Zobrazeno 1 - 10
of 58 314
pro vyhledávání: '"A, Amini"'
Federated learning is emerging as a promising machine learning technique in the medical field for analyzing medical images, as it is considered an effective method to safeguard sensitive patient data and comply with privacy regulations. However, rece
Externí odkaz:
http://arxiv.org/abs/2409.18907
Ontological commitment, i.e., used concepts, relations, and assumptions, are a corner stone of qualitative reasoning (QR) models. The state-of-the-art for processing raw inputs, though, are deep neural networks (DNNs), nowadays often based off from m
Externí odkaz:
http://arxiv.org/abs/2409.17109
Autor:
Far, Amin Zakaie, Far, Mohammad Zakaie, Gharibzadeh, Sonia, Zangeneh, Shiva, Amini, Leila, Rahimi, Morteza
The accelerated expansion of the Internet of Things (IoT) has raised critical challenges associated with privacy, security, and data integrity, specifically in infrastructures such as smart cities or smart manufacturing. Blockchain technology provide
Externí odkaz:
http://arxiv.org/abs/2409.16444
Deep neural network (DNN) models are effective solutions for industry 4.0 applications (\eg oil spill detection, fire detection, anomaly detection). However, training a DNN network model needs a considerable amount of data collected from various sour
Externí odkaz:
http://arxiv.org/abs/2409.15802
Federated learning (FL) has emerged as a promising framework for distributed machine learning. It enables collaborative learning among multiple clients, utilizing distributed data and computing resources. However, FL faces challenges in balancing pri
Externí odkaz:
http://arxiv.org/abs/2409.13133
We develop a 1D continuum model of twin branching in shape memory alloys. The free energy of the branched microstructure comprises the interfacial and elastic strain energy contributions, both expressed in terms of the average twin spacing treated as
Externí odkaz:
http://arxiv.org/abs/2409.07382
Publikováno v:
Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track. ECML PKDD 2024 vol 14948
Self-training methods have proven to be effective in exploiting abundant unlabeled data in semi-supervised learning, particularly when labeled data is scarce. While many of these approaches rely on a cross-entropy loss function (CE), recent advances
Externí odkaz:
http://arxiv.org/abs/2409.07292
Despite their high accuracy, complex neural networks demand significant computational resources, posing challenges for deployment on resource-constrained devices such as mobile phones and embedded systems. Compression algorithms have been developed t
Externí odkaz:
http://arxiv.org/abs/2409.03555
Publikováno v:
Volume 189, May 2024, 207817
This paper advocates for an innovative approach designed for estimating optoelectronic properties of quantum structures utilizing Tight-Binding (TB) theory. Predicated on the comparative analysis between estimated and actual properties, the study str
Externí odkaz:
http://arxiv.org/abs/2408.16329
Autor:
Amini, Mohammad Hossein, Nejati, Shiva
Deep Neural Networks (DNNs) for Autonomous Driving Systems (ADS) are typically trained on real-world images and tested using synthetic simulator images. This approach results in training and test datasets with dissimilar distributions, which can pote
Externí odkaz:
http://arxiv.org/abs/2408.13950