Asset management deals with the process of identifying, monitoring, controlling, and supervising the assets for reliable, stable, and uninterrupted operation of the smart grid. Installing and distributing asset manage...
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Urbanization has risen the levels of economic, social, and political changes that have led to various serious socioeconomic problems. The unplanned growth of population and development for meeting the needs of the soc...
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In order to enhance patient care and medical decision-making, smart healthcare services are progressively utilizing cutting-edge technologies. Deep learning (DL), one of these technologies, has become a potent instrum...
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ISBN:
(数字)9798331523893
ISBN:
(纸本)9798331523909
In order to enhance patient care and medical decision-making, smart healthcare services are progressively utilizing cutting-edge technologies. Deep learning (DL), one of these technologies, has become a potent instrument for evaluating enormous volumes of medical data, allowing for improved clinical procedures and more precise diagnosis. This study focuses on enhancing smart healthcare services through deep learning-based medical data analysis, with a particular emphasis on Named Entity Recognition (NER) and Relation Extraction (RE) tasks from clinical records. The study is implemented using Python software on the MIMIC-III dataset, utilizing an optimized Particle Swarm Optimization (PSO)-BioBERT model. The model achieved remarkable performance in both NER and RE tasks, with accuracy rates of 99.2% for NER and 99.5% for RE, and F1-scores of 98.1% and 98.8%, respectively. Compared to traditional models like Bi-LSTM and BERT, the PSO-BioBERT model significantly outperformed them, with the proposed model exceeding Bi-LSTM by 6.2% in accuracy and BERT by 12.12%. The findings highlight that the PSO-BioBERT model provides substantial improvements in extracting and classifying medical entities and their relationships from clinical data. These developments revolutionize smart healthcare services by making it possible to analyze unstructured medical information more accurately and consistently. The study emphasizes the effectiveness of deep learning models, particularly PSO-BioBERT, in improving data-driven decision-making in healthcare, which could ultimately enhance patient care outcomes and healthcare system efficiency. Future work could focus on integrating these models with real-time clinical systems for broader applications, including predictive analytics and personalized treatment plans. This approach holds promise for revolutionizing how healthcare data is utilized to improve overall patient care.
Our paper gives first answers on a fundamental question: How can the design of architectures of intelligent digital systems and services be accomplished methodologically? intelligent systems and services are the goals...
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In the framework of the emerging smart ElectroMagnetic Environment (SEME) paradigm, this work presents a first experimental proof of the feasibility and effectiveness of passive and static electromagnetic skins (PSSs)...
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The proceedings contain 41 papers. The special focus in this conference is on IoT and its Applications. The topics include: Industrial IoT: Development of smart Cooler for Solder Paste Storage and Management;applicati...
ISBN:
(纸本)9789811676369
The proceedings contain 41 papers. The special focus in this conference is on IoT and its Applications. The topics include: Industrial IoT: Development of smart Cooler for Solder Paste Storage and Management;applications and Challenges in Internet of Vehicles: A Survey;effective View of Swimming Pool Using Autodesk 3ds Max: 3D Modelling and Rendering;secure Outsourcing of Image Editing Based on Homomorphic Encryption;survey on Botnet Detection Techniques;security of Cloud computing Using Quantum Zero-Knowledge Proof System;intrusion Detection System Performance Comparison Using Dimensionality Reduction Techniques;A Compact ZOR Antenna with Defective Ground for Wireless data Transmission and Short-Range Radar Applications;performance Analysis of Consensus-Based Time Synchronization Algorithms for Wireless Sensor Network;analysis of Energy Harvesting Techniques for Wireless Sensor Networks Deployment Scenarios;a General data Retrieval Technique in Remote Healthcare Application;Malaria Detection Using VGG19 and Deep Convolutional Neural Network;brain Tumor Detection: A Review of Early Stage Tumor Detection Techniques;supervised Machine Learning Approaches for Attack Detection in the IoT Network;Feature Extraction and Classification of ECG Signals Through Dimension Reduction;regular Self-Health Monitoring and Medicine Reminder System with Emergency Alert Messaging Using IoT;a Comparative Analysis of Network Intrusion Detection System for IoT Using Machine Learning;Dissected Scene Character Recognition Using HOG Descriptors;students Performance Prediction Using Educational data Mining;design of intelligent Transportation System for smart City;container-Based Lab-as-a-Service Application;an IoT-Based smart Garbage Segregation System Using Deep Learning;blockchain-Based Access Control Model for IoT Applications;implementation of IoT-Based smart Healthcare Monitoring System;preface.
High speed communication and data movement is the key for mobile technologies and systems evolutions, which requires broadband communications and intelligent networking. The 5G mobile infrastructure enables developmen...
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ISBN:
(纸本)9781665428071
High speed communication and data movement is the key for mobile technologies and systems evolutions, which requires broadband communications and intelligent networking. The 5G mobile infrastructure enables development of many mobile technologies to fully use new technologies such as AI, mobile edge computing, M2M and dataanalytics. 5G/IMT-2020 will provide new applications and services for both developed and developed countries. Applications such as smart transport systems, e-health, education, smart grid, agriculture and disaster relief. In this paper we simulate and evaluate the mobile system related to its mobile application by using suitable queuing models to provide best performance based on its QoS.
The high incidence of chronic non-communicable diseases (NCDs) places a significant financial strain on public health systems. Brazil is projected to see a rise in mortality rates caused by non-communicable diseases (...
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ISBN:
(数字)9798331508432
ISBN:
(纸本)9798331508449
The high incidence of chronic non-communicable diseases (NCDs) places a significant financial strain on public health systems. Brazil is projected to see a rise in mortality rates caused by non-communicable diseases (NCDs) by 2030. These include cancer, cardiovascular disease, respiratory illness, and diabetes. The unique challenge of managing chronic and integrated care for NCDs is well-documented. This study presents an AI-based framework for managing chronic diseases and providing digital health services globally. The suggested platform is constructed and supported by healthcare 4.0 technologies, which enable the implementation of a smart healthcare system. Among these innovations are AI-enabled cloud solutions, the internet of medical things, and wearable technology. Also included in the publication is a case study of diabetes prediction that demonstrates the platform's viability. An initial dataset was created for the research case with many attributes, including bio-impedance, skin impedance, skin temperature, pulse rate, and oxygen concentration. In this study, we investigate how the IoT and Artificial Intelligence (AI) are changing the face of illness detection in connected healthcare systems. AI has quickly become an indispensable tool in the healthcare industry, providing advanced algorithms for evaluating medical data and facilitating forecasting and decision-making. The internet of things (IoT) improves upon this by allowing web-enabled devices, such as implanted sensors and wearables, to continuously gather data. By integrating AI and IoT, smart healthcare systems improve medical procedures, patient experiences, and operational operations. Rapid and reliable disease diagnosis is made possible by combining AI-driven procedures with IoT data streams. This paper aims to overcome the limitations of traditional methods, which are typically influenced by human biases.
Advances in network and storage technologies enable data centers to provide high-concurrency and low-latency processing responses. To meet user needs, a variety of low-latency technologies can be used to improve the o...
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ISBN:
(纸本)9781510663350
Advances in network and storage technologies enable data centers to provide high-concurrency and low-latency processing responses. To meet user needs, a variety of low-latency technologies can be used to improve the overall concurrent response capability of the system on the premise of ensuring system stability. User mode thread pool is one of the techniques that can be used. In a multi-core processor system, the scheduling management of the user mode thread pool is much more complicated. At present, the scheduling management of the user mode thread pool mainly focuses on the resource utilization of the user mode thread pool itself and the scheduling management of many user-mode threads, such as dynamically adjusting the number and usage of user mode threads in the user mode thread pool according to the load of the system, and replacing the system thread scheduling with user mode thread scheduling, etc. The existing user mode thread scheduling methods do not distinguish the performance requirements of different data processing tasks, treat all user mode thread tasks in the same way, cannot reflect the performance requirements of different user mode thread tasks, and cannot provide more fine-grained user mode thread execution timing control;the existing user mode thread scheduling methods do not provide more effective scheduling control for the host thread, but instead use the thread scheduling mechanism of the operating system itself to control the execution of all kernel threads which is difficult to isolate the host thread and other kernel threads in the system;The existing user mode thread scheduling methods cannot give full play to the parallel execution advantages of the multi-core processor architecture, and it is difficult to implement unified resource allocation management among multiple user mode thread pools. This paper provides a two-level scheduling management method for user mode thread pools that effectively integrates the operating system scheduling m
Edge computing uses devices deployed at the edge of the network to preprocess data to reduce the computing pressure on the central server. Through edge computing, smart grid can process large amounts of data faster, m...
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