The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and d...
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The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and distributed nature of IoMT systems exposes them to significant cybersecurity threats, including data breaches and unauthorized access. This study aims to develop a secure and scalable framework to safeguard IoMT environments using blockchain technology, federated learning, and dynamic consensus algorithms. The proposed approach integrates artificial intelligence to detect threats and employs federated learning to maintain patient privacy while training models collaboratively. Experimental evaluation of the framework shows improved data security, reduced response times, and enhanced system trustworthiness. The results suggest that the integration of blockchain and AI-driven mechanisms offers a robust solution to IoMT cybersecurity challenges, paving the way for more reliable and efficient healthcare systems.
The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the syst...
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Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models a...
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Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built on the assumption of a static learning environment, but in practical situations, the data generated by the process is dynamic. This evolution of the data is termed concept drift. This research paper presents an approach for predicting mechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment. The method proposed here is applicable to all mechanical devices that are susceptible to failure or operational degradation. The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation. The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature. It is observed that, in the settings without concept drift in the data, methods like SVM and Random Forest performed better compared to deep neural networks. However, this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift. In this perspective, DNN generated the stable drift detection method; it reported an accuracy of 84 % and an AUC of 0.87 while detecting only a single drift point, indicating the stability to perform better in detecting and adapting to new data in the drifting environments under industrial measurement settings.
Restaurant selection by a consumer is a challenging task. The main problem is the proper optimization of ratings by food bloggers and the cost of food in the restaurant. In this paper, we propose an approach for a res...
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Stimulated Raman scattering (SRS) microscopy is regarded as a powerful technique of vibrational spectroscopic imaging. Nevertheless, it is still challenging to discriminate various organelles only with spectroscopic s...
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Image reconstruction is at the core of improving Computed Tomography (CT), enabling high-quality imaging while addressing challenges like noise, limited data, and radiation exposure. Recent innovations have introduced...
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ISBN:
(数字)9798331520762
ISBN:
(纸本)9798331520779
Image reconstruction is at the core of improving Computed Tomography (CT), enabling high-quality imaging while addressing challenges like noise, limited data, and radiation exposure. Recent innovations have introduced adaptive algorithms that enhance reconstruction efficiency, clarity, and safety, making CT more effective in diverse applications. The Parameter-extended Expectation-Maximization (PXEM) algorithm dynamically adjusts reconstruction parameters during iterations, demonstrating superior noise resilience and faster convergence, especially in high-noise environments. Similarly, the Simultaneous Algebraic Reconstruction Technique with Guided Filtering (SART-G) uses prior guidance images to retain structural details and outperform traditional methods, such as Filtered Back Projection (FBP) and standard SART, in low-exposure and limited-projection scenarios. Muon Computed Tomography (MCT) presents a novel, non-invasive approach that utilizes cosmic-ray muons for imaging dense and complex materials. MCT has significant potential in nuclear safety and industrial applications where conventional CT faces limitations. Meanwhile, the Simultaneous Algebraic Reconstruction Technique with Total Variation (SART-TV) mitigates noise accumulation in sparse-angle imaging by applying total variation regularization, ensuring enhanced image quality while minimizing radiation exposure. These advancements illustrate the transformative impact of modern image reconstruction algorithms on CT technology. By integrating adaptive noise reduction, efficient data utilization, and safety-oriented approaches, these methods pave the way for more precise and versatile CT applications in clinical, research, and industrial settings. This article presents an overview of some of the CT reconstruction iterative techniques that are commonly used in CT reconstruction from limited views.
This paper describes the development of an anomaly detection system for players in a rugby match to provide appropriate aid and avoiding severe injury. In the developed system, deep neural networks with fine tuning an...
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We introduce a new task, Open-set Mixed Domain Adaptation (OSMDA), which considers the potential mixture of multiple distributions in the target domains, thereby better simulating real-world scenarios. To tackle the s...
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Named Entity Recognition (NER), sometimes also referred to as Entity Extraction has been an integral part of Natural Language Understanding systems. To date, there have been continuous myriad efforts in linguistics an...
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