No abstract

No abstract

This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA to solve problems with continuous and binary decision variables. The SCMWOA algorithm is first tested on nineteen datasets from the UCI Machine Learning Repository with different number attributes, instances, and classes for feature selection. It is then employed to solve several benchmark functions and classical engineering case studies. The SCMWOA algorithm is applied for solving constrained optimization problems. The two tested examples are the welded beam design and the tension/compression spring design. The results emphasize that the SCMWOA algorithm outperforms several comparative optimization algorithms and provides better accuracy compared to other algorithms. The statistical analysis tests, including one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum, confirm that the SCMWOA algorithm performs better.

No abstract

No abstract

No abstract

The design of microstrip antennas is a complex and time-consuming process, especially the step of searching for the best design parameters. Meanwhile, the performance of microstrip antennas can be improved using metamaterial, which results in a new class of antennas called metamaterial antenna. Several parameters affect the radiation loss and quality factor of this class of antennas, such as the antenna size. Recently, the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning, which presents a better alternative to simulation tools and trialand-error processes. However, the prediction accuracy depends heavily on the quality of the machine learning model. In this paper, and benefiting from the current advances in deep learning, we propose a deep network architecture to predict the bandwidth of metamaterial antenna. Experimental results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error (MSE). In addition, the proposed model is compared with current competing approaches that are based on support vector machines, multi-layer perceptron, K-nearest neighbors, and ensemble models. The results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.

Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be integrated with other systems better than Arabic ASR methods without diacritics. In this work, the application of state-of-the-art end-to-end deep learning approaches is investigated to build a robust diacritised Arabic ASR. These approaches are based on the Mel-Frequency Cepstral Coefficients and the log Mel-Scale Filter Bank energies as acoustic features. To the best of our knowledge, end-to-end deep learning approach has not been used in the task of diacritised Arabic automatic speech recognition. To fill this gap, this work presents a new CTC-based ASR, CNN-LSTM, and an attention-based end-to-end approach for improving diacritisedArabic ASR. In addition, a word-based language model is employed to achieve better results. The end-to-end approaches applied in this work are based on state-of-the-art frameworks, namely ESPnet and Espresso. Training and testing of these frameworks are performed based on the Standard Arabic Single Speaker Corpus (SASSC), which contains 7 h of modern standard Arabic speech. Experimental results show that the CNN-LSTM with an attention framework outperforms conventional ASR and the Joint CTC-attention ASR framework in the task of Arabic speech recognition. The CNN-LSTM with an attention framework could achieve a word error rate better than conventional ASR and the Joint CTC-attention ASR by 5.24% and 2.62%, respectively.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.