Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that has gained significant attention in recent years due to its increasing prevalence and profound impact on individuals, families, and society as a whole. In this study, we explore the use of different machine learning classifiers for the accurate detection of ASD in children, adolescents, and adults. Furthermore, we conduct feature reduction to identify key features contributing to ASD classification within each age group using Cuckoo Search Algorithm. Logistic Regression has the highest accuracy compared to the other two models.

In the area of computer vision, one of the most difficult and challenging tasks is facial emotion recognition. Facial expression recognition (FER) stands out as a pivotal focus within computer vision research, with applications in various domains such as emotion analysis, mental health assessment, and human–computer interaction. In this study, we explore the effectiveness of ensemble methods that combine pre-trained deep learning architectures, specifically AlexNet, ResNet50, and Inception V3, to enhance FER performance on the FER2013 dataset. The results from this study offer insights into the potential advantages of ensemble-based approaches for FER, demonstrating that combining pre-trained architectures can yield superior recognition outcomes.

The application of Deep Learning Techniques in biometrics has grown significantly during the last decade. The use of deep learning models in ear biometrics is restricted due to the lack of large ear datasets. Researchers employ transfer learning based on several pretrained models to overcome the limitations. For the unconstrained AWE ear dataset, traditional Machine Learning (ML) techniques and hand-crafted features fall short of providing a good recognition accuracy. This paper evaluates the influence of separating left and right ears and the effect of occlusion on the recognition accuracy in AWE dataset. The left and right ear of a person need not be identical. A study by separating the left and right ear into two different datasets is carried out with the pretrained ResNet50 based model. There is a remarkable increase in accuracy when the left and right ear images are independently considered. A new data augmentation technique, incorporating occlusion, is also proposed and experimented with the ResNet50 based model.

An individual's authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition