Prospects and challenges of nanopesticides in advancing pest management for sustainable agricultural and environmental service

Environmental Research, Год журнала: 2024, Номер 261, С. 119722 - 119722

Опубликована: Авг. 2, 2024

Язык: Английский

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ENVQA: Improving Visual Question Answering model by enriching the visual feature

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 142, С. 109948 - 109948

Опубликована: Янв. 5, 2025

Язык: Английский

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Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 28, 2025

Internet of Things (IoT) is one the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, smart gadgets into environment enables IoT to deepen interactions enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because devices are often built with minimal hardware connected Internet, they highly susceptible different types cyberattacks, presenting significant security problem maintaining secure infrastructure. Conventional techniques have difficulty countering these evolving threats, highlighting need adaptive solutions powered artificial intelligence (AI). This work seeks improve trust in edge integrated study revolves around hybrid framework combines convolutional neural networks (CNN) machine learning (ML) classifying models, like categorical boosting (CatBoost) light gradient-boosting (LightGBM), further optimized through metaheuristics optimizers leveraged performance. A two-leveled architecture was designed manage intricate data, detection classification attacks within networks. thorough analysis utilizing real-world network dataset validates proposed architecture's efficacy identification specific variants malevolent assaults, classic multi-class challenge. Three experiments were executed open public, where top models attained supreme accuracy 99.83% classification. Additionally, explainable AI methods offered valuable supplementary insights model's decision-making supporting future collection efforts enhancing systems.

Язык: Английский

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Intelligent deep federated learning model for enhancing security in internet of things enabled edge computing environment

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 3, 2025

In the present scenario, Internet of Things (IoT) and edge computing technologies have been developing rapidly, foremost to development new tasks in security privacy. Personal information privacy leakage become main concerns IoT surroundings. The promptly IoT-connected devices below an integrated Machine Learning (ML) method might threaten data confidentiality. standard centralized ML-assisted methods challenging because they require vast numbers a vital unit. Due rising distribution many systems linked devices, decentralized ML solutions required. Federated learning (FL) was proposed as optimal solution discover these issues. Still, heterogeneity environments poses essential task when executing FL. Therefore, this paper develops Intelligent Deep Model for Enhancing Security (IDFLM-ES) approach IoT-enabled edge-computing environment. presented IDFLM-ES aims identify unwanted intrusions certify safety To accomplish this, technique introduces federated hybrid deep belief network (FHDBN) model using FL on time series produced by devices. Besides, uses normalization golden jackal optimization (GJO) based feature selection pre-processing step. learns individual distributed representation over databases enhance convergence quick learning. Finally, dung beetle optimizer (DBO) is utilized choose effectual hyperparameter FHDBN model. simulation value methodology verified benchmark database. experimental validation portrayed superior accuracy 98.24% compared other models.

Язык: Английский

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Refined offshore wind speed prediction: Leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108435 - 108435

Опубликована: Апрель 25, 2024

Язык: Английский

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Deep study on autonomous learning techniques for complex pattern recognition in interconnected information systems

Computer Science Review, Год журнала: 2024, Номер 54, С. 100666 - 100666

Опубликована: Сен. 20, 2024

Язык: Английский

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Wearable hydrogel-based health monitoring systems: A new paradigm for health monitoring?

Chemical Engineering Journal, Год журнала: 2024, Номер 495, С. 153382 - 153382

Опубликована: Июнь 21, 2024

Язык: Английский

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Enhancing Solar Convection Analysis With Multi‐Core Processors and GPUs

Engineering Reports, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 13, 2024

ABSTRACT In the realm of astrophysical numerical calculations, demand for enhanced computing power is imperative. The time‐consuming nature particularly in domain solar convection, poses a significant challenge Astrophysicists seeking to analyze new data efficiently. Because they let different kinds be worked on separately, parallel algorithms are good way speed up this kind work. A lot study about how use both multi‐core computers and GPUs do math work energy at same time. Cutting down time it takes with main goal. This way, can looked more quickly without having practice long It works well when you things parallel, especially 3D tasks, which speeds lot. proof important adjust parallelization methods based size numbers. But 2D math, than one core better. results not only fix bugs models but also show that changes little gear processed.

Язык: Английский

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Distributed intelligence for IoT-based smart cities: a survey

Neural Computing and Applications, Год журнала: 2024, Номер 36(27), С. 16621 - 16656

Опубликована: Июль 22, 2024

Язык: Английский

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BIBLIOMETRIC ANALYSIS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE RESEARCH: TRENDS AND FUTURE DIRECTIONS

Future Healthcare Journal, Год журнала: 2024, Номер 11(3), С. 100182 - 100182

Опубликована: Сен. 1, 2024

The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that completely transforming the industry as whole. Using sophisticated algorithms data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, fostering innovation across ecosystem. This study conducts comprehensive bibliometric analysis research on healthcare, utilising SCOPUS database primary source.

Язык: Английский

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