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A methodological and analytical framework for image steganography using evolutionary algorithmsNARA Non-Subscribed
Steganography has been developed with the aim of establishing secure communication in a completely imperceptible manner. In a steganography system, confidential information is embedded within a carrier medium such as an image, audio, or video in such a way that no discernible change occurs in the apparent content of the medium, and an observer or unauthorized attacker is unable to detect the presence of the hidden message. How...
An investigation to the hourly and seasonal network reconfiguration approach in unbalanced power distribution networkNARA Non-Subscribed
Network reconfiguration (NR) serves as an effective mechanism for optimizing electricity delivery by modifying the network topology through sectionalizing and tie-switches. Dynamic reconfiguration is gaining prominence with the commercial use of remotely controlled line switches; however, unbalanced power distribution networks (UPDNs) with diverse customer categories present a significant challenge in identifying optimal confi...
Artificial intelligence for next-generation 6G technologies and networksNARA Non-Subscribed
GeoJitter: a flexible toolkit for region-aware node jittering in spatial networksNARA Non-Subscribed
GeoJitter is an open-source Python package for region-aware randomization of node locations in spatial networks, designed to preserve network structure while mitigating privacy risks and supporting spatial visualization of incomplete or uncertain data. Existing open-source randomization techniques rarely incorporate geospatial boundaries, limiting their applicability for spatial analysis; GeoJitter addresses this gap and provi...
Advancements and challenges in the development of generative adversarial network (GANs) for deep learningNARA Non-Subscribed
Generative adversarial networks (GANs) have reshaped modern deep learning by enabling the creation of high-fidelity synthetic data. This survey distils a decade of progress while adding three fresh dimensions. First, we propose a unified three-layer taxonomy–linking divergence choice, objective loss, and architecture family–that clarifies how theoretical tweaks ripple through training dynamics. Second, we deliver the field’s m...
Quantum conference key agreement with classical advantage distillationNARA Non-Subscribed
Inaugural editorial for Discover NetworksNARA Non-Subscribed