Intrusion Detection System (IDS) is a crucial security mechanism for protecting computer networks from cyber-attacks. Deep learning models have the potential to detect attack types by leveraging their ability to learn and extract features from large volumes of data. In this study, we compare the performance of four different deep learning algorithms for IDS: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, and bidirectional GRU. We evaluate the attack prediction accuracy for three types of attacks: Denial of Service (DoS), Generic, and Exploits. We vary each algorithm's range parameter and epochs and determine the best parameter combination sets for achieving the highest accuracy. Our experimental results demonstrate that increased range parameters influence the accuracy of LSTM, bi-LSTM, and Bi-GRU models. Ultimately, GRU proved to have the most outstanding performance among the four algorithms tested.

This study utilizes vibration-based signal analysis as a non-destructive testing technique that involves analyzing the vibration signals produced by a structure to detect possible defects or damage. The study aims to employ deep learning models to identify defects in a 3D-printed cantilever beam by analyzing the beam’s tip displacement given a random input signal generated by an electromagnetic shaker. This study is focused on the output signal without any information of the random input, which is common for structural health monitoring applications in practice. Additionally, the study has revealed that the number of times the test set is applied to the trained model significantly impacts the accuracy of the model’s consistent predictions.

This study proposes a visualization framework for bottlenose dolphin (Tursiops truncatus) whistle classification using statistical signal properties such as slope, standard deviation, kurtosis, skewness, minimum and maximum frequency, frequency range, absolute value, and duration. For each whistle, all statistical properties are stored in a MySQL database, one of the relational databases, so that users can classify whistles. Moreover, correlation values are provided in a heat map to examine the similarity of whistles among the population. This framework will allow marine mammal researchers to determine the whistle properties of individual bottlenose dolphins. Researchers can then investigate behavioral and ecological questions such as (i) does whistle structure differs among dolphin populations (ii) and does anthropogenic noise affects whistle structure.

As many artificial neural libraries are developing the deep learning algorithm and implementing it became accessible to anyone. This study points out the disparity of performance in deep learning models such as convolutional neural networks (CNN) when implemented with different artificial neural libraries. Libraries such as Keras, Pytorch, and MXnet was utilized for each three CNN model then binary image classification was done based on the Dogs vs. Cats dataset from Kaggle. With using 75% of the dataset as the training set and the rest of 25% as a testing set, and as a result, each CNN model gave a different F1 score value and accuracy.

Structural health monitoring is an area of growing interest and is worthy of new and innovative approaches. Since the automatic diagnosis of structures is very complex and challenging, recent research to apply deep learning techniques has been actively conducted. In this study, we assumed that a PLA beam copied by 3D printing is the smallest unit constituting a complex structure and applied GRU to detect defects. To set the defect point of the beam, a total of four holes were drilled at regular intervals, and then a mass was attached. Signals at different locations were collected through a vibrator and trained through GRU, and the results were compared in terms of RMSE value. As a result of this experiment, we checked the defect by inputting test data into the trained model and were able to measure the defect degree of the PLA beam with a weighted average F1 score of 84%.

Vibration-based methods have received considerable attention in structural condition monitoring applications. We have proposed a model to detect damaged points of a target structure using the GRU model and classify the 0.84 overall accuracy. To increase the model's accuracy in this research, we propose an ensemble deep learning model using LSTM and bi-directional LSTM incorporated with GRU. Each model predicted its RMSE trend and combined the damage estimation results from both models, which are mostly close to the true damage locations. As a result of synthesizing the three algorithms, the damage point of the cantilever beam was found with an accuracy of 0.88 and a misclassification rate of 0.12. The results indicate that the proposed combined approach provides enhanced reliability than a single algorithm.