This paper addresses the nurse rostering problem, aiming to create an eight-week cyclic schedule that ensures an equitable distribution of work hours among nurses while adhering to a complex set of constraints. Two different modeling approaches are proposed. The initial model, incorporating strict hourly constraints, proved computationally infeasible using the CPLEX solver. By reformulating the constraints to focus on shift counts that inherently satisfy total working hours, the computational complexity was significantly reduced. The improved model efficiently provides optimal solutions on real data, demonstrating that different modeling techniques can lead to vastly different outcomes in terms of solution time and efficiency.

Palm vein recognition is a biometric method for individuals’ authentication and/or identification based on the unique patterns of veins in the palms of their hands. This paper presents a comprehensive study of the Convolutional Neural Network (CNN) for palm vein recognition. Several CNN architectures, such as VGGNet, AlexNet, and ZFNet, have been studied and adapted by proposing an improved version based on the optimization of their parameters. Two hybrid approaches based on the fusion of the improved versions of these CNN models are proposed. The proposed method was evaluated on near-infrared palm vein images from MS-PolyU database using data augmentation. The experimental results carried out have shown the high accuracy of the proposed method, allowing achieving an accuracy rate of up to 99.72%.

This paper presents a comprehensive study on deep learning for COVID-19 detection using CT-scan images. The proposed study investigates several Conventional Neural Networks (CNN) architectures such as AlexNet, ZFNet, VGGNet, and ResNet, and thus proposed a hybrid methodology base on merging the relevant optimized architectures considered for detecting COVID-19 from CT-scan images. The proposed methods have been assessed on real datasets, and the experimental results conducted have shown the effectiveness of the proposed methods, allowing achieving a higher accuracy up to 99%.