Zobrazeno 1 - 10
of 13 526
pro vyhledávání: '"A Shafique"'
Publikováno v:
Journal of Clinical and Diagnostic Research, Vol 18, Iss 01, Pp 27-30 (2024)
Introduction: Women exhibit a distinct natural history of chronic liver disease compared to men, particularly regarding progression and outcomes. Although liver disease prevalence is generally higher in men, the incidence of non ethanol-related l
Externí odkaz:
https://doaj.org/article/a137143e2af04556b8b23ce026b39cb3
Autor:
Younesi, Abolfazl, Ansari, Mohsen, Ejlali, Alireza, Fazli, Mohammad Amin, Shafique, Muhammad, Henkel, Jörg
Fog computing brings about a transformative shift in data management, presenting unprecedented opportunities for enhanced performance and reduced latency. However, one of the key aspects of fog computing revolves around ensuring efficient power and r
Externí odkaz:
http://arxiv.org/abs/2412.11310
The Dilemma of Random Parameter Initialization and Barren Plateaus in Variational Quantum Algorithms
Autor:
Kashif, Muhammad, Shafique, Muhammad
This paper presents an easy-to-implement approach to mitigate the challenges posed by barren plateaus (BPs) in randomly initialized parameterized quantum circuits (PQCs) within variational quantum algorithms (VQAs). Recent state-of-the-art research i
Externí odkaz:
http://arxiv.org/abs/2412.06462
Hybrid Quantum Neural Networks (HQNNs) have gained attention for their potential to enhance computational performance by incorporating quantum layers into classical neural network (NN) architectures. However, a key question remains: Do quantum layers
Externí odkaz:
http://arxiv.org/abs/2412.04991
Currently, state-of-the-art RL methods excel in single-task settings, but they still struggle to generalize across multiple tasks due to catastrophic forgetting challenges, where previously learned tasks are forgotten as new tasks are introduced. Thi
Externí odkaz:
http://arxiv.org/abs/2412.04847
The rapid advancement in Quantum Computing (QC), particularly through Noisy-Intermediate Scale Quantum (NISQ) devices, has spurred significant interest in Quantum Machine Learning (QML) applications. Despite their potential, fully-quantum QML algorit
Externí odkaz:
http://arxiv.org/abs/2412.04844
Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural networks, of
Externí odkaz:
http://arxiv.org/abs/2412.03220
Predicting loan eligibility with high accuracy remains a significant challenge in the finance sector. Accurate predictions enable financial institutions to make informed decisions, mitigate risks, and effectively adapt services to meet customer needs
Externí odkaz:
http://arxiv.org/abs/2412.03158
This paper introduces the Federated Learning-Quantum Dynamic Spiking Neural Networks (FL-QDSNNs) framework, an innovative approach specifically designed to tackle significant challenges in distributed learning systems, such as maintaining high accura
Externí odkaz:
http://arxiv.org/abs/2412.02293
Autor:
Dutta, Siddhant, Innan, Nouhaila, Yahia, Sadok Ben, Shafique, Muhammad, Neira, David Esteban Bernal
The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often results in performance degradation of the aggregated model, hindering th
Externí odkaz:
http://arxiv.org/abs/2412.01858