In recent years data science has been applied in a variety of real-life applications such as human-computer interaction applications, computer gaming, mobile services, and emotion evaluation. Among the wide range of applications, speech emotion recognition (SER) is also an emerging and challenging research topic. For SER, recent studies used handcrafted features that provide the best results but failed to provide accuracy while applied in complex scenarios. Later, deep learning techniques were used for SER that automatically detect features from speech signals. Deep learning-based SER techniques overcome the issues of accuracy, yet there are still significant gaps in the reported methods. Studies using lightweight CNN failed to learn optimal features from composite acoustic signals. This study proposed a novel SER model to overcome the limitations mentioned earlier in this study. We focused on Arabic vocal emotions in particular because they received relatively little attention in research. The proposed model performs data augmentation before feature extraction. The 273 derived features were fed as input to the transformer model for emotion recognition. This model is applied to four datasets named BAVED, EMO-DB, SAVEE, and EMOVO. The experimental findings demonstrated the robust performance of the proposed model compared to existing techniques. The proposed SER model achieved 95.2%, 93.4%, 85.1%, and 91.7% accuracy on BAVED, EMO-DB, SAVEE, and EMOVO datasets respectively. The highest accuracy was obtained using BAVED dataset, indicating that the proposed model is well suited to Arabic vocal emotions.
The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a 5468 CMC, 2023, vol.74, no.3 maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.
The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this study, an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning (AATC-HTHDL) model is proposed. The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text. The first step in the proposed model is to pre-process the input data to transform it into a useful format. The Term Frequency-Inverse Document Frequency (TF-IDF) model is applied to extract the feature vectors. Next, the Convolutional Neural Network with Recurrent Neural Network (CRNN) model is utilized to classify the Arabic text. In the final stage, the Crow Search Algorithm (CSA) is applied to fine-tune the CRNN model's hyperparameters, showing the work's novelty. The proposed AATC-HTHDL model was experimentally validated under different parameters and 5448 CMC, 2023, vol.74, no.3 the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.
With a population of 440 million, Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users. 11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day. In order to develop a classification system for the Arabic language there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective. In this view, this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification (DSOCDBN-STC) model on Arabic Corpus. The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media. The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage. Besides, the DSOCDBN-STC model involves CDBN based classification model for Arabic short text. At last, the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method. To establish the enhanced performance of the DSOCDBN-STC model, a wide range of simulations have been performed. The simulation results confirmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.
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