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Data-centric artificial intelligence: A survey
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …
of its great success is the availability of abundant and high-quality data for building machine …
A review of deep transfer learning and recent advancements
M Iman, HR Arabnia, K Rasheed - Technologies, 2023 - mdpi.com
Deep learning has been the answer to many machine learning problems during the past two
decades. However, it comes with two significant constraints: dependency on extensive …
decades. However, it comes with two significant constraints: dependency on extensive …
Data‐driven design for metamaterials and multiscale systems: a review
Metamaterials are artificial materials designed to exhibit effective material parameters that
go beyond those found in nature. Composed of unit cells with rich designability that are …
go beyond those found in nature. Composed of unit cells with rich designability that are …
Backbones-review: Feature extractor networks for deep learning and deep reinforcement learning approaches in computer vision
O Elharrouss, Y Akbari, N Almadeed… - Computer Science …, 2024 - Elsevier
To understand the real world using various types of data, Artificial Intelligence (AI) is the
most used technique nowadays. While finding the pattern within the analyzed data …
most used technique nowadays. While finding the pattern within the analyzed data …
Two-branch attention adversarial domain adaptation network for hyperspectral image classification
Recent studies have shown that deep domain adaptation (DA) techniques have good
performance on cross-domain hyperspectral image (HSI) classification problems. However …
performance on cross-domain hyperspectral image (HSI) classification problems. However …
Domain adaptation in remote sensing image classification: A survey
Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …
samples for model training. When labeled samples are unavailable or labeled samples have …
Deep learning and medical image analysis for COVID-19 diagnosis and prediction
The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to
health-care organizations worldwide. To combat the global crisis, the use of thoracic …
health-care organizations worldwide. To combat the global crisis, the use of thoracic …
Survey on genetic programming and machine learning techniques for heuristic design in job shop scheduling
Job shop scheduling (JSS) is a process of optimizing the use of limited resources to improve
the production efficiency. JSS has a wide range of applications, such as order picking in the …
the production efficiency. JSS has a wide range of applications, such as order picking in the …
A collective AI via lifelong learning and sharing at the edge
A Soltoggio, E Ben-Iwhiwhu, V Braverman… - Nature Machine …, 2024 - nature.com
One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …
independently over a lifetime and share their knowledge with each other. The synergy …
[HTML][HTML] A novel image expression-driven modeling strategy for coke quality prediction in the smart cokemaking process
In pursuit of carbon neutrality and advancing energy-efficient practices within the steel and
coking industries, the traditional cokemaking process is progressively evolving towards …
coking industries, the traditional cokemaking process is progressively evolving towards …