• Proposed an optimization-driven resource orchestration algorithm that enhances admission control and ensures Quality of Service (QoS) in SDN-based network slicing. • Introduced a Partial Admission Control (PAC) strategy, dynamically allocating network resources based on priority and real-time availability, addressing the limitations of rigid binary admission models. Efficient resource orchestration is essential for ensuring high Quality of Service (QoS) and reliability in Software-defined Networks (SDNs). This paper introduces an optimization-based algorithm that integrates Lagrangian Relaxation (LR) and Queueing Theory to enhance admission control and priority scheduling in SDNs. The proposed approach overcomes the limitations of traditional binary admission control methods by enabling Partial Admission Control (PAC), which allows more flexible resource allocation. The system’s performance is significantly improved through the use of non-preemptive and preemptive priority scheduling, while LR techniques effectively manage complex network conditions. Specifically, the proposed Bisection-Search (B-S) heuristic leverages the Lagrangian multipliers generated during the optimization process to intelligently guide resource allocation, consistently producing high-quality feasible solutions ( Z primal ). These solutions are validated against the theoretical bound ( Z LR ) provided by the LR method, demonstrating a provably small duality gap. The proposed algorithm is evaluated through extensive simulations across diverse network scales, traffic loads, and delay constraints, demonstrating substantial improvements in network performance and service differentiation. These results provide a comprehensive analysis of the performance envelope of the proposed framework, highlighting the trade-offs between solution quality, computational complexity, and network scale. The study offers an adaptive and mathematically grounded solution, demonstrating its effectiveness in complex, high-contention networking environments.

Biometric recognition plays an increasingly pivotal role in cybersecurity, where the CIA triad, Confidentiality, Integrity, and Availability, forms the cornerstone of information security, with authentication as a critical yet challenging component. This paper presents the Biometric Multi-modal Authentication System using Geometric Programming (BMMA-GPT), tailored for deployment in Fast IDentity Online (FIDO/FIDO2)-enabled environments and Zero Trust Architectures (ZTA). The system employs a dual-threshold mechanism integrated with Defense-in-Depth (DiD) strategies to simultaneously enhance accuracy, efficiency, and security. The underlying optimization problem is formulated as a mathematical programming task and reformulated into a Geometric Programming (GP) model to efficiently compute optimal biometric permutations and verification thresholds under constrained estimation errors. BMMA-GPT enables the flexible integration of multiple biometric modalities, allowing dynamic adjustments to meet both individual user profiles and organizational security requirements. It achieves a high Area Under Curve (AUC) of approximately 0.99 while maintaining authentication latency under 1.5 seconds. This design supports Chief Information Security Officers (CISOs) in configuring tailored authentication processes with minimal computational cost, enhancing resilience against spoofing attacks and ensuring seamless user experience. By aligning biometric verification with DiD principles and GP-based optimization, the proposed framework offers a scalable and robust solution for identity authentication in complex digital ecosystems.

This paper tackles key challenges in Software-Defined Networking (SDN) by proposing a novel approach for optimizing resource allocation and dynamic priority assignment using OpenFlow’s priority field. The proposed Lagrangian relaxation (LR)-based algorithms significantly reduces network delay, achieving performance management with dynamic priority levels while demonstrating adaptability and efficiency in a sliced network. The algorithms’ effectiveness were validated through computational experiments, highlighting the strong potential for QoS management across diverse industries. Compared to the Same Priority baseline, the proposed methods: RPA, AP–1, and AP–2, exhibited notable performance improvements, particularly under strict delay constraints. For future applications, the study recommends expanding the algorithm to handle larger networks, integrating it with artificial intelligence technologies for proactive resource optimization. Additionally, the proposed methods lay a solid foundation for addressing the unique demands of 6G networks, particularly in areas such as base station mobility (Low-Earth Orbit, LEO), ultra-low latency, and multi-path transmission strategies.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring substantial expertise for interpretation. This research introduces an innovative strategy that integrates two-dimensional (2D) and three-dimensional (3D) deep learning models within a federated learning (FL) framework for precise segmentation of liver and tumor regions in medical images. The study utilized 131 CT scans from the Liver Tumor Segmentation (LiTS) challenge and demonstrated the superior efficiency and accuracy of the proposed Hybrid-ResUNet model with a Dice score of 0.9433 and an AUC of 0.9965 compared to ResNet and EfficientNet models. This FL approach is beneficial for conducting large-scale clinical trials while safeguarding patient privacy across healthcare settings. It facilitates active engagement in problem-solving, data collection, model development, and refinement. The study also addresses data imbalances in the FL context, showing resilience and highlighting local models' robust performance. Future research will concentrate on refining federated learning algorithms and their incorporation into the continuous implementation and deployment (CI/CD) processes in AI system operations, emphasizing the dynamic involvement of clients. We recommend a collaborative human-AI endeavor to enhance feature extraction and knowledge transfer. These improvements are intended to boost equitable and efficient data collaboration across various sectors in practical scenarios, offering a crucial guide for forthcoming research in medical AI.

Band selection for remotely sensed image data is an effective means to mitigate the curse of dimensionality. Many criteria have been suggested in the past for optimal band selection. In this paper, a joint band-prioritization and band-decorrelation approach to band selection is considered for hyperspectral image classification. The proposed band prioritization is a method based on the eigen (spectral) decomposition of a matrix from which a loading-factors matrix can be constructed for band prioritization via the corresponding eigenvalues and eigenvectors. Two approaches are presented, principal components analysis (PCA)-based criteria and classification-based criteria. The former includes the maximum-variance PCA and maximum SNR PCA, whereas the latter derives the minimum misclassification canonical analysis (MMCA) (i.e., Fisher's discriminant analysis) and subspace projection-based criteria. Since the band prioritization does not take spectral correlation into account, an information-theoretic criterion called divergence is used for band decorrelation. Finally, the band selection can then be done by an eigenanalysis based band prioritization in conjunction with a divergence-based band decorrelation. It is shown that the proposed band-selection method effectively eliminates a great number of insignificant bands. Surprisingly, the experiments show that with a proper band selection, less than 0.1 of the total number of bands can achieve comparable performance using the number of full bands. This further demonstrates that the band selection can significantly reduce data volume so as to achieve data compression.