Unleashing the power of Bat optimized CNN-BiLSTM model for advanced network anomaly detection: Enhancing security and performance in IoT environments

Antonius, Franciskus; Sekhar, J.C.; Sreenivasa Rao, Vuda; Pradhan, Rahul; Narendran, S.; Fernando Cosio Borda, Ricardo; Silvera-Arcos, Susan

doi:10.1016/j.aej.2023.11.015

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Cited by 7 publications

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“…Convolutional neural networks (CNNs) can be used to extract data features. Many researchers are combining BiLSTM with CNNs for various fields [34][35][36], such as recognition [37,38] and prediction [39]. The selection of hyperparameters is crucial for the performance of the model.…”

Section: Introductionmentioning

confidence: 99%

DBO-CNN-BiLSTM: Dung Beetle Optimization Algorithm-Based Thrust Estimation for Micro-Aero Engine

Lei,

Huang,

Chen

et al. 2024

Aerospace

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Thrust constitutes a pivotal performance parameter for aircraft engines. Thrust, being an indispensable parameter in control systems, has garnered significant attention, prompting numerous scholars to propose various methods and algorithms for its estimation. However, research methods for estimating the thrust of the micro-turbojet engines used in unmanned aerial vehicles are relatively scarce. Therefore, this paper proposes a thrust estimator for micro-turbojet engines based on DBO (dung beetle optimization) utilizing bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN). Furthermore, the efficacy of the proposed model is further validated through comparative analysis with others in this paper.

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