Federated learning based reference evapotranspiration estimation for distributed crop fields

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0314921 - e0314921

Published: Feb. 5, 2025

Water resource management and sustainable agriculture rely heavily on accurate Reference Evapotranspiration (ET o ). Efforts have been made to simplify the ) estimation using machine learning models. The existing approaches are limited a single specific area. There is need for ET estimations of multiple locations with diverse weather conditions. study intends propose distinct conditions federated approach. Traditional centralized require aggregating all data in one place, which can be problematic due privacy concerns transfer limitations. However, trains models locally combines knowledge, resulting more generalized estimates across different regions. three geographical Pakistan, each conditions, selected implement proposed model from 2012 2022 locations. At location, named Random Forest Regressor (RFR), Support Vector (SVR), Decision Tree (DTR), evaluated local (ET) global model. feature importance-based analysis also performed assess impacts parameters performance at location. evaluation reveals that (RFR) based outperformed other coefficient determination (R 2 = 0.97%, Root Mean Squared Error (RMSE) 0.44, Absolute (MAE) 0.33 mm day −1 , Percentage (MAPE) 8.18%. yields against site. results suggest maximum temperature wind speed most influential factors predictions.

Language: Английский

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An Efficient and Hybrid Deep Learning-Driven Model to Enhance Security and Performance of Healthcare Internet of Things

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 22931 - 22945

Published: Jan. 1, 2025

Language: Английский

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Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Healthcare, Journal Year: 2024, Volume and Issue: 12(24), P. 2587 - 2587

Published: Dec. 22, 2024

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, remote monitoring, which empower real-time, decentralized data processing for predictive analytics personalized care. It addresses key challenges, including security risks like adversarial attacks, poisoning, model inversion. Additionally, it covers issues related to heterogeneity, scalability, system interoperability. Alongside these, the highlights emerging privacy-preserving solutions, such as differential secure multiparty computation, critical overcoming limitations. Successfully addressing these hurdles essential enhancing efficiency, accuracy, broader adoption in healthcare. Ultimately, FL offers transformative potential secure, data-driven promising improved outcomes, operational sovereignty ecosystem.

Language: Английский

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Perceptions of business graduates in Egypt on incorporating data analytics into the business curriculum

Cogent Education, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 2, 2025

The advent of data analytics has disrupted the business professions. It not only created new job positions but also transformed established jobs. curriculum is expected to undergo a technological change prepare students be competitive in market. However, minimal studies have explored incorporation technologies such as education, especially context developing countries. This study aims investigate importance incorporating into Egypt. In doing so, questionnaire was distributed school graduates Egypt explore their perceptions regarding curriculum, tools that should incorporated and whether individual work-related factors can affect perceptions. final sample consisted 191 respondents. Descriptive statistics, t-test ANOVA were used analyze data. Generally, perceived necessary. Also, findings showed work experience profession influenced respondent's integrating curriculum. this direct attention education authorities universities curricula.

Language: Английский

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Edge-Cloud Solutions for Big Data Analysis and Distributed Machine Learning - 2

Future Generation Computer Systems, Journal Year: 2025, Volume and Issue: unknown, P. 107745 - 107745

Published: Feb. 1, 2025

Language: Английский

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Exploring the Pathways Through Which the Digital Economy Drives Common Prosperity in the Context of Sustainable Development

Sustainability, Journal Year: 2025, Volume and Issue: 17(8), P. 3709 - 3709

Published: April 19, 2025

The digital economy, as a major economic form after the agricultural and industrial economies, has become new driving force in development of national it may provide opportunities for rural through businesses such platform economy live e-commerce. However, there also be risk divide, mechanism its impact on shared prosperity needs to scientifically verified. Based panel data 2243 counties China from 2011 2021, article empirically examines how promotes common among regions spatial spillover effects economy. findings suggest that, first, geographic distance matrix reveals positive relationship between prosperity, phenomenon geo-graphic agglomeration is observed, which manifests itself high-high-low aggregation. Second, had an that transcends space, enabling both “expand cake” “share more equitably. Third, coordinated, inclusive, structurally optimizing help achieve by upgrading level public services promoting structure. Ultimately, long-term county economies innovation-driven optimized resource allocation.

Language: Английский

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Timed-Release and Partially Private Access Control for Decentralized Iot Collaboration Systems

Published: Jan. 1, 2025

Language: Английский

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Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions

Computer Science Review, Journal Year: 2025, Volume and Issue: 57, P. 100751 - 100751

Published: April 4, 2025

Language: Английский

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NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2369 - 2369

Published: April 8, 2025

Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality life. They cause nasal congestion, facial pain, headaches, thick discharge, and a reduced sense smell. However, accurately diagnosing these is challenging due to multiple factors, including inadequate adherence pre-diagnostic protocols. By leveraging latest developments in Artificial Intelligence (AI), there exists substantial opportunity improve precision effectiveness classification diseases. In this study, we present novel AI-based approach for sinonasal pathology detection, using Self-Supervised Learning (SSL) techniques Random Forest (RF) algorithms. We have collected new diagnostic imaging dataset, which major contribution study. The dataset contains 137 CT MRI images meticulously labeled by expert radiologists, with two classes: healthy unhealthy (sinus disease). This useful asset developing evaluating techniques. addition, our proposed employs Deep InfoMax (DIM) model extract meaningful global local features from data self-supervised method. These then used as input an RF classifier, effectively distinguishes between pathological cases. combination both DIM provides efficient feature learning powerful sinus Our preliminary results demonstrate efficiency approach, achieves mean accuracy 92.62%. findings highlight potential improving diagnosis.

Language: Английский

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Navigating the fusion of federated learning and big data: a systematic review for the AI landscape

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(5)

Published: April 28, 2025

Language: Английский

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