FedL_DBNFSpinalNet based malware detection in IoT devices DOI

R. Bhavani,

Veeramalai Sankaradass

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

Prospects and challenges of nanopesticides in advancing pest management for sustainable agricultural and environmental service DOI
R. Siti Zainab,

Maria Hasnain,

Faraz Ali

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 261, P. 119722 - 119722

Published: Aug. 2, 2024

Language: Английский

Citations

23
ENVQA: Improving Visual Question Answering model by enriching the visual feature DOI
Souvik Chowdhury, Badal Soni

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 142, P. 109948 - 109948

Published: Jan. 5, 2025

Language: Английский

Citations

4
Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework DOI Creative Commons
Miloš Antonijević, Miodrag Źivković, Milica Djurić-Jovičić

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 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.

Language: Английский

Citations

4
Intelligent deep federated learning model for enhancing security in internet of things enabled edge computing environment DOI Creative Commons

Nasser Albogami

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 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.

Language: Английский

Citations

3
Refined offshore wind speed prediction: Leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts DOI
Mie Wang, Feixiang Ying,

Qianru Nan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108435 - 108435

Published: April 25, 2024

Language: Английский

Citations

17
Deep study on autonomous learning techniques for complex pattern recognition in interconnected information systems DOI
Zahra Mohtasham‐Amiri, Arash Heidari,

Nima Jafari

et al.

Computer Science Review, Journal Year: 2024, Volume and Issue: 54, P. 100666 - 100666

Published: Sept. 20, 2024

Language: Английский

Citations

15
Wearable hydrogel-based health monitoring systems: A new paradigm for health monitoring? DOI

Xintao Wang,

Haixia Ji,

Li Gao

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 495, P. 153382 - 153382

Published: June 21, 2024

Language: Английский

Citations

12
Enhancing Solar Convection Analysis With Multi‐Core Processors and GPUs DOI Creative Commons
Arash Heidari, Zahra Mohtasham‐Amiri, Mohammad Ali Jabraeil Jamali

et al.

Engineering Reports, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 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.

Language: Английский

Citations

11
Distributed intelligence for IoT-based smart cities: a survey DOI
Mohamed Hashem, Aisha Siddiqa, Fadele Ayotunde Alaba

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(27), P. 16621 - 16656

Published: July 22, 2024

Language: Английский

Citations

10
BIBLIOMETRIC ANALYSIS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE RESEARCH: TRENDS AND FUTURE DIRECTIONS DOI Creative Commons
Renganathan Senthil, Thirunavukarasou Anand,

Chaitanya Sree Somala

et al.

Future Healthcare Journal, Journal Year: 2024, Volume and Issue: 11(3), P. 100182 - 100182

Published: Sept. 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.

Language: Английский

Citations

9