The network slicing of physical infrastructure is required for fifth-generation mobile networks to make significant changes in how service providers deliver and defend services in the face of evolving end-user performance requirements. To perform this, a fast and secure slicing technique is employed for node allocation and connection establishment, which necessitates the usage of a large number of domain applications across the network. PROMETHEE-II and SLE algorithms were used in this study’s approach to network design for node allocation and link construction, respectively. The PROMETHEE-II approach takes into account a variety of node characteristics while constructing a node importance rank array (NIRA), including the node capacity, bandwidth of neighboring connections, degree of the node, and proximity centrality among others. The SLE method is proposed to record all possible link configurations for the network slice request (NSR) nodes to guarantee that the shortest path array (SPA) of the NSR has a high acceptance rate. Performance metrics such as the service revenue and acceptance ratio were considered to evaluate the effectiveness of the suggested approach. The effectiveness of network slicing has been further examined under different infrastructure models to determine whether a small-world network structure is beneficial to 5G network. For each scenario, simulations were carried out and the results were compared to previously published findings from other sources.
Biometric cryptosystem is an integrated system of cryptography with biometric features which is considered to be a promising component of template security. This paper presents a novel method for biometric cryptosystem based on fuzzy vault using palmprint features. Initially, pre-processing is performed to remove noise in palmprint enrolled image and to pre-align image automatically.Bottom-hat filtering is employed for the feature extraction from the palmprint images. A polynomial is evaluated from the secret key and this polynomial is used to generate projections from palmprint features. Fuzzy vault is created by combining projected points with randomly generated dummy or chaff points. During decoding process, matching scores are calculated by comparing the query feature and stored vault. The vault is successfully decoded when the matched scores should be greater than a predefined threshold value. Experiments were conducted on CASIA database.Simulation result demonstrates that the proposed method gives better performance than other methods and improves the robustness against some security attacks. KEYWORDSbrute force attack, fuzzy vault, palmprint, principal line, security analysis, template protection INTRODUCTIONBiometrics is one of the reliable approaches to measure human individuality with the end goal of authenticating or identifying the character of a core. Palmprint, fingerprint, iris, palm vein, retina, facial features, voice, handwriting, and signatures are commonly used characteristics in person recognition. 1 Among these characteristics, palmprint identification has many advantages: higher acceptance rate, low-resolution image, stable line features, rich texture information, and low intrusiveness. Palms are large in size and palmprint has the features like principal lines, wrinkles, delta point, datum point, minutiae, and creases (a), as shown in Figure 1. 2 Template is a digital representation of the unique feature set that has been obtained from the biometric sample during enrolment process and stored in the system database. It represents a strong link between a person, his/her identity, and these characteristics cannot be easily forged or lost. 3 However, biometric based identification system requires huge volume of biometric information that leads to identity theft and leakage of privacy. Biometric template security has drawn more attention from the researchers. Both biometrics and cryptography have combined in order to address problem of biometric template protection. Various biometric template protection methods have been found in the literature. [4][5][6][7] Biometric cryptosystems have been emerged as a promising tool for biometric template protection. These systems integrate biometric features with the transformed version of cryptographic keys to generate secure biometric template. In biometric cryptosystem, some public information named as helper data is generated. 8 Based on the usage of helper data, biometric cryptosystems can be categorized into two methods, namely, key generation and key...
Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.
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