Federated learning for green and sustainable 6G IIoT applications

Internet of Things, Год журнала: 2024, Номер 25, С. 101061 - 101061

Опубликована: Янв. 4, 2024

Язык: Английский

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Improving Healthcare Prediction of Diabetic Patients Using KNN Imputed Features and Tri-Ensemble Model

IEEE Access, Год журнала: 2024, Номер 12, С. 16783 - 16793

Опубликована: Янв. 1, 2024

Objective: Diabetes ranks as the most prevalent ailment in developing nations. Vital steps to mitigate consequences of diabetes include early detection and expert medical intervention. A highly effective approach for identifying involves assessing specific indicators associated with this condition. When it comes automated detection, frequently encountered datasets exhibit gaps data, which can markedly impact effectiveness machine learning models. Methods: The aim study is propose an method predicting diabetes, a focus on appropriately dealing missing data improving accuracy. proposed framework makes use K-Nearest Neighbour (KNN) imputed features along Tri-ensemble voting classifier model. Results:By incorporating KNN imputer, presented model demonstrates impressive performance metrics, including accuracy 97.49%, precision 98.16%, recall 99.35%, F1 score 98.84%. conducted thorough comparison against seven alternative algorithms, them under two conditions: one omitted values another imputer applied. These findings support model's efficacy, highlighting its superiority over currently established state-of-the-art techniques. Conclusion: This research explores problem diagnosis highlights efficacy KNN-imputed technique. results are promising healthcare practitioners they could facilitate improve quality diabetic patient care.

Язык: Английский

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The neurosociological paradigm of the metaverse

Frontiers in Psychology, Год журнала: 2025, Номер 15

Опубликована: Янв. 7, 2025

Metaverse integrates people into the virtual world, and challenges depend on advances in human, technological, procedural dimensions. Until now, solutions to these have not involved extensive neurosociological research. The study explores pioneering paradigm metaverse, emphasizing its potential revolutionize our understanding of social interactions through advanced methodologies such as hyperscanning interbrain synchrony. This convergence presents unprecedented opportunities for neurotypical neurodivergent individuals due technology personalization. Traditional face-to-face, coupling, metaverse are empirically substantiated. Biomarkers interaction feedback between brain networks is presented. innovative contribution findings broader literature neurosociology article also discusses ethical aspects integrating metaverse.

Язык: Английский

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Factors affecting the adoption of metaverse in healthcare: The moderating role of digital division, and meta-culture

Heliyon, Год журнала: 2024, Номер 10(7), С. e28778 - e28778

Опубликована: Апрель 1, 2024

This research aims to find out the factors affecting adoption of Metaverse in healthcare. study explores effect perceived ease use, usefulness, and trust on adopting healthcare by keeping digital division metaculture as moderating variables. The philosophical foundation is rooted positivism paradigm, methodology quantitative, approach used deductive. Data was collected Pakistan China through judgmental sampling from 384 respondents. Partial Least Square Structural Equation Modelling (PLS-SEM) analyze data. findings validate relationship between use metaverse with β-value 0.236, t-value 5.207 p-value 0.000, usefulness 0.233, 4.017 a 0.192, 3.589 0.000. Results also show that divide moderates relation having 0.078, 1.848 0.032. Similarly, does not moderate relationships metaverse. Moreover, meta culture contributes theoretical examining various necessary for its development. It provides guidelines developers adopters suitable technology.

Язык: Английский

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Privacy preservation in Artificial Intelligence and Extended Reality (AI-XR) metaverses: A survey

Journal of Network and Computer Applications, Год журнала: 2024, Номер 231, С. 103989 - 103989

Опубликована: Авг. 2, 2024

The metaverse is a nascent concept that envisions virtual universe, collaborative space where individuals can interact, create, and participate in wide range of activities. Privacy the critical concern as evolves immersive experiences become more prevalent. privacy problem refers to challenges concerns surrounding personal information data within Virtual Reality (VR) environments shared VR becomes accessible. Metaverse will harness advancements from various technologies such Artificial Intelligence (AI), Extended (XR) Mixed (MR) provide personalized services its users. Moreover, enable experiences, relies on collection fine-grained user leads issues. Therefore, before potential be fully realized, related must addressed. This includes safeguarding users' control over their data, ensuring security information, protecting in-world actions interactions unauthorized sharing. In this paper, we explore future metaverses are expected face, given reliance AI for tracking users, creating XR MR facilitating interactions. thoroughly analyze technical solutions differential privacy, Homomorphic Encryption, Federated Learning discuss sociotechnical issues regarding privacy.

Язык: Английский

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Harnessing the Power of Artificial Intelligence in Pharmaceuticals: Current Trends and Future Prospects

Intelligent Pharmacy, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 772 - 772

Опубликована: Янв. 14, 2025

This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with aim improving adaptive personalized environments. A systematic review scientific literature was conducted, analyzing 370 articles published between 2006 2024. The research examines how AI can support identification patterns individual student needs. Through EDM, are analyzed to predict performance enable timely interventions. HITL-ML ensures that educators remain in control, allowing them adjust system according their pedagogical goals minimizing potential biases. Machine-assisted teaching allows processes be structured around specific criteria, ensuring relevance outcomes. findings suggest these applications significantly improve learning, tracking, resource optimization institutions. highlights ethical considerations, such as need protect privacy, ensure transparency algorithms, promote equity, inclusive fair Responsible implementation methods could quality.

Язык: Английский

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Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC

IEEE Transactions on Consumer Electronics, Год журнала: 2023, Номер 70(1), С. 2596 - 2607

Опубликована: Дек. 13, 2023

With the rapid development of consumer electronics and communication technology, a large amount data is generated from end users at edge networks. Modern recommendation systems take full advantage such for training their various artificial intelligence (AI) models. However, traditional centralized model has to transmit all cloud-based servers, which suffers privacy leakage resource shortage. Therefore, mobile computing (MEC) combined with federated learning (FL) considered as promising paradigm address these issues. The smart devices can provide resources FL local parameters base station (BS) equipped servers aggregate into global model. Nevertheless, due limited physical risk leakage, (the owners devices) would not like participate in voluntarily. To this issue, we game theory propose an incentive mechanism based on two-stage Stackelberg inspire contribute FL. We define two utility functions BS, formulate maximization problem. Through theoretical analysis, obtain Nash equilibrium strategy Furthermore, game-based algorithm (GIMA) achieve equilibrium. Finally, simulation results are provided verify performance our GIMA algorithm. experimental show that converges quickly, higher value compared other methods.

Язык: Английский

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MDEformer: Mixed Difference Equation Inspired Transformer for Compressed Video Quality Enhancement

IEEE Transactions on Neural Networks and Learning Systems, Год журнала: 2024, Номер 36(2), С. 2410 - 2422

Опубликована: Янв. 29, 2024

Deep learning methods have achieved impressive performance in compressed video quality enhancement tasks. However, these rely excessively on practical experience by manually designing the network structure and do not fully exploit potential of feature information contained sequences, i.e., taking full advantage multiscale similarity artifact seriously considering impact partition boundaries overall quality. In this article, we propose a novel Mixed Difference Equation inspired Transformer (MDEformer) for enhancement, which provides relatively reliable principle to guide design yields new insight into interpretable transformer. Specifically, drawing graphical concept mixed difference equation (MDE), utilize multiple cross-layer cross-attention aggregation (CCA) modules establish long-range dependencies between encoders decoders transformer, where boundary smoothing (PBS) are inserted as feedforward networks. The CCA module can make use compression artifacts effectively remove artifacts, recover texture detail frame. PBS leverages sensitivity convolution eliminate improve its quality, while having too much impacts non-boundary pixels. Extensive experiments MFQE 2.0 dataset demonstrate that proposed MDEformer improving video, surpasses state-of-the-arts (SOTAs) terms both objective metrics visual

Язык: Английский

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Enhancing medical image classification via federated learning and pre-trained model

Egyptian Informatics Journal, Год журнала: 2024, Номер 27, С. 100530 - 100530

Опубликована: Авг. 28, 2024

Язык: Английский

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