Recognizing emotions from speech using machine learning algorithms has become an active research topic lately as a result of the demand for more human interactive applications. Emotion recognition systems are mostly implemented in German, English, Spanish, Dutch, Danish, and other European and Asian languages due to the availability of datasets for these languages. However, for Arabic, there is an extremely limited number of available speech emotion datasets. Therefore, in this paper studies emotion recognition based on Arabic Saudi dialect spoken data. The dataset was created from freely available YouTube videos and labeled using four perceived emotions: anger, happiness, sadness, and neutral. Various spectral features such as the mel-frequency cepstral coefficient (MFCC) and mel spectrogram, were extracted, and then the classification methods support vector machine (SVM), multi-layer perceptron (MLP), and k-nearest neighbor (KNN) were applied. The results were discussed, analyzed, and compared between the three models using different feature extractions. Experiments showed that SVM obtained the best accuracy result with 77.14%, demonstrating improvement in Arabic speech emotion recognition for this classification method.

The transaction and market of bitcoin is volatile, meaning it’s uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction of timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a “well-formulated” statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency’s performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.

The rising adoption of social media has led to the widespread dissemination of online textual data. Arabic is among the top five most popular languages worldwide (Arabic is spoken by a total of about 360.2 million people worldwide as a native language). In this regard, Arabic-text data available on social media are presented using different Arabic dialects, such as the Gulf, Iraqi, Egyptian, Levantine, and North Africa dialects. Particularly, identifying the Arabic dialect used in text is of significant value for several natural language processing tasks, such as machine translation, text generation, word correction, and information retrieval. Arabic-dialect identification is a multiclass classification problem in which classes represent different Arabic dialects. In this study, we investigated the performance of two bidirectional deep learning models for Arabic-dialect classification: MARBERT and ARBERT. We analyzed the performance of the models on two publicly available datasets: the Arabic Online Commentary dataset and the Social Media Arabic Dialect Corpus. Extensive experiments were conducted, encompassing binary dialect classification, three-way dialect classification, and multi-way dialect classification. The results indicate that MARBERT consistently achieved higher F1-scores than ARBERT, which can be attributed to the significant differences between the two models, including their architectures, training mechanisms, and data sources.

This paper highlights how the proliferation of online transactions, especially those involving the use of credit cards, has resulted in the emergence of new security flaws that pose threats to customers and enterprises worldwide. E-commerce and other forms of online monetary transactions have become essential in the manufacturing and service sectors, propelling the global economy. The widespread and dependent connectivity of mobile payment systems using credit card transactions presents chances for fraud, risk, and security breaches. In light of the importance of accurately predicting fraud incidents through payment procedures, this study investigated the credit card payment methods used for movie tickets, using the machine learning logistic regression method to analyze and predict such incidents. This study used a dataset from cinema ticket credit card transactions made in two days of September 2013 by European cardholders, including 284,807 transactions out of which 492 were fraudulent purchases. The results of the proposed method showed a prediction accuracy of 99%, proving its high prediction performance.

Identifying hidden payloads in images has become increasingly critical as steganography continues to challenge traditional security measures. This paper introduces a deep learning framework for both the detection (binary classification) and fine-grained classification (multi-class) of steganographic payloads embedded using Least Significant Bit (LSB) techniques. The proposed system distinguishes between benign images and stego images containing five different payload types: HTML, JavaScript, PowerShell, URLs, and Ethereum-related data. To achieve this, we systematically evaluate various architectures, including a custom Convolutional Neural Network (CNN), hybrid CNN-GRU and CNN-LSTM models, and a Vision Transformer (ViT) at different input resolutions using 5-fold cross-validation. Our experiments reveal a critical finding: image resizing significantly degrades detection performance, as subtle LSB artifacts are often corrupted. While our custom CNN model achieved the highest mean cross-validation accuracy (0.9702), the hybrid CNN-GRU model demonstrated superior generalization on the held-out test set and external dataset, achieving a multi-class accuracy of 0.98 on the testset and 0.97 on the external. This result highlights the advantage of combining the CNN’s spatial feature extraction with the GRU’s ability to model sequential dependencies for robust payload identification on unseen data.