Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Marchisio, Alberto"'
Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of quantum-class
Externí odkaz:
http://arxiv.org/abs/2408.03088
Autor:
Maouaki, Walid El, Innan, Nouhaila, Marchisio, Alberto, Said, Taoufik, Bennai, Mohamed, Shafique, Muhammad
In this study, we develop a novel quantum machine learning (QML) framework to analyze cybersecurity vulnerabilities using data from the 2022 CISA Known Exploited Vulnerabilities catalog, which includes detailed information on vulnerability types, sev
Externí odkaz:
http://arxiv.org/abs/2408.02314
Portfolio Optimization (PO) is a financial problem aiming to maximize the net gains while minimizing the risks in a given investment portfolio. The novelty of Quantum algorithms lies in their acclaimed potential and capability to solve complex proble
Externí odkaz:
http://arxiv.org/abs/2407.19857
Autonomous embedded systems (e.g., robots) typically necessitate intelligent computation with low power/energy processing for completing their tasks. Such requirements can be fulfilled by embodied neuromorphic intelligence with spiking neural network
Externí odkaz:
http://arxiv.org/abs/2407.05262
Publikováno v:
2024 IEEE International Conference on Image Processing (ICIP)
Recent advancements in quantum computing have led to the emergence of hybrid quantum neural networks, such as Quanvolutional Neural Networks (QuNNs), which integrate quantum and classical layers. While the susceptibility of classical neural networks
Externí odkaz:
http://arxiv.org/abs/2407.03875
Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically
Externí odkaz:
http://arxiv.org/abs/2404.09331
Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is h
Externí odkaz:
http://arxiv.org/abs/2404.03493
Autor:
Putra, Rachmad Vidya Wicaksana, Marchisio, Alberto, Zayer, Fakhreddine, Dias, Jorge, Shafique, Muhammad
Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies ha
Externí odkaz:
http://arxiv.org/abs/2404.03325
This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection. Usi
Externí odkaz:
http://arxiv.org/abs/2404.02595
Autor:
Innan, Nouhaila, Khan, Muhammad Al-Zafar, Marchisio, Alberto, Shafique, Muhammad, Bennai, Mohamed
Publikováno v:
2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, pp. 1-9
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about
Externí odkaz:
http://arxiv.org/abs/2403.10861