Real-time Machine Learning Research Articles

A real-time ghost machine learning model built on YOLOv8 for traffic road signs detection and classification in Germany

A real-time ghost machine learning model built on YOLOv8 for traffic road signs detection and classification in Germany

Advancing Precision Oncology with Digital and Virtual Twins: A Scoping Review

Digital twins (DTHs) and virtual twins (VTHs) in healthcare represent emerging technologies towards precision medicine, providing opportunities for patient-centric healthcare. Our scoping review aimed to map the current DTH and VTH technologies in oncology, summarize their technical solutions, and assess their credibility. A systematic search was conducted in the main bibliographic databases, identifying 441 records, of which 30 were included. The studies covered a wide range of cancers, including breast, lung, colorectal, and gastrointestinal malignancies, with DTH and VTH applications focusing on diagnosis, therapy, and monitoring. The results revealed heterogeneity in targeted topics, technical approaches, and outcomes. Most twining solutions use synthetic or limited real-world data, raising concerns regarding their reliability. Few studies have integrated real-time data and machine learning for predictive modeling. Technical challenges include data integration, scalability, and ethical considerations, such as data privacy and security. Moreover, the evidence lacks sufficient clinical validation, with only partial credibility in most cases. Our findings underscore the need for multidisciplinary collaboration among end-users and developers to address the technical and ethical challenges of DTH and VTH systems. Although promising for the future of personalized oncology, substantial steps are required to move beyond experimental frameworks and to achieve clinical implementation.

Big Data in Pharmacy: Transforming Patient Care with Analytics

The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.

Digital twinning of vertical centrifugal casting

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation.

“AgriVision” A Flutter -Based Application for RealTime Detection of Pesticides Residues on Fruits

“AgriVision” is a Flutter-based mobile application designed to address the critical issue of pesticide contamination on fruits. The application utilizes real-time image processing and machine learning techniques to detect pesticide residues on the surface of various fruits, ensuring food safety and healthstandards. By capturing and analyzing images through a smartphone camera, the app provides instant and reliablefeedback on the presence of harmful chemicals. The user-friendly interface, combined with the portability of the solution, offers farmers, consumers, and vendors a practical tool to assess fruit quality and promote safer agricultural practices. This project aims to contribute to healthier food choices and enhance consumer safety. Key Words: AgriVision , Flutter application , Real-time detection , Pesticide residues , Fruits , Agricultural technology , Pesticide detection , Mobile app , Food safety , AgriTech , Image processing , Machine learning , Smart agriculture , Fruit quality analysis.

Advanced Analysis of Scalability and Mobility in Cloud Servers using Logistic Regression Models.

With the rapid expansion of cloud computing, efficiently managing scalability and mobility has become paramount. This paper presents an in-depth examination of how logistic regression models can predict and optimize these two critical aspects of cloud infrastructure. By analyzing various cloud configurations, workload patterns, and system performance data, the logistic regression framework forecasts resource needs and identifies potential bottlenecks. The study incorporates real-world applications and case studies to demonstrate the practical benefits of logistic regression in enhancing cloud performance. Additionally, the paper compares logistic regression with other predictive models and explores future advancements in cloud optimization through AI-driven automation and real-time machine learning. The findings underscore the importance of predictive analytics in maintaining seamless cloud operations and provide actionable insights for cloud architects aiming to design more resilient and efficient cloud systems.

Semi-static conditions in low-latency C++ for high frequency trading: Better than branch prediction hints

Conditional branches pose a challenge for code optimisation, particularly in low latency settings. We present a novel language construct, referred to as a semi-static condition, which enables programmers to dynamically modify the direction of a branch at run-time by modifying the assembly code within the underlying executable. Subsequently, we explore scenarios where the use of semi-static conditions outperforms traditional conditional branching, highlighting their potential applications in real-time machine learning and high-frequency trading (HFT). Throughout the development process, key considerations of performance, portability, syntax, and security were taken into account.

FACTS: Present and Future

Flexible AC Transmission Systems (FACTS) are pivotal in modernizing power systems, enhancing their stability, controllability, and efficiency. Currently, FACTS devices such as Static VAR Compensators (SVCs), Static Synchronous Compensators (STATCOMs), and Unified Power Flow Controllers (UPFCs) are employed to manage power flow, mitigate system instabilities, and improve voltage regulation across transmission networks. These technologies address present challenges, including the integration of renewable energy sources, reduction of transmission losses, and enhancement of system reliability in the face of fluctuating power demands. However, the growing complexity of power grids, driven by the increasing penetration of intermittent renewable energy, electric vehicles, and distributed generation, necessitates advanced and scalable solutions. The future of FACTS lies in the development of more sophisticated, adaptive, and intelligent devices that leverage real-time data analytics, artificial intelligence, and machine learning to optimize power flow dynamically. Future advancements are expected to focus on enhancing the interoperability of FACTS with smart grid technologies, improving the resilience of power systems against cyber-physical threats, and facilitating the transition towards more decentralized and sustainable energy systems. Moreover, the integration of energy storage with FACTS devices could revolutionize their functionality, offering not only reactive power compensation but also energy balancing capabilities. This paper explores the current applications of FACTS and envisions their future role in addressing the evolving challenges of global power systems, emphasizing the importance of innovation and strategic investment in the ongoing transformation of electrical networks.

Developing Advanced Navigation Algorithms for Autonomous Vehicles to Enhance Safety and Efficiency in Urban Environments Using Real-Time Sensor Data and Machine Learning

Autonomous vehicles (AVs) have the ability to completely change how people get around cities. They could make transportation safer and more efficient while also reducing pollution and traffic. But their broad utilize depends on the creation of progressed direction frameworks that can dependably discover their way around complicated urban settings. An inventive way to illuminate this issue is portrayed in this think about. It employments real-time sensor information and machine learning to form and utilize cutting-edge direction strategies that work well in cities. There are five primary steps within the study process. To begin with, exhaustive information collection strategies are utilized to urge valuable sensor information from numerous places, such as GPS, LiDAR, webcams, and more. Another, progressed machine learning models are made and instructed with this information so that AVs can make smart choices and find their way around. These models are made to require into consideration things just like the environment, activity, how individuals walk, and other changing factors to form beyond any doubt they work well in complicated urban circumstances. A number of distinctive measures are utilized to judge the suggested algorithms, such as their security, adequacy, steadfastness, and capacity to alter. Re-enactment circumstances are utilized to test the strategies in a run of urban settings, from swarmed city zones to calm roads within the rural areas. When compared to conventional strategies, the comes about appear enormous picks up in following execution. The equations make it more secure and more productive to utilize them in cities. The modern strategies are too compared to current direction frameworks to appear how much way better they are at being precise, adaptable, and fast to reply. The research also finds possible problems and restrictions, like the need for real-time processing and complicated computations, and suggests ways to fix them.

The Role of Complex Systems in Predictive Analytics for E-Commerce Innovations in Business Management

This review explores the incorporation of complex systems theory into predictive analytics in the e-commerce sector, particularly emphasizing recent advancements in business management. By analyzing the intersection of these two domains, the review emphasizes the potential of complex systems models—including agent-based modeling and network theory—to improve the precision and efficacy of predictive analytics. It will provide a comprehensive overview of the applications of emergent predictive analytics techniques and tools, including real-time data analysis and machine learning, in inventory optimization, dynamic pricing, and personalization of customer experiences. In addition, this review will suggest future research directions to advance the discipline and address the technical, ethical, and practical challenges encountered during this integration phase.

Digital Engineering in Photonics: Optimizing Laser Processing

This article explores the transformative impact of digital engineering on photonic technologies, emphasizing advancements in laser processing through digital models, artificial intelligence (AI), and freeform optics. It presents a comprehensive review of how these technologies enhance efficiency, precision, and control in manufacturing processes. Digital models are pivotal for predicting and optimizing thermal effects in laser processing, thereby reducing material deformation and defects. The integration of AI further refines these models, improving productivity and quality in applications such as micromachining and cladding. Additionally, the combination of AI with freeform optics advances laser technology by enabling real-time adjustments and customizable beam profiles, which enhance processing versatility and reduce material damage. The use of digital twins is also examined as a key development in laser-based manufacturing, offering significant improvements in process optimization, defect reduction, and system efficiency. By incorporating real-time monitoring, machine learning, and physics-based modeling, digital twins facilitate precise simulations and predictions, leading to more effective and reliable manufacturing practices. Overall, the integration of digital twins, AI, and freeform optics into laser processing marks a significant progression in manufacturing technology. These advancements collectively enhance precision, efficiency, and adaptability, resulting in improved product quality and reduced operational costs. The continued evolution of these technologies is expected to drive further advancements in manufacturing practices, offering more robust solutions for complex production environments.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

Development of a Machine Learning Framework for Real-Time PMI (Post-Mortem Interval) Estimation in Field Forensics

Accurate estimation of the Post-Mortem Interval (PMI) is critical in forensic investigations, aiding in determining the time of death. However, traditional PMI estimation methods, often reliant on physiological observations and environmental factors, face significant limitations in accuracy and efficiency, especially in field conditions. This paper presents the development of a machine learning (ML) framework designed for real-time PMI estimation, integrating multimodal sensor data to address the challenges encountered in field forensics. Our framework utilizes environmental and physiological features, including body temperature, ambient humidity, and biochemical decomposition markers, to predict PMI with high precision. The ML model, trained on historical forensic data, is deployed on a real-time processing platform, enabling rapid analysis and decision-making in resource- constrained environments. The system is optimized for field operations, incorporating low-power hardware and edge computing capabilities to provide forensic investigators with reliable PMI estimates on-site. Through a series of controlled experiments simulating forensic scenarios, our framework demonstrates a significant improvement in PMI accuracy compared to traditional methods, while maintaining low latency for real-time applications. This research highlights the potential of machine learning to revolutionize forensic practices, offering a scalable and adaptive solution for time-sensitive investigations. Here are some relevant keywords for the development of a machine learning framework for real-time PMI (Post- Mortem Interval) estimation in field forensics: Keywords: Field Forensics, Real-Time Machine Learning, Body Decomposition Stages, Machine Learning in Forensic Science, Artificial Intelligence for PMI Analysis, Sensor Data in PMI Estimation, Deep Learning for PMI Estimation, Automated Forensic Analysis, Data Acquisition in Field Forensics.

Developing a Machine-Learning 'Smart' PCR Thermocycler, Part 1: Construction of a Theoretical Framework.

The use of PCR is widespread in biological fields. Some fields, such as forensic biology, push PCR to its limits as DNA profiling may be required in short timeframes, may be produced from minute amounts of starting material, and may be required to perform in the presence of inhibitory compounds. Due to the extreme high-throughput of samples using PCR in forensic science, any small improvement in the ability of PCR to address these challenges can have dramatic effects for the community. At least part of the improvement in PCR performance could potentially come by altering PCR cycling conditions. These alterations could be general, in that they are applied to all samples, or they could be tailored to individual samples for maximum targeted effect. Further to this, there may be the ability to respond in real time to the conditions of PCR for a sample and make cycling parameters change on the fly. Such a goal would require both a means to track the conditions of the PCR in real time, and the knowledge of how cycling parameters should be altered, given the current conditions. In Part 1 of our work, we carry out the theoretical groundwork for the ambitious goal of creating a smart PCR system that can respond appropriately to features within individual samples in real time. We approach this task using an open qPCR instrument to provide real-time feedback and machine learning to identify what a successful PCR 'looks like' at different stages of the process. We describe the fundamental steps to set up a real-time feedback system, devise a method of controlling PCR cycling conditions from cycle to cycle, and to develop a system of defining PCR goals, scoring the performance of the system towards achieving those goals. We then present three proof-of-concept studies that prove the feasibility of this overall method. In a later Part 2 of our work, we demonstrate the performance of the theory outlined in this paper on a large-scale PCR cycling condition alteration experiment. The aim is to utilise machine learning so that throughout the process of PCR automatic adjustments can be made to best alter cycling conditions towards a user-defined goal. The realisation of smart PCR systems will have large-scale ramifications for biological fields that utilise PCR.

Smart aquaculture analytics: Enhancing shrimp farming in Bangladesh through real-time IoT monitoring and predictive machine learning analysis

Water quality is a critical factor in shrimp farming, and the success of shrimp production is closely tied to the overall condition of the water. Challenges such as rapid population growth, environmental pollution, and global warming have led to a decline in fisheries production, particularly in the freshwater shrimp sector. This study addresses these challenges by monitoring multiple water parameters in shrimp farms, including pH, temperature, TDS, EC, and salinity. Traditional manual monitoring systems are known to be cumbersome, time-consuming, and lacking real-time capabilities. Consequently, a continuous and automated monitoring system becomes imperative for efficient and real-time metrics handling. This study introduces a real-time freshwater shrimp (locally named Galda, i.e., Macrobrachium Rosenbergii) farm monitoring system. The proposed system incorporates technologies such as microcontroller-based physical devices, IoT, cloud storage with service, machine learning models, and web applications. This integrated system enables users to remotely monitor shrimp farms and receive alerts when water parameters fall outside the optimal range. The physical implementation involves a set of sensors for collecting data on water metrics in shrimp farms. Regression analysis is employed for predicting next-day values, and a newly developed decision-based algorithm classifies shrimp production levels into low, medium, and maximum categories using six well-known classification algorithms. The system demonstrates a high success rate for next-day predictions (r2 of 0.94) by multiple linear regression, and the accuracy in classifying shrimp production is 97.84 % by Random Forest. Additionally, a ‘Smart Aquaculture Analytics’ web application has been developed, offering features such as real-time dashboards, historical data visualization, prediction and classification tools, and automated notifications to farmers in Bangladesh.

Looking beyond disruption to build agile and resilient supply chains for the future

The events of recent years, ranging from geopolitical tensions and global health events to labour shortages and economic crises, have highlighted significant vulnerabilities within global supply chains and emphasised the need for agility and resilience. Recent studies referenced in this paper, including the ‘Global Business Optimism Insights’ report, underscore that while large businesses tend to prioritise strengthening their supply chains, smaller businesses are often more focused on customer base expansion, potentially risking operational robustness. This approach could jeopardise customer satisfaction due to failures in meeting on-time, in-full (OTIF) delivery metrics, leading to potential financial penalties and customer dissatisfaction. With that in mind, this paper aims to help supply chain organisations understand how they can obtain the agility and resilience needed to weather ongoing disruption and futureproof their operations. It provides recommendations on how this can be achieved, pointing to access to trusted, real-time data across the supply chain network as a critical component. The paper also identifies, however, that many supply chain businesses struggle to obtain this access to data for rapid and informed decision making. Research from InterSystems finds that this data challenge is further complicated by a lack of end-to-end supply chain visibility, which is often exacerbated by data silos created by disparate systems and manual processes. These visibility challenges negatively affect key performance indicators such as OTIF metrics and restrict an organisation’s ability to adapt to disruptions swiftly. The paper explores how supply chain organisations can overcome these difficulties, identifying investment in digital supply chain technologies such as decision intelligence platforms as a way in which they can bridge the real-time execution gap and enable supply chain leaders to optimise processes. These platforms integrate real-time data analytics, artificial intelligence (AI) and machine learning (ML) to enhance visibility, decision making and operational efficiency. The paper draws the conclusion that building resilient, future-proof supply chains is crucial for organisations to manage disruptions and capitalise on opportunities effectively. In the wake of ongoing disruption, the paper aims to demonstrate that by adopting the right technological solutions and embracing a cultural shift to data-driven operations, supply chains can transform to better withstand future challenges and move their organisation forward, thus ensuring sustained growth and enhanced customer loyalty.

Detecting and Preventing ARP Spoofing Attacks Using Real-Time Data Analysis and Machine Learning

Detecting and Preventing ARP Spoofing Attacks Using Real-Time Data Analysis and Machine Learning

Methodology for assessing the impact of emergencies on the spread of infectious diseases

The spread of infectious diseases is significantly influenced by emergencies, particularly military conflicts, which disrupt healthcare systems and increase the risks of epidemics. The full-scale Russian invasion of Ukraine has exacerbated these challenges, causing environmental damage, mass displacement, and the breakdown of healthcare services, all of which contribute to the spread of infectious diseases. This study aims to develop a comprehensive methodology for assessing the impact of emergencies on the spread of infectious diseases, focusing on the full-scale invasion of Ukraine. The object of this study is to address epidemic threats posed by emergencies, particularly the increased spread of infectious diseases due to war-related disruptions. The subject of this study is methods and models of infectious disease transmission under conditions of emergencies, emphasizing the Russian full-scale invasion of Ukraine. The tasks of this study are to provide an analysis of the current state of research and develop a methodology for assessing the impact of emergencies on the spread of infectious diseases. The proposed methodology includes several key components. Comprehensive data from public health organizations includes infectious disease statistics, demographic shifts, healthcare disruptions, and environmental factors exacerbated by emergencies. Data preprocessing removes inconsistencies, standardization of formats, and normalization for population size differences. Machine learning models, including convolutional neural networks and recurrent neural networks, have been developed to simulate the spread of diseases based on demographic, environmental, and healthcare-related variables. Deep learning models analyze spatial and temporal patterns, whereas compartmental models such as SIR estimate changes in reproductive numbers (R₀ and Re). Additionally, models of excess mortality incorporate mixed effects to account for regional and time-based variations. The methodology incorporates real-time monitoring of epidemic threats using real-time data from multiple sources, enabling dynamic assessments of disease spread and facilitating predictive modeling. The models were trained on historical data and validated using cross-validation techniques to ensure robustness and reliability, with a specific focus on the pre- and post-invasion phases in Ukraine.Results: The study provides a comprehensive framework for collecting and processing data on infectious diseases and epidemic threats in emergencies. The proposed model introduces advanced machine learning and epidemiological models trained on pre- and post-invasion data to analyze disease transmission patterns and forecast future epidemic dynamics. Conclusion: The proposed methodology addresses current gaps in infectious disease during emergencies by integrating real-time data and machine learning techniques. This research improves decision-making in public health management and biosafety during crises, particularly in war-affected regions like Ukraine.

Overview of the KSTAR experiments toward fusion reactor

Abstract The KSTAR has been focused on exploring the key physics and engineering issues for future fusion reactors by demonstrating the long pulse operation of high beta steady-state discharge. Advanced scenarios are being developed with the goal for steady-state operation, and significant progress has been made in high ℓi, hybrid and high beta scenarios with βN of 3. In the new operation scenario called FIRE, fast ions play an essential role in confinement enhancement. GK simulations show a significant reduction of the thermal energy flux when the thermal ion fraction decreases and the main ion density gradient is reversed by the fast ions in FIRE mode. Optimization of 3D magnetic field techniques, including adaptive control and real-time machine learning (ML) control algorithm, enabled long-pulse operation and high-performance ELM-suppressed discharge. Symmetric multiple shattered pellet injections and real-time DECAF are being performed to mitigate and avoid the disruptions associated with high-performance, long-pulse ITER-like scenarios. Finally, the near-term research plan will be addressed with the actively cooled tungsten divertor, a major upgrade of the NBI and helicon current drive heating, and transition to a full metallic wall.

A Review On Revolutionizing Waste Water Collection and Recycling Processes with IoT

This paper provides an extensive review of IoT-driven solutions addressing critical challenges in sectors like wastewater treatment, agriculture, waste management, e-waste recycling, and water quality monitoring. Innovative models leverage IoT technology for real-time monitoring, control, and optimization. Examples include an industrial IoT cloud-based model for wastewater treatment, sensor integration in aquaculture systems for improved resource management, and intelligent waste collection systems in smart cities. The review identifies gaps and opportunities for advancements, highlighting the importance of real-time data collection, cloud-based analytics, and machine learning for informed decision- making in environmental and resource management