Phishing emails remain a primary vector for cyberattacks, necessitating advanced detection mechanisms. Existing studies often focus on limited datasets or a small number of models, lacking a comprehensive evaluation approach. This study develops a novel framework for implementing and testing phishing email detection models to address this gap. A total of fourteen machine learning (ML) and deep learning (DL) models are evaluated across ten datasets, including nine publicly available datasets and a merged dataset created for this study. The evaluation is conducted using multiple performance metrics to ensure a comprehensive comparison. Experimental results demonstrate that DL models consistently outperform their ML counterparts in both accuracy and robustness. Notably, transformer-based models BERT and RoBERTa achieve the highest detection accuracies of 98.99% and 99.08%, respectively, on the balanced merged dataset, outperforming traditional ML approaches by an average margin of 4.7%. These findings highlight the superiority of DL in phishing detection and emphasize the potential of AI-driven solutions in strengthening email security systems. This study provides a benchmark for future research and sets the stage for advancements in cybersecurity innovation.

Early and accurate diagnosis of lung diseases is essential for effective treatment and patient management. Conventional diagnostic models trained on a single data type often miss important clinical information. This study explored a multimodal deep learning framework that integrates cough sounds, chest radiograph (X-rays), and computed tomography (CT) scans to enhance disease classification performance. Two fusion strategies, early and intermediate fusion, were implemented and evaluated against three single-modality baselines. The dataset was collected from different sources. Each dataset underwent preprocessing steps, including noise removal, grayscale conversion, image cropping, and class balancing, to ensure data quality. Convolutional neural network (CNN) and Extreme Inception (Xception) architectures were used for feature extraction and classification. The results show that multimodal learning achieves superior performance compared with single models. The intermediate fusion model achieved 98% accuracy, while the early fusion model reached 97%. In contrast, single CXR and CT models achieved 94%, and the cough sound model achieved 79%. These results confirm that multimodal integration, particularly intermediate fusion, offers a more reliable framework for automated lung disease diagnosis.

The Convolution Neural Network (CNN) is the most widely used deep learning architecture as it has broken most world records for recognition tasks. Facial Expression Recognition (FER) systems that use classical feature-based techniques, especially CNN’s, is best for classifying images. This paper used three CNN-based methods, which are VGG-16, Inception-v3, and Resnet50-V2 network architectures, to classify facial expressions into seven classes of emotions: happy, angry, neutral, sad, disgust, fear, and surprise. The face expression dataset from Kaggle and JAFFE dataset were used to compare the accuracy between the three architectures to find the pretrained network that best classifies models. The results showed that VGG-16 network architecture produced a higher accuracy (93% in JAFFE and 54% in Kaggle) than the other architectures.