TY  - JOUR
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117009228&doi=10.32604%2fcmc.2022.020732&partnerID=40&md5=0a89f42575a6dd3c06e57dbb05782837
A1  - Muneer, A.
A1  - Taib, S.M.
A1  - Fati, S.M.
A1  - Balogun, A.O.
A1  - Aziz, I.A.
JF  - Computers, Materials and Continua
VL  - 70
Y1  - 2022///
N2  - Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the â??curse of dimensionalityâ?? that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a real-world gas turbine (GT) dataset that contains 87620 columns and 56 rows. During the experiment, two issues have been investigated and solved to enhance the results. The first is the dataset class imbalance, which solved using SMOTE technique. The second issue is the poor performance, which can be solved using one of the optimization algorithms. Several optimization algorithms have been investigated and tested, including stochastic gradient descent (SGD), RMSprop, Adam and Adamax. However, Adamax optimization algorithm showed the best results when employed to train the DANN model. The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40, F1-score of 0.9649, Area Under the Curve (AUC) rate of 0.9649, and a minimal loss function during the hybrid model training. © 2022 Tech Science Press. All rights reserved.
IS  - 3
KW  - Clustering algorithms; Deep learning; Gradient methods; Multilayer neural networks; Optimization; Stochastic systems
KW  -  Anomaly detection; Auto encoders; Critical researches; Deep learning; High dimensional data; Hybrid model; Optimization algorithms; Real-world problem; Research issues; Unsupervised anomaly detection
KW  -  Anomaly detection
ID  - scholars17837
EP  - 6088
SN  - 15462218
PB  - Tech Science Press
N1  - cited By 15
TI  - A hybrid deep learning-based unsupervised anomaly detection in high dimensional data
SP  - 6073
AV  - none
ER  -