Scopus Research — Zahraa Hazim Obaid

Determining the type of modulation is an important task in military communications, satellite communications systems, and submarine communications. In this study, a new digital modulation classification model is presented for detecting various types of modulated signals. The continuous wavelet transform is used in the first step to create a visual representation of the spectral density of the frequencies of the modulation signals in a scalogram image. The subsequent stage involves the utilization of a deep convolutional neural network for feature extraction from the scalogram images. In the next step, the best features are chosen using the MRMR algorithm. MRMR algorithm increases the classification speed and the ability of interpret the classification model by reducing the dimensions of the features. In the fourth step, the modulations are classified using the group learning technique. In the simulations, modulated signals with different amounts of noise with SNR from 0 to 25 dB are considered. Then, accuracy, precision, recall, and F1-score are used to evaluate the performance of the proposed method. The results of the simulations prove that the proposed model with achieving above 99.9% accuracy performs well in the presence of different amounts of noise and provides better performance than other previous studies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

Neural networks are effectively used in a variety of applications including data mining. The neural network can realize different complex nonlinear functions by making them attractive to identify a system. One of the most important issues of classifying datasets through neural networks is the formation of an ideal network, that consists of many successive steps like set parameters. Perhaps the most prominent parameter is the learning rate. Indeed, choosing an appropriate learning rate value is one of the things that greatly helps to control the overall network performance. In contrast, any inappropriate value for the learning rate negatively affects the classification model and can therefore destabilize the model’s performance and thus seriously deteriorate its quality. This paper presents a new model by adopting a cyclical learning rate instead of using a constant value for training deep neural networks by Multi-Layer Perceptron (MLP) architecture. This model is tested on various real-world datasets; Electricity, NSL- KDD, and four sub-datasets of HuGaDB (HuGaDB-01-01, HuGaDB-05- 12, HuGaDB-13-11, and HuGaDB-14-05). The proposed model achieves an accuracy of, 89.57%, 99.12%, 99.2%, 97.83%, 96.19%, and 99.85% for these datasets respectively. Accordingly, the proposed model outperforms many previous models. As a result, the deep neural network models can be more effective when they adopt an appropriate value for the learning rate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.