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Novel hybrid DNN approaches for speaker verification in emotional and stressful talking environments
Shahin Ismail Nassif Ali Bou Nemmour Nawel Elnagar Ashraf Alhudhaif Adi Polat Kemal 《Neural computing & applications》2021,33(23):16033-16055
Neural Computing and Applications - In this work, we conducted an empirical comparative study of the performance of text-independent speaker verification in emotional and stressful environments.... 相似文献
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Qureshi Kashif Naseer Alhudhaif Adi Azahar Moeen Javed Ibrahim Tariq Jeon Gwanggil 《Wireless Personal Communications》2022,126(4):2825-2839
Wireless Personal Communications - The concept of connected devices is effectively integrated to create heterogeneous wireless networks like the Internet of Things (IoT). These networks are using... 相似文献
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Adi Alhudhaif Ammar Saeed Talha Imran Muhammad Kamran Ahmed S. Alghamdi Ahmed O. Aseeri Shtwai Alsubai 《计算机系统科学与工程》2022,40(1):223-235
Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme, particle swarm optimization (PSO), was employed for autonomous vehicle classification. The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented. The trained model was classified using several classifiers; however, the Cubic SVM (CSVM) classifier was found to outperform the others in both time consumption and accuracy (94.8%). The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accuracy (94.8%) but also in terms of training time (82.7 s) and speed prediction (380 obs/sec). 相似文献
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