Zobrazeno 1 - 10
of 2 460 423
pro vyhledávání: '"Ali, AS"'
Autor:
Pourghoraba, Ali, KhajueeZadeh, MohammadSadegh, Amini, Ali, Vahedi, Abolfazl, Agah, Gholam Reza, Rahideh, Akbar
Reliable mechanical fault detection with limited data is crucial for the effective operation of induction machines, particularly given the real-world challenges present in industrial datasets, such as significant imbalances between healthy and faulty
Externí odkaz:
http://arxiv.org/abs/2412.04255
This exploratory study highlights the significant threats of social media polarization and weaponization in Ethiopia, analyzing the Northern Ethiopia (Tigray) War (November 2020 to November 2022) as a case study. It further uncovers the lack of effec
Externí odkaz:
http://arxiv.org/abs/2412.01549
A standard model that arises in several applications in sequential decision making is partially observable Markov decision processes (POMDPs) where a decision-making agent interacts with an uncertain environment. A basic objective in such POMDPs is t
Externí odkaz:
http://arxiv.org/abs/2412.00941
Autor:
Ramzan, Muhammad Umer, Khaddim, Wahab, Rana, Muhammad Ehsan, Ali, Usman, Ali, Manohar, Hassan, Fiaz ul, Mehmood, Fatima
This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become
Externí odkaz:
http://arxiv.org/abs/2411.19690
Autor:
Ramzan, Muhammad Umer, Zia, Ali, Khamis, Abdelwahed, Elgharabawy, yman, Liaqat, Ahmad, Ali, Usman
This paper presents a novel deep-learning framework that significantly enhances the transformation of rudimentary face sketches into high-fidelity colour images. Employing a Convolutional Block Attention-based Auto-encoder Network (CA2N), our approac
Externí odkaz:
http://arxiv.org/abs/2411.19005
Modern sequence models (e.g., Transformers, linear RNNs, etc.) emerged as dominant backbones of recent deep learning frameworks, mainly due to their efficiency, representational power, and/or ability to capture long-range dependencies. Adopting these
Externí odkaz:
http://arxiv.org/abs/2411.15671
Federated Learning (FL) is a distributed learning technique that maintains data privacy by providing a decentralized training method for machine learning models using distributed big data. This promising Federated Learning approach has also gained po
Externí odkaz:
http://arxiv.org/abs/2411.05173
In this research, we define two concepts; random matrix and mean square stability. Studying the stability of solutions for perturbed random differential systems is included. Partial moments of the second order were verified, which determines the stab
Externí odkaz:
http://arxiv.org/abs/2411.04929
In this study, we investigate the performance of the sparse identification of nonlinear dynamics (SINDy) algorithm and the neural ordinary differential equations (ODEs) in identification of the underlying mechanisms of open ocean Lagrangian drifter h
Externí odkaz:
http://arxiv.org/abs/2411.04350
Autor:
Zhiany, Saeed, Ghassemi, Fatemeh, Abbasimoghadam, Nesa, Hodaei, Ali, Ataollahi, Ali, Kovács, József, Ábrahám, Erika, Sirjani, Marjan
Hybrid Rebeca is introduced for modeling asynchronous event-based Cyber-Physical Systems (CPSs). In this work, we extend Hybrid Rebeca to allow the modeling of non-deterministic time behavior. We provide a set of rules to define the semantic model of
Externí odkaz:
http://arxiv.org/abs/2411.03160