SMS spam is a pervasive issue that affects millions worldwide, leading to significant inconvenience, time wastage, and potential financial scams. Given the prevalence and potential harm, accurate and real-time detection of SMS spam is crucial. This paper proposes a novel approach to SMS spam detection involving five steps: preprocessing, feature extraction, feature fusion, feature selection, and classification. Our model is designed to simultaneously capture local, temporal, and global text message features using a hybrid deep learning model to enhance feature representation. We evaluated our model using the UCI dataset, comparing it with traditional and deep learning algorithms such as RF and BERT using cross-validation to ensure the robustness of our results. Our proposed method exhibited superior performance, achieving a good accuracy of 99.56%, surpassing other methods. The effectiveness of this method in SMS spam detection proved its potential for real-world implementation, where it could substantially mitigate the prevalence and impact of SMS spam.

Facial expression recognition (FER) is a highly active field with applications in computer vision, human-computer interaction, security, and computer graphics animation. Recent advancements in deep learning and machine learning have increased interest in utilizing these techniques for accurate facial expression classification. This paper presents a comparative study that evaluates the performance of deep learning and machine learning as classifiers in FER systems, specifically after data fusion. Data fusion techniques combine and integrate multiple sources of information, aiming to enhance the overall classification accuracy by extracting two types of features using geometrical and appearance features trained using two types of convolutional neural networks. The feature outputs of these networks are fused to create a final feature vector for the classification process. The study evaluates the performance of deep learning on two benchmark datasets, the extended Cohn-Kanade (CK+) and Oulu-CASIA datasets, to assess the performance of deep learning. As a point of comparison, the traditional machine learning approach based on the support vector machine (SVM) is also evaluated on the same datasets. Performance metrics such as classification accuracy, precision, recall, and F1-score are utilized. The results obtained from the study highlight the strengths and limitations of both deep learning and machine learning techniques when employed as classifiers in FER systems. Notably, the experimental results demonstrate that the deep learning approach significantly outperforms the baseline methods, achieving an increase in recognition accuracy of 5.22% for the CK+ and 3.07% for the Oulu-CASIA dataset.