Applications of Deep Learning Models in Diverse Streams of IoT DOI
Atul Srivastava,

Haider Rizvi,

Surbhi Bhatia

et al.

Published: Nov. 25, 2023

Internet of Things (IoT) has gained enormous popularity in recent years. From obvious home automations to sophisticated medical procedures, IoT considerable attention and applicability. But there are certain challenges also pertaining apt use applications. The range from generation huge amount data by sensors security privacy threats models. Malwares, energy consumption, decision-making healthcare or agriculture few the challenging aspects need time is make intelligent. Deep learning undoubtedly paves way put intelligence into devices. Application deep techniques helps frameworks handle difficult more easily. For instance, models very suitable find valuable inferences. Malware detection optimisation consumption applications finds right bid for In this chapter, we have gathered compiled various fields IoT. This chapter presents an in-depth study these order explore new horizons different areas

Language: Английский

AI-driven approaches for air pollution modeling: A comprehensive systematic review DOI Creative Commons

Lorenzo Garbagna,

Lakshmi Babu Saheer, Mahdi Maktabdar Oghaz

et al.

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 125937 - 125937

Published: March 1, 2025

Language: Английский

Citations

1
Spatiotemporal informer: A new approach based on spatiotemporal embedding and attention for air quality forecasting DOI
Yang Feng,

Ju-Song Kim,

Jin‐Won Yu

et al.

Environmental Pollution, Journal Year: 2023, Volume and Issue: 336, P. 122402 - 122402

Published: Aug. 16, 2023

Language: Английский

Citations

16
Predicting Air Pollution from Chinese Listed Companies: A Gradient Boosting Regression Tree Approach DOI
Yanjie Jiang, Linlin Zhang, Mei Han

et al.

Environmental science and engineering, Journal Year: 2025, Volume and Issue: unknown, P. 129 - 134

Published: Jan. 1, 2025

Language: Английский

Citations

0
Toward accurate multi-region air quality prediction: integrating transformer-based deep learning and crossover boosted dynamic arithmetic optimization (CDAO) DOI

Vinoth Panneerselvam,

T. Revathi

Signal Image and Video Processing, Journal Year: 2024, Volume and Issue: 18(5), P. 4145 - 4156

Published: March 23, 2024

Language: Английский

Citations

2
AirQFormer: Improving Regional Air Quality Forecast with a Hybrid Deep Learning Model DOI
Mingyun Hu, Xingcheng Lu, Yiang Chen

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 119, P. 106113 - 106113

Published: Dec. 31, 2024

Language: Английский

Citations

1
Deep Learning Techniques for Air Quality Prediction: A Focus on PM2.5 and Periodicity DOI Creative Commons
Lakshmi Shankar,

A. Krishnamoorthy

MIGRATION LETTERS, Journal Year: 2023, Volume and Issue: 20(S13), P. 468 - 484

Published: Dec. 20, 2023

The rapid increase in traffic, urbanization, and industrial expansion has all contributed to a decrease air quality, which vital impact on both the long-term feasibility of environment health humans, particularly industrialized nations. Numerous studies have explored using machine learning for quality forecasting reduce pollution. While shallow architectures offer less accurate forecasts, deep learning, recent advancement computational intelligence, immense potential predicting quality. Deep frameworks can identify intricate correlations patterns data resulting more dependable predictions. Several aspects, including climatic conditions, emission sources, geographical characteristics, may be considered by these models, help one better understand anticipate pollution levels. This research investigates applications' periodic changes Hybrid methods utilize optimization, decomposition, correlation evaluation between PM2.5 particles other factors overcome limitations. study contrasts various algorithms forecasts demonstrates that hybrid is compared each model alone at future periods It proposes directions generation models. literature summary provides valuable insights academics seeking this field.

Language: Английский

Citations

2
Spatiotemporal Patterns and Characteristics of PM2.5 Pollution in the Yellow River Golden Triangle Demonstration Area DOI Creative Commons
Ning Jin, Liang He,

Haixia Jia

et al.

Atmosphere, Journal Year: 2023, Volume and Issue: 14(4), P. 733 - 733

Published: April 19, 2023

Improving air quality in the Yellow River Golden Triangle Demonstration Area (YRGTDA) is an important practice for ecological protection and high-quality development Basin. Preventing controlling PM2.5 pollution this region will require a scientific understanding of spatiotemporal patterns characteristics pollution. data from different sources were combined study (the annual average concentrations obtained Atmospheric Composition Analysis Group Dalhousie University, daily concentration China National Environmental Monitoring Centre). Then, temporal variation at annual, seasonal, monthly scales, spatial concentrations, classes analyzed. Results showed that: (1) scale, decreasing trend 2000 to 2021 area. The divided into two stages. (2) At seasonal high occurred mainly winter, low summer. U-shaped pattern January December each year. (3) hotspot analysis area cyclical pattern. (4) exhibited values central northern southern parts YRGTDA. (5) number days 2015 followed order Good > Excellent Light Moderate Heavy Severe results have great theoretical practical significance because they reveal lead scientifically based measures reasonably prevent control

Language: Английский

Citations

1
IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review DOI Creative Commons

Hayder Qasim Flayyih,

Jumana Waleed,

Amer M. Ibrahim

et al.

International Journal of Computing and Digital Systems, Journal Year: 2024, Volume and Issue: 15(1), P. 813 - 826

Published: May 14, 2024

This exploratory disquisition delves into the world of Indoor Air Quality( IAQ) monitoring systems, using solidarity Artificial Intelligence( AI) and Internet Effects ( IoT) technologies.Its overarching thing is to check efficacity these structures in regulating IAQ within structures, with a specific focus on mollifying pollutant degrees their dangerous results inhabitants.The study undertakes comprehensive review present literature exploration trials, which depend upon AI IoT algorithms for border monitoring, records analysis, contrivance evaluation.also, it complications machine armature, deployment ways, functional efficiency.Furthermore, attracts different instructional budgets, including clever detectors bias stationed ambient surroundings.It elucidates functionality those instruments accumulate real-time statistics, encompassing variables together unpredictable natural composites, temperature oscillations, moisture ranges.A vital aspect this AI, getting know Machine Learning ML), Deep DL) algorithms, showcasing prophetic prowess shadowing fabrics.also, they have look at delving symbiotic dating among expounding function enhancing delicacy optimizing energy intake.Moreover, studies trials delineate personalized health tips knitter-made character inhabitants, decided from wealth accrued through structures.By integrating present-day technologies empirical perceptivity, takes pave manner better control strategies, fostering more healthy lesser sustainable lodging surroundings.

Language: Английский

Citations

0
Air quality index prediction for clearer skies using improved long short-term memory DOI

Nilesh Bhaskarrao Bahadure,

Oshin Sahare,

Nishant Shukla

et al.

Intelligent Decision Technologies, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 10

Published: Sept. 10, 2024

Air pollution has become an international calamity, a problem for human health and the environment. The ability to predict air quality becomes crucial task. usual approaches assessing are exhausted when extracting complicated non-linear relationships long-term dependence features embedded in data. Long- short-term memory, recurrent neural network family, emerged as potent tool addressing mentioned issues, so computer-aided technology essential aid with high level of prediction best-in-class accuracy. In this study, we investigated classic time-series analysis based on Improved Long memory (ILSTM) improve performance index prediction. predicted AQI value 25 days lies 97.63% Confidence interval zone highly adoptable metrics such R-Square, MSE, RMSE, MAE values.

Language: Английский

Citations

0
Predicting air quality in smart city using novel transfer learning based framework DOI Open Access
Shilpa Sonawani, Kailas Patil

Indonesian Journal of Electrical Engineering and Computer Science, Journal Year: 2023, Volume and Issue: 32(2), P. 1014 - 1014

Published: Sept. 24, 2023

Air quality is a matter of concern these days due to its adverse effect on human health. Multiple new air pollution monitoring and prediction stations are being developed in smart cities tackle the issue. Recent advanced deep learning techniques show excellent performance for predictions but need sufficient training data model performance. The insufficiency issue at station can be resolved using proposed novel transfer learning-based framework predict concentration station. ability significantly enhanced by this effective technology. assessed various Delhi, India.

Language: Английский

Citations

0