Comprehensive systematic review of information fusion methods in smart cities and urban environments

Information Fusion, Journal Year: 2024, Volume and Issue: 107, P. 102317 - 102317

Published: Feb. 21, 2024

Smart cities result from integrating advanced technologies and intelligent sensors into modern urban infrastructure. The Internet of Things (IoT) data integration are pivotal in creating interconnected spaces. In this literature review, we explore the different methods information fusion used smart cities, along with their advantages challenges. However, there notable challenges managing diverse sources, handling large volumes, meeting near-real-time demands various city applications. review aims to examine applications detail, incorporating quality evaluation techniques identifying critical issues while outlining promising research directions. order accomplish our goal, conducted a comprehensive search applied selective criteria. We identified 59 recent studies addressing machine learning (ML) deep (DL) These were obtained databases such as ScienceDirect (SD), Scopus, Web Science (WoS), IEEE Xplore. main objective study is provide more detailed insights by supplementing existing research. word cloud visualisation learning/deep papers shows landscape, covering both technical aspects artificial intelligence practical settings. Apart exploration, also delves ethical privacy implications arising cities. Moreover, it thoroughly examines that must be addressed realise revolution's potential fully.

Language: Английский

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A Systematic Literature Review of Digital Twin Research for Healthcare Systems: Research Trends, Gaps, and Realization Challenges

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 4099 - 4126

Published: Jan. 1, 2024

Using the PRISMA approach, we present first systematic literature review of digital twin (DT) research in healthcare systems (HSs). This endeavor stems from pressing need for a thorough analysis this emerging yet fragmented area, with goal consolidating knowledge to catalyze its growth. Our findings are structured around three questions aimed at identifying: (i) current trends, (ii) gaps, and (iii) realization challenges. Current trends indicate global interest interdisciplinary collaborations address complex HS However, existing predominantly focuses on conceptualization; integration, verification, implementation is nascent. Additionally, document that substantial body papers mislabel their work, often disregarding modeling twinning methods necessary elements DT. Furthermore, provide non-exhaustive classification based two axes: the object (i.e., product or process) context patient's body, medical procedures, facilities, public health). While testament diversity field, it implies specific pattern could be reimagined. We also identify gaps: considering human-in-the-loop nature HSs focus provider decision-making research. Lastly, discuss challenges broad-scale DTs HSs: improving virtual-to-physical connectivity data-related issues. In conclusion, study suggests DT potentially help alleviate acute shortcomings manifested inability concurrently improve quality care, wellbeing, cost efficiency.

Language: Английский

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A Novel Fake News Detection Model for Context of Mixed Languages Through Multiscale Transformer

IEEE Transactions on Computational Social Systems, Journal Year: 2023, Volume and Issue: 11(4), P. 5079 - 5089

Published: Aug. 21, 2023

Fake news detection has been a more urgent technical demand for operators of online social platforms, and the prevalence deep learning well boosts its development. From model structure, existing research works can be categorized into three types: convolution filtering-based neural network approaches, sequential analysis-based attention mechanism-based approaches. However, almost all them were developed oriented to scenes single language, without considering context mixed languages. To bridge such gap, this article extends basic pretraining language processing transformer multiscale format proposes novel fake languages through fully capture semantic information text. By extracting fruitful feature levels initial textual contents, it is expected obtain resilient spaces semantics characteristics Finally, experiments are conducted on postprocessed real-world dataset illustrate efficiency proposal by comparing performance with four baseline methods. The results obtained show that proposed method an accuracy about 2%–10% higher than commonly used models, indicating scheme appropriate in scenarios.

Language: Английский

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Predicting Diabetes with Federated Learning: A Digital Twin and Medical Fog-based IoT Framework

ACM Transactions on Internet Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

In today's world, maintaining good health has become increasingly paramount. The global prevalence of diabetes surged due to the stress modern life and unhealthy dietary habits. Detecting at an early stage imperative. Leveraging advancements in Cloud Fog computing, we can create Internet-enabled Medical framework that incorporates Machine Learning (ML) techniques predict diagnose its inception. This prediction diagnosis would enable remote medical assistance for individuals living far from immediate facilities. Internet Things allows patient data be gathered via sensors, analyzed using ML techniques, stored Cloud, providing direct access healthcare professionals. Therefore, current study introduces a Digital Twin (DT)-enabled framework, supported by Federated (FL) SaJAYA-ANFIS approach prediction. FL ensures privacy is upheld while fostering seamless, intelligent ecosystem bridges gap between patients doctors through DT. Data collection begins (IoT) layer followed processing layer, comprising diverse computing nodes with specific pre-processing tools model Predicted outcomes are then analysis proposed addresses concerns (FL). method been validated UCI dataset compared state-of-the-art FL-supported DT-supported various performance metrics (Accuracy, Precision, Specificity, Recall Fβ-measure). results demonstrate our outperforms other baselines, achieving 93.5% accuracy 92% Fβ-measure, respectively. Healthcare

Language: Английский

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Digital twins in healthcare and biomedicine

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 365 - 401

Published: Jan. 1, 2024

Language: Английский

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Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis

Sensors, Journal Year: 2024, Volume and Issue: 24(3), P. 958 - 958

Published: Feb. 1, 2024

The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in terms diagnosis and management thoracic disorders. This study proposes comprehensive framework, named Lung-DT, which leverages IoT sensors AI algorithms establish the digital representation patient’s respiratory health. Using YOLOv8 neural network, Lung-DT system accurately classifies chest X-rays into five distinct categories lung diseases, including “normal”, “covid”, “lung_opacity”, “pneumonia”, “tuberculosis”. performance was evaluated employing X-ray dataset available literature, demonstrating average accuracy 96.8%, precision 92%, recall 97%, F1-score 94%. proposed framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring health through continuous data acquisition from sensors, facilitating early intervention. Secondly, AI-powered classification module provides automated objective assessments X-rays, reducing dependence on subjective human interpretation. Thirdly, twin allows for analysis correlation multiple streams, providing valuable insights personalized treatment plans. algorithms, DT technology within demonstrates significant step towards improving healthcare. By enabling monitoring, diagnosis, analysis, enormous potential enhance patient outcomes, reduce healthcare costs, optimize resource allocation.

Language: Английский

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Developing a Skilled Workforce for Future Industry Demand: The Potential of Digital Twin-Based Teaching and Learning Practices in Engineering Education

Sustainability, Journal Year: 2023, Volume and Issue: 15(23), P. 16433 - 16433

Published: Nov. 30, 2023

Engineering education providers should foresee the potential of digital transformation teaching and skill-developing activities so that graduating engineers can find themselves highly aligned with demands attributes needed by prospective industrial employers. The advancement revolutions towards hybridisation enabling technologies recognised Industry 4.0, Society 5.0, 5.0 have transformed components engineering higher system remarkably. Future workforce requirements will demand an employee’s multidisciplinary skill mix other professional qualities. Implementing human-centric decision-making based on insights from Digital Twin (DT) systems, sustainability, lean systems is necessary for further economic growth. Recent barriers identified Australian Council Deans, development capabilities, affordable digitally learning facilities were all considered. This paper explores role Twins (DTs) in enhancing incorporating 4.0 advances. By reviewing curricula, pedagogy, evolving graduates, this study identifies key benefits DTs, such as cost-effectiveness, resource management, immersive experiences. also outlines challenges implementing DT-based labs, including IT infrastructure, data quality, privacy, security issues. findings indicate embrace DTs to foster skills meet future demands. Collaboration industry highlighted a crucial factor successful practices offering real-world COVID-19 pandemic has expedited adoption DT technologies, demonstrating their utility minimising educational disruptions. While acknowledges high prepare students demands, it emphasises need among educators ensure effective balanced implementation.

Language: Английский

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Digital Twins for Healthcare Using Wearables

Bioengineering, Journal Year: 2024, Volume and Issue: 11(6), P. 606 - 606

Published: June 13, 2024

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is large part due to their ability update real time physical counterparts and connect across multiple devices. As result, much interest directed towards using the healthcare industry. Recent advancements smart wearable technologies have allowed for utilization human healthcare. Human can be generated biometric data from patient gathered wearables. These then used enhance care through variety means, such as simulated clinical trials, disease prediction, monitoring treatment progression remotely. revolutionary method still its infancy, such, there limited research on wearables generate applications. paper reviews literature pertaining twins, including methods, applications, challenges. The also presents conceptual creating body sensors.

Language: Английский

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Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis

Published: Jan. 3, 2024

The integration of Artificial Intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in the diagnosis and management thoracic disorders. This study proposes comprehensive framework, named Lung-DT, which leverages IoT sensors AI algorithms establish digital representation patient’s respiratory health. Using YOLOv8 neural network, Lung-DT system accurately classifies chest X-Rays into five distinct categories lung diseases, including "Normal," "Covid," "Lung Opacity," "Pneumonia," "Tuberculosis". system’s performance was evaluated on X-Ray dataset, demonstrating an impressive average accuracy 96.6% across all classes. Further tests (prediction) were conducted trained network using third dataset available literature completely unknown yielding 98% three proposed framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring health through continuous data acquisition from sensors, facilitating early intervention. Secondly, AI-powered classification module provides automated objective assessments X-Rays, reducing dependence subjective human interpretation. Thirdly, twin allows for analysis correlation multiple streams, providing valuable insights personalized treatment plans. algorithms, DT technology within demonstrates significant step towards improving healthcare. By enabling monitoring, diagnosis, analysis, enormous potential enhance patient outcomes, reduce healthcare costs, optimize resource allocation.

Language: Английский

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Digital twin and sensor networks for healthcare monitoring frameworks

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 217 - 261

Published: Jan. 1, 2025

Language: Английский

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