State-of-the-Art in 1D Convolutional Neural Networks: A Survey

Ige, Ayokunle Olalekan; Sibiya, Malusi

doi:10.1109/access.2024.3433513

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 157 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial intelligence assisted nanogenerator applications

Xu,

Manshaii,

Xiao

et al. 2025

J. Mater. Chem. A

View full text Add to dashboard Cite

show abstract

Artificial intelligence assisted nanogenerator applications

Xu,

Manshaii,

Xiao

et al. 2025

J. Mater. Chem. A

View full text Add to dashboard Cite

show abstract

Data-Driven Fault Diagnosis of Mooring Systems in Wave Energy Converters

Subramanian,

Zou,

Zhou

et al. 2024

OCEANS 2024 - Halifax

View full text Add to dashboard Cite

Optimized 3D-2D CNN for automatic mineral classification in hyperspectral images

Attallah,

Zigh,

Adda

2024

Reports on Geodesy and Geoinformatics

View full text Add to dashboard Cite

Mineral classification using hyperspectral imaging represents an essential field of research improving the understanding of geological compositions. This study presents an advancedmethodology that uses an optimized 3D-2D CNNmodel for automatic mineral identification and classification. Our approach includes such crucial steps as using the Diagnostic Absorption Band (DAB) selection technique to selectively extract bands that contain the absorption features of minerals for classification in the Cuprite zone. Focusing on the Cuprite dataset, our study successfully identified the following minerals: alunite, calcite, chalcedony, halloysite, kaolinite,montmorillonite,muscovite, and nontronite. The Cuprite dataset results with an overall accuracy rate of 95.73%underscore the effectiveness of our approach and a significant improvement over the benchmarks established by related studies. Specifically, ASMLP achieved a 94.67%accuracy rate, followed by 3D CNN at 93.86%, SAI-MLP at 91.03%, RNN at 89.09%, SPE-MLP at 85.53%, and SAMat 83.31 %. Beyond the precise identification of specific minerals, ourmethodology proves its versatility for broader applications in hyperspectral image analysis. The optimized 3D-2D CNNmodel excels in terms of mineral identification and sets a new standard for robust feature extraction and classification.

show abstract

State-of-the-Art in 1D Convolutional Neural Networks: A Survey

Cited by 4 publications

References 157 publications

Artificial intelligence assisted nanogenerator applications

Artificial intelligence assisted nanogenerator applications

Data-Driven Fault Diagnosis of Mooring Systems in Wave Energy Converters

Optimized 3D-2D CNN for automatic mineral classification in hyperspectral images

Contact Info

Product

Resources

About