Novel semi-supervised learning approach for descriptor generation using artificial neural networks
[ X ]
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The rise of machine learning and neural networks has opened many doors for making various arduous real-life tasks far
more accessible, in addition to their ability to analyze vast amounts of data that are considered to be impossible for humans
to process. Neural networks are an essential topic as they can be applied in many real-life applications, such as image,
video and sound matching, making them a very attractive research area. Numerous methods and approaches are available
for training neural networks, but this paper is concerned with only the semi-supervised training approach, for which a new
‘‘enhanced semi-supervised’’ learning method is proposed. Semi-supervised learning means that machines, such as
computers, can learn in the presence of datasets that are both labeled and unlabeled. In contrast, the supervised learning
approach can be applicable with labeled data only. A novel semi-supervised learning approach for descriptor generation
using artificial neural networks is proposed to control the values that are output by the neural network. However, no
interaction with the assignment of these values to each input group occurs, nor is the space where the output values belong
utilized. Thus, this method seeks to provide a more efficient learning approach with a more even distribution of the output
throughout the output field of space, resulting in a more effective learning approach. The handwritten digit experiment
showed an accuracy of 85.27%, while Alzheimer’s detection experiment recorded an accuracy of 99.27%. The results after
applying the proposed method to two sets of experimental data revealed a significant improvement in accuracy compared
with the use of Siamese neural networks in different applications.
Açıklama
Anahtar Kelimeler
Artificial Neural Network, Semi-Supervised Learning, Feature Extraction, Descriptor Generation
Kaynak
Soft Computing
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Alwindawi, A. F., Uçan, O. N., Ibrahim, A. A., & Yusuf, A. (2022). Novel semi-supervised learning approach for descriptor generation using artificial neural networks. Soft Computing, 1-12.
