Pyramid Channel-based Feature Attention Network with Ensemble Learning based UAV Image Classification on IoT Assisted Remote Sensing Environment DOI
Saud Alotaibi,

Sana Alazwari,

Iman A. Basheti

et al.

Fractals, Journal Year: 2024, Volume and Issue: 32(09n10)

Published: July 17, 2024

Advances in Unmanned Aerial Vehicles (UAVs), otherwise recognized as drones, have tremendous promise improving the wide-ranging applications of Internet Things (IoT). UAV image classification using deep learning (DL) is an amalgamation to modernize data analysis, collection, and decision-making a variety sectors. IoT devices collect information real time, while remote sensing captures afar without direct contact. UAVs equipped with sensors offer high-quality images for tasks. DL techniques, especially convolutional neural networks (CNNs), analyze streams, extracting complicated features accurate objects or environmental features. This synergy enables including urban planning precision agriculture, fostering smarter disaster response, decision support systems, efficient resource management. paper introduces novel Pyramid Channel-based Feature Attention Network Ensemble Learning-based Image Classification (PCFAN-ELUAVIC) technique assisted environment. The PCFAN-ELUAVIC begins contrast enhancement CLAHE technique. Following that, feature vectors are derived by use PCFAN model. Meanwhile, hyperparameter tuning procedure executed inclusion vortex search algorithm (VSA). For classification, comprises ensemble three classifiers like long short-term memory (LSTM), graph (GCNs), Hermite network (HNN). To exhibit improved detection results system, extensive range experiments carried out. experimental values confirmed enhanced performance model when compared other techniques.

Language: Английский

A reinforcement learning and predictive analytics approach for enhancing credit assessment in manufacturing DOI Creative Commons
Abdul Razaque,

Aliya Beishenaly,

Zhuldyz Kalpeyeva

et al.

Decision Analytics Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100560 - 100560

Published: March 1, 2025

Language: Английский

Citations

1
Pyramid Channel-based Feature Attention Network with Ensemble Learning based UAV Image Classification on IoT Assisted Remote Sensing Environment DOI
Saud Alotaibi,

Sana Alazwari,

Iman A. Basheti

et al.

Fractals, Journal Year: 2024, Volume and Issue: 32(09n10)

Published: July 17, 2024

Advances in Unmanned Aerial Vehicles (UAVs), otherwise recognized as drones, have tremendous promise improving the wide-ranging applications of Internet Things (IoT). UAV image classification using deep learning (DL) is an amalgamation to modernize data analysis, collection, and decision-making a variety sectors. IoT devices collect information real time, while remote sensing captures afar without direct contact. UAVs equipped with sensors offer high-quality images for tasks. DL techniques, especially convolutional neural networks (CNNs), analyze streams, extracting complicated features accurate objects or environmental features. This synergy enables including urban planning precision agriculture, fostering smarter disaster response, decision support systems, efficient resource management. paper introduces novel Pyramid Channel-based Feature Attention Network Ensemble Learning-based Image Classification (PCFAN-ELUAVIC) technique assisted environment. The PCFAN-ELUAVIC begins contrast enhancement CLAHE technique. Following that, feature vectors are derived by use PCFAN model. Meanwhile, hyperparameter tuning procedure executed inclusion vortex search algorithm (VSA). For classification, comprises ensemble three classifiers like long short-term memory (LSTM), graph (GCNs), Hermite network (HNN). To exhibit improved detection results system, extensive range experiments carried out. experimental values confirmed enhanced performance model when compared other techniques.

Language: Английский

Citations

0