Evidence map›Paper›PMID 39309890›Full record

ArticleHeliyon2024

Prairie Dog Optimization Algorithm with deep learning assisted based Aerial Image Classification on UAV imagery.

Amal K Alkhalifa, Muhammad Kashif Saeed, Kamal M Othman, Shouki A Ebad, Mohammed Alonazi, Abdullah Mohamed

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Amal K AlkhalifaDepartment of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.
Muhammad Kashif SaeedDepartment of Computer Science, Applied College at Mahayil, King Khalid University, Saudi Arabia.
Kamal M OthmanDepartment of Electrical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Saudi Arabia.
Shouki A EbadDepartment of Computer Science, Faculty of Science, Northern Border University, Arar, 91431, Saudi Arabia.
Mohammed AlonaziDepartment of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia.
Abdullah MohamedResearch Centre, Future University in Egypt, New Cairo, 11845, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a Prairie Dog Optimization Algorithm with a Deep learning-assisted Aerial Image Classification Approach (PDODL-AICA) on UAV images. The PDODL-AICA technique exploits the optimal DL model for classifying aerial images into numerous classes. In the presented PDODL-AICA technique, the feature extraction procedure is executed using the EfficientNetB7 model. Besides, the hyperparameter tuning of the EfficientNetB7 technique uses the PDO model. The PDODL-AICA technique uses a convolutional variational autoencoder (CVAE) model to detect and classify aerial images. The performance study of the PDODL-AICA model is implemented on a benchmark UAV image dataset. The experimental values inferred the authority of the PDODL-AICA approach over recent models in terms of dissimilar measures.

Indexed as

Aerial image classificationDeep learningPrairie dog optimizationRemote sensingUAV

Identifiers

PMID39309890
PMCPMC11416545

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.