i) encode RMS configurations, (ii) manage RMS scheduling and (iii) employ graph transformation theory rules as genetic algorithm operators for real-time system reconfiguration. Thus, Gen-RONs formalism provides a robust framework for optimizing RMSs using genetic algorithms. To ensure deadlock-free RMS configurations, Gen-RONs incorporate a binary tree-based technique. Furthermore, Taguchi’s method is employed to fine-tune genetic algorithm parameters. The contribution of this paper is demonstrated through a case study involving an RMS and the widely used genetic algorithm NSGA-II. Significant gains in terms of the number of solutions (i.e. configurations) and the criteria values are achieved.

Intraoperative navigation in craniofacial surgery.

Craniofacial surgery requires comprehensive anatomical knowledge of the head and neck to ensure patient safety and surgical precision. Over recent decades, there have been significant advancements in imaging techniques and the development of real-time surgical navigation systems. Intraoperative navigation technology aligns surgical instruments with imaging-derived information on patient anatomy, enabling surgeons to closely follow preoperative plans. This system functions as a radiologic map, improving the accuracy of instrument placement and minimizing surgical complications. The introduction of first-generation navigation systems in the early 1990s revolutionized surgical procedures by enabling real-time tracking of instruments using preoperative imaging. Initially utilized in neurosurgery, intraoperative navigation has since become standard practice in otolaryngology, cranio-maxillofacial surgery, and orthopedics. Since the 2000s, second-generation navigation systems have been developed to meet the growing demand for precision across various surgical specialties. The adoption of these systems in craniofacial surgery has been slower, but their use is increasing, particularly in procedures such as foreign body removal, facial bone fracture reconstruction, tumor resection, and craniofacial reconstruction and implantation. In Korea, insurance coverage for navigation in craniofacial surgery began in 2021, and new medical technologies for orbital wall fracture treatment were approved in August 2022. These technologies have only recently become clinically available, but are expected to play an increasingly important role in craniofacial surgery. Intraoperative navigation enhances operative insight, improves target localization, and increases surgical safety. Although these systems have a steep learning curve and initially prolong surgery, efficiency improves with experience. Calibration issues, registration errors, and soft tissue deformation can introduce inaccuracies. Nonetheless, navigation technology is evolving, and the integration of intraoperative computed tomography data holds promise for further enhancements of surgical accuracy. This paper discusses the various types and applications of navigation employed in craniofacial surgery, highlighting their benefits and limitations.

Proposal of UAV-SLAM-Based 3D Point Cloud Map Generation Method for Orchards Measurements

This paper proposes a method for generating highly accurate point cloud maps of orchards using an unmanned aerial vehicle (UAV) equipped with light detection and ranging (LiDAR). The point cloud captured by the UAV-LiDAR was converted to a geographic coordinate system using a global navigation satellite system / inertial measurement unit (GNSS/IMU). The converted point cloud is then aligned with the simultaneous localization and mapping (SLAM) technique. As a result, a 3D model of an orchard is generated in a low-cost and easy-to-use manner for pesticide application with precision. The method of direct point cloud alignment with real-time kinematic-global navigation satellite system (RTK-GNSS) had a root mean square error (RMSE) of 42 cm between the predicted and true crop height values, primarily due to the effects of GNSS multipath and vibration of automated vehicles. Contrastingly, our method demonstrated better results, with RMSE of 5.43 cm and 2.14 cm in the vertical and horizontal axes, respectively. The proposed method for predicting crop location successfully achieved the required accuracy of less than 1 m with errors not exceeding 30 cm in the geographic coordinate system.

Wearable Online Freezing of Gait Detection and Cueing System.

This paper presents a real-time wearable system designed to assist Parkinson's disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide 'on demand' vibratory stimulation to patients. This paper examines the system's ability to operate with minimal latency, achieving an average detection delay of just 261 milliseconds and a freezing of gait detection accuracy of 95.1%. While patients received on-demand stimulation, the system's effectiveness was assessed by decreasing the average duration of freezing of gait episodes by 45%. These preliminarily results underscore the potential of personalized, real-time feedback systems in enhancing the quality of life and rehabilitation outcomes for patients with movement disorders.

Overview of Artificial Neural Network-Based Solution for Ordinary and Partial Differential Equations by Feed Forward Method Using Python

In the modern era, models such as Neural Networks are increasingly used to deal with ordinary and partial differential equations (ODEs-PDEs) because of their related complexity. The main aim of the paper is to focus on Artificial Neural Networks as a method to solve these equations since it is possible to approximate complex nonlinear relationships with great accuracy and adaptively reduce computational costs, thus enhancing the accuracy of solutions within highly dimensional and nonlinear problems. The traditional methods are not applicable to partial differential equations due to the problem of nonlinearity and high dimensionality. However, the Feed Forward method implemented in Python is quite a powerful alternative that can easily approximate complicated functions and very well have a hold on nonlinear equations. This research paper contains differential equations as nonlinear and linear equations and Feed Forward neural network models for both solutions of ODEs and PDEs. Python enables the integration of AI-based algorithms for the enhancement of more accurate and efficient results. This work compares the existing solutions based on ANNs and further highlights the possible advantages of using ANNs in ordinary as well as complex differential equations problems. The results indicate that ANNs have immense potential for further developing computational algorithms applicable to solving ODEs and PDEs, particularly in real-time applications that demand high precision and adaptability.

Systematic Review of Quantitative Risk Quantification Methods in Construction Accidents

Construction accidents pose significant risks to workers and the public, affecting industry productivity and reputation. While several reviews have discussed risk assessment methods, recent advancements in artificial intelligence (AI), big data analytics, and real-time decision support systems have created a need for an updated synthesis of the quantitative methodologies applied in construction safety. This study systematically reviews the literature from the past decade, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough search identified studies utilizing statistical analysis, mathematical modeling, simulation, and artificial intelligence (AI). These methods were categorized and analyzed based on their effectiveness and limitations. Statistical approaches, such as correlation analysis, examined relationships between variables, while mathematical models, like factor analysis, quantified risk factors. Simulation methods, such as Monte Carlo simulations, explored risk dynamics and AI techniques, including machine learning, enhanced predictive modeling, and decision making in construction safety. This review highlighted the strengths of handling large datasets and improving accuracy, but also noted challenges like data quality and methodological limitations. Future research directions are suggested to address these gaps. This study contributes to construction safety management by offering an overview of best practices and opportunities for advancing quantitative risk assessment methodologies.

CONTEMPORARY CHALLENGES IN CIVIL PROTECTION AND DEFENSE

The addressed problem is the need to enhance public policies and regulations in disaster management in Brazil, due to the increasing frequency and severity of these events. The hypothesis posits that integrating public policies with emerging technologies and community participation can significantly improve disaster management. The methodology involves a literature review of existing legislation, case study analysis, and the implementation of real-time hydrometeorological monitoring systems. Key results indicate that, despite advancements in prevention and mitigation policies, significant gaps remain in inter-institutional coordination and practical application of regulations. The main conclusion is that effective integration of public policies and emerging technologies is essential for risk reduction and the effective protection of vulnerable communities.

A Portable Electronic Nose Coupled with Deep Learning for Enhanced Detection and Differentiation of Local Thai Craft Spirits

In this study, our primary focus is the biomimetic design and rigorous evaluation of an economically viable and portable ‘e-nose’ system, tailored for the precise detection of a broad range of volatile organic compounds (VOCs) in local Thai craft spirits. This e-nose system is innovatively equipped with cost-efficient metal oxide gas sensors and a temperature/humidity sensor, ensuring comprehensive and accurate sensing. A custom-designed real-time data acquisition system is integrated, featuring gas flow control, humidity filters, dual sensing/reference chambers, an analog-to-digital converter, and seamless data integration with a laptop. Deep learning, utilizing a multilayer perceptron (MLP), is employed to achieve highly effective classification of local Thai craft spirits, demonstrated by a perfect classification accuracy of 100% in experimental studies. This work underscores the significant potential of biomimetic principles in advancing cost-effective, portable, and analytically precise e-nose systems, offering valuable insights into future applications of advanced gas sensor technology in food, biomedical, and environmental monitoring and safety.

Inquiring the robot operating system community on the state of adoption of the ROS 2 robotics middleware

AbstractThe robot operating system (ROS) is a well-established and popular open-source robotics middleware used for rapid prototyping of robotic applications. However, ROS has several known weaknesses, such as lack of support for real-time systems and limitations in fully distributed deployments with multiple robots. To address these weaknesses, ROS underwent a major update and the first version of ROS 2 was released back in 2015, being lingeringly adopted by the community in recent years. Yet, long-term support for ROS 1 will come to an end on May 2025. As such, it is more important than ever to analyze and explore the features of ROS 2, and understand its technological readiness. This work focuses on the state of adoption of ROS 2. Specifically, the article presents a user experience questionnaire targeting the ROS community to understand the more eminent needs with respect to ROS 2, determine the current levels of adoption, and identify what is holding the community back from migrating their ROS 1 applications to ROS 2. Results with more than 100 participants from around the world show that the community is aware of ROS 2 and the middleware is addressing most user expectations. We unveil the main reasons given for not migrating to ROS 2, further understand the community’s views on ROS 2, and identify what is missing to anticipate ROS 1 users migrating to ROS 2 and make ROS 2 widely adopted. Moreover, we gather important impressions on the appropriateness of ROS 2 for multi-robot system use cases.

ExtendinG SUb-DAily River Discharge data over INdia (GUARDIAN).

River discharge information is crucial for various applications, but the measurement process often remains impeded by factors that hinder near real-time (NRT) data availability in India. However, leveraging telemetry-based water surface elevation (WSE) data across the country provides an opportunity to convert it into river discharge. This conversion is made possible by utilizing rating curves (RCs) derived from historical collocated measurements of WSE and discharge. In this study, NRT WSE from the Central Water Commission (CWC) flood portal is obtained via the web-scraping tool. Through the application of RCs, discharge data is extended across 210 gauging stations in India from the year 2020 to the present, encompassing sub-daily discharge during the non-monsoon and hourly discharge series during monsoon (June-September) across the Indian rivers. Annually, the study generated over 800,000 discharge data points for Indian rivers, accounting for more than 4000 discharge measurements per station annually. These comprehensive datasets provide valuable insights for water resource and flood management research, offering NRT access to WSE, and discharge, along with the local RCs.

The Relationship Between MOTS-c K14Q Polymorphism and Sarcopenia, Blood Lipids, and Mental Health in Older Korean Adults.

Background/objectives: An East Asian-specific 1382A>C polymorphism in the mitochondrial open reading frame of the 12S rRNA type-c results in an amino acid substitution from Lys (K) to Gln (Q) at the 14th amino acid residue. This study investigated the association between m.1382A>C polymorphism and sarcopenia, blood lipids, and mental health in older Korean adults. Methods: The study included 683 community-dwelling Korean adults (345 men and 338 women) aged 65 years and older. The m.1382A>C polymorphism was genotyped with a 7500 real-time PCR system. Handgrip strength (HGS) was measured, and appendicular skeletal muscle (ASM) mass was calculated. Demographics, blood lipids, falling risk, nutritional intake, cognition function, and depression were additionally measured. Results: Men carrying the C allele had significantly higher ASM (21.6 ± 3.0 vs. 19.5 ± 2.2 kg, p = 0.018), ASM/height2 (7.76 ± 0.76 vs. 7.14 ± 0.62 kg/m2, p = 0.012), lean mass (53.3 ± 6.2 vs. 46.5 ± 4.0 kg, p < 0.001), left HGS (33.3 ± 5.0 vs. 28.9 ± 4.0 kg, p = 0.010), and right HGS (35.6 ± 5.3 vs. 30.9 ± 4.3 kg, p = 0.009) than men carrying the A allele. The genotype differences in ASM (p = 0.017), ASM/height2 (p = 0.011), lean mass (p < 0.001), left HGS (p = 0.010), and right HGS (p = 0.009) remained significant even after adjusting for all measured covariates. By contrast, no significant differences in other measured parameters were found between women carrying the A and C alleles. Conclusions: Our study findings indicate that the m.1382A>C polymorphism may be used as a genetic biomarker of age-related sarcopenia in older Korean men.

Data Stream Protocols for Real-Time Messaging : Optimizing Performance and Reliability

This article explores the design and implementation of data stream protocols for real-time messaging, focusing on key aspects that contribute to their performance and reliability. It examines protocol architectures, data formatting and serialization techniques, error handling mechanisms, and quality of service features. The article compares popular protocols such as WebSocket and MQTT, discusses performance optimization techniques, and addresses security considerations. Emerging trends like WebTransport, HTTP/3, and edge computing integration are also explored, highlighting their potential to reshape the landscape of real-time messaging. By providing a comprehensive overview of current practices and future directions, this article aims to equip developers and system architects with the knowledge needed to design efficient and reliable real-time communication systems.

The function of p97/valosin-containing protein (VCP) and small VCP-interacting protein (SVIP) in invasion and migration of pancreatic cancer cells.

Misfolded proteins are eliminated by a process known as endoplasmic reticulum-associated protein degradation (ERAD). ERAD has an impact on a variety of illnesses, such as diabetes, cystic fibrosis, cancer, and neurological conditions. As one of the many proteins involved in ERAD, this study is focused on p97/valosin-containing protein (VCP) and small VCP-interacting protein (SVIP). The existence and function of SVIP and p97/VCP in various types of pancreatic cancer have not yet been investigated. The study's objectives are to examine the expressions of SVIP and p97/VCP in two pancreatic cancer types and to show whether these proteins aid in the invasion and migration of pancreatic cancer cells. In this work, MIA PaCa-2 and PANC-1 human cell lines were examined. Immunocytochemistry and immunofluorescence were performed to detect the cellular localization and presence of p97/VCP and SVIP in pancreatic cancer cells. Following p97/VCP siRNA and SVIP siRNA transfection of the cells, protein expressions were assessed using Western blot analysis. The effects of this suppression on cell invasion and migration were determined using the xCELLigence real-time analysis system (RTAC). In the nucleus and cytoplasm of MIA PaCa-2 and PANC-1 cells, p97/VCP and SVIP immunoexpressions were seen. The decrease in protein expressions of p97/VCPsi and SVIPsi was significant in pancreatic cells compared to the controlsi. The p97/VCP siRNA transfection reduced the invasion and migration of MIA PaCa-2 and PANC-1 cells. In addition, the SVIP siRNA suppression resulted in increasing the invasion and migration ability of both cells. This study also demonstrated, for the first time, SVIP expression in MIA PaCa-2 and PANC-1 cells. Overall, the findings show the differential expression and function of p97/VCP and SVIP in pancreas ductal adenocarcinoma cells. The potential of the pancreatic cancer cells to migrate and invade altered when the two cell lines were transfected with p97/VCPsi and SVIPsi.

XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles

The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances real-time communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100 %, precision of 100 %, recall of 100 %, F1-score of 100 %, and Matthew Correlation Coefficient of 100 % in binary-class situations for internal vehicular (CAN) networks and achieved 99.88 % accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power.

EvPowerStop - Electric Vehicle Station Finder and Slot Scheduler

With the increasing adoption of electric vehicles (EVs) worldwide, a significant challenge has emerged for EV drivers: locating compatible charging stations in real-time and securing slots for efficient use. The transition to electric mobility has created a demand for seamless infrastructure that supports the dynamic needs of EV drivers. Currently, the lack of centralized information on charging station availability and compatibility with specific vehicles leads to inconveniences and delays, reducing the overall efficiency of electric vehicle usage. This project addresses this gap by developing an EV station finder and slot scheduling application—EV Power Stop—designed to simplify the charging process and ensure that EV drivers can plan their trips with confidence. The broader context of this work lies in the realm of smart cities and sustainable transportation. As nations push toward carbon-neutral goals, the role of electric vehicles becomes crucial in reducing emissions and lowering dependency on fossil fuels. For this shift to be effective, smart solutions that integrate transportation with infrastructure are vital. This project aligns with the goals of smart urban mobility by offering a system that leverages real-time data to provide EV drivers with the most up-to-date information on charging station availability. The proposed system uses Flutter as a cross-platform framework for the frontend and Firebase as the backend for managing user data, station information, and slot booking in real-time. APIs like Google Maps are integrated to offer navigational support, ensuring users can easily find and access nearby stations. Key features include user authentication, slot booking, real-time updates on availability, and route guidance. The system is designed with scalability, performance, and security in mind, ensuring that it can handle a growing user base while maintaining quick response times. Initial testing shows that the app responds within 2 seconds for most user interactions, meets usability standards, and securely manages personal data. Future developments will focus on integrating additional functionalities like iOS support, predictive scheduling, and automated slot management to improve efficiency further. This project demonstrates the potential for integrating real-time systems with urban mobility to enhance the electric vehicle user experience and support broader sustainability goals..

Current Applications of Raman Spectroscopy in Intraoperative Neurosurgery.

Neurosurgery demands exceptional precision due to the brain's complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon's ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes. Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications. Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates. This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS's clinical utility and cost-effectiveness.

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data

PurposeSpontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any surgical conditions or procedures, and is a common complication of cirrhosis. With a potential mortality rate of 40 ​%, accurate diagnosis and prompt initiation of appropriate antibiotic therapy are crucial for optimizing patient outcomes and preventing life-threatening complications. This study aimed to expand the use of computational models to improve the diagnostic accuracy of SBP in cirrhotic patients by incorporating a broader range of data, including clinical variables and laboratory values. Patients and methodsWe employed 5 machine learning classification methods - Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Random Forest, utilizing a variety of demographic, clinical, and laboratory features and biomarkers. ResultsAscitic fluid markers, including white blood cell (WBC) count, lactate dehydrogenase (LDH), total protein, and polymorphonuclear cells (PMN), significantly differentiated between SBP and non-SBP patients. The Random Forest model demonstrated the highest overall accuracy at 86 ​%, while the Naive Bayes model achieved the highest sensitivity at 72 ​%. Utilizing 10 key features instead of the full feature set improved model performance, notably enhancing specificity and accuracy. ConclusionOur analysis highlights the potential of machine learning to enhance the accuracy of SBP diagnosis in cirrhotic patients. Integrating these models into clinical workflows could substantially improve patient outcomes. To achieve this, ongoing multidisciplinary research is crucial. Ensuring model interpretability, continuous monitoring, and rigorous validation will be essential for the successful implementation of real-time clinical decision support systems.