Computing Paradigm
Computing paradigms are undergoing a significant shift, driven by the need for more efficient and powerful computation across diverse applications. Current research focuses on integrating quantum computing with classical methods, particularly in machine learning, exploring architectures like quantum-hybrid support vector machines and generative adversarial networks to improve performance and address privacy concerns through techniques like federated learning and fully homomorphic encryption. This research is crucial for advancing fields like artificial intelligence, cybersecurity, and scientific modeling, offering the potential for faster, more efficient, and privacy-preserving solutions.
Papers
Spiking Neural Streaming Binary Arithmetic
James B. Aimone, Aaron J. Hill, William M. Severa, Craig M. Vineyard
The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms
Javad Hassannataj Joloudari, Sanaz Mojrian, Hamid Saadatfar, Issa Nodehi, Fatemeh Fazl, Sahar Khanjani shirkharkolaie, Roohallah Alizadehsani, H M Dipu Kabir, Ru-San Tan, U Rajendra Acharya