Person identification is a pivotal challenge in the security domain, with important and impactful applications such as identifying crime suspects and locating missing persons. One convenient person identification method is gait identification, where individuals are identified by their unique walking style. However, traditional methods of gait identification are often affected by variations in appearance and occlusion. This work introduces a novel and robust spatiotemporal kinematics‐informed non‐invasive gait identification (STONI‐GID) method that uses human pose estimation, occlusion state estimation and deep machine learning. Furthermore, unlike some existing methods, we demonstrate that our method remains unaffected by everyday appearance changes, environment, or viewing angle. Our approach achieved identification accuracy of up to 98.66% when evaluated using our primary dataset of 65 diverse participants in real‐world environments. Moreover, the model outperformed existing methods during cross‐dataset validation on the large Southampton dataset and the Gait Recognition Image and Depth Dataset (GRIDDS), achieving identification accuracies of 97.68% and 99.12%, respectively. Our findings will particularly advance the research frontiers of real‐world gait identification and impact interdisciplinary areas of security and healthcare applications.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder causing cognitive decline and memory loss. With its significant impact on individuals’ lives, AD is the most prevalent form of dementia, contributing to 60-80% of all dementia cases. At the same time, symptoms may not surface until years later, making early detection vital for effective intervention. Thus, this work presents an approach to early AD detection by integrating Genome-Wide Association Studies (GWAS) with deep learning models and Explainable Artificial Intelligence (XAI). First, different classical machine learning models are developed for AD, and a Convolutional Neural Network (CNN) model is trained using the AD GWAS dataset obtained from the AD neuroimaging initiative. We then employ transfer learning to train our CNN model as a base model over the ADNI dataset. In addition, XAI methods are used to interpret the transfer learning model decision. Acknowledging the well-known limitation that classical machine learning is not inherently a generic model. The results from this study will help determine the most critical genetic markers associated with AD and provide transparency in understanding the deep learning model decisions.

Early prediction of patients in need of admission to the intensive care unit (ICU) is essential for maximizing the use of available hospital resources and enhancing the quality of patient care outcomes. This work uses the Covid19MPD Dataset to predict ICU admissions based on various machine learning techniques such as Random Forest, Support Vector Machine, Gradient Boosting, and Multi-Layer Perceptron alongside Explainable Artificial Intelligence (XAI) approaches. Our findings show that the Gradient Boosting model achieved the best accuracy at 97.49% and an F1 score of 71% for ICU admissions. Notably, the study finds that age and pneumonia are important predictors, with patients 45 years and older who come with COVID-19 and pneumonia having a much higher chance of needing ICU care. These findings highlight how important it is to use machine learning models in clinical settings in order to improve ICU admission prediction and facilitate prompt medical intervention.

Flamingos are known for their vibrant pink and reddish hues, which are not merely aesthetic but indicative of their overall health and diet. These colors are derived from carotenoid pigments in their food sources, making coloration a vital marker for monitoring their well-being and environmental conditions. This study introduces a two-stage classification methodology designed to safeguard flamingo populations by leveraging deep learning techniques. Convolutional Neural Networks (CNNs) are used for both shape and color detection, ensuring accurate identification of flamingos and insights into their health status. Simulation results demonstrated the CNNs model’s effectiveness, making it a valuable resource for wildlife conservation efforts aimed at preserving flamingo habitats. The first stage employs digital classification filters to distinguish flamingo images from other species, achieving an accuracy of 97.52%, while the second stage refines these detections through color analysis with an accuracy of 86.27%. This approach promises to mark a significant advancement in wildlife conservation, offering reliable methods for assessing and managing flamingo populations in their natural environments.