Information security has become an inseparable aspect of the field of information technology as a result of advancements in the industry. Authentication is crucial when it comes to dealing with security. A user must be identified using biometrics based on certain physiological and behavioral markers. To validate or establish the identification of an individual requesting their services, a variety of systems require trustworthy personal recognition schemes. The goal of such systems is to ensure that the offered services are only accessible by authorized users and not by others. This case study provides enhanced accuracy for multimodal biometric authentication based on voice and face hence, reducing the equal error rate. The proposed scheme utilizes the Gaussian mixture model for voice recognition, FaceNet model for face recognition and score level fusion to determine the identity of the user. The results reveal that the proposed scheme has the lowest equal error rate in comparison to the previous work.

The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed–trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate.

Recently, thyroid disease has been a leading cause of mortality, underscoring the importance of early diagnosis to mitigate its impact. Researchers have randomly employed static selection ensemble methods aiming to forecast the disease in its initial stages. However, the use of such ensemble methods in healthcare diagnosis poses challenges related to performance consistency and potential mismatches with new data characteristics. Hence, this paper proposes a novel approach by introducing the Dynamic Selection Hybrid Model (DSHM) that leverages the most effective conventional classifiers using an appropriate ensemble method. Instead of going the conventional way, we evaluate various baseline classifiers to demonstrate their impact on the characteristics selected by two robust feature selection techniques. This evaluation employs an explainable AI (XAI) method, Permutation Feature Importance (PFI), and selects the most effective classifiers based on their characteristics impact. Then the selected classifiers are integrated by an appropriate ensemble method, selected based on a comparative evaluation between four efficient ensemble methods. Allowing the proposed DSHM to dynamically adjust its composition based on selecting conditions can potentially achieve robust performance by better adaptability on unseen data. Prior to the training DSHM, the methodology begins by addressing the dataset’s imbalance issue using an effective data balancing method BOO-ST. To demonstrate the superiority of DSHM, various performance evaluation matrices, and a statistical test are employed. The experimental results reveal the effectiveness of our proposed DSHM, outperformed with an impressive 99.33% accuracy. Finally, to enhance transparency, trust, and patient outcomes, we applied the Local Interpretable Model-agnostic Explanations (LIME) to explain DSHM-provided outcomes. With a robust classification performance, our proposed DSHM aims to explain its outcomes, contributing to improved clinical decision-making processes and ultimately enhancing patient care.

Speech can actively elicit feelings and attitudes by using words. It is important for researchers to identify the emotional content contained in speech signals as well as the sort of emotion that resulted from the speech that was made. In this study, we studied the emotion recognition system using a database in Arabic, especially in the Saudi dialect, the database is from a YouTube channel called Telfaz11, The four emotions that were examined were anger, happiness, sadness, and neutral. In our experiments, we extracted features from audio signals, such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), then we classified emotions using many classification algorithms such as machine learning algorithms (Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)) and deep learning algorithms such as (Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM)). Our Experiments showed that the MFCC feature extraction method and CNN model obtained the best accuracy result with 95 %, proving the effectiveness of this classification system in recognizing Arabic spoken emotions.

Background: The techniques differed in many of the literature on the detection of Acute Lymphocytic Leukemia from the blood smear pictures, as the cases of infection in the world and the Kingdom of Saudi Arabia were increasing and the causes of this disease were not known, especially for children, which is a serious and fatal disease. Objective: Through this work we seek to contribute to discover the blood cells affected by Acute Lymphocytic Leukem and to find an effective and fast method and to have the correct diagnosis as the time factor is important in the diagnosis and the initiation of treatment. which is based on one of the deep learning techniques that specialize in very deep networks, the use of one of the CNNs is VGG16. Methods: Detection scheme is implemented by pre-processing, feature extraction, model building, fine tuning method, classification are executed. By using VGG16 pre-trained, and using SVM and MLP classification algorithms in Machine Learning. Results: Our results are evaluated based on criteria, such as Accuracy, Precision, Recall, and F1-Score. The accuracy results for SVM classifier MLP of 77% accuracy at 0.001 learning rate and the accuracy for SVM classifier 75% at 0.005 learning rate. Whereas, the best accuracy result for VGG16 model was 92.27% at 0.003 learning rate. The best validation accuracy result was 85.62% at 0.001 learning rate.