[HTML][HTML] Artificial intelligence and business value: A literature review
IM Enholm, E Papagiannidis, P Mikalef… - Information Systems …, 2022 - Springer
Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several
advantages for organizations in terms off added business value. Over the past few years …
advantages for organizations in terms off added business value. Over the past few years …
Deep learning models for solar irradiance forecasting: A comprehensive review
P Kumari, D Toshniwal - Journal of Cleaner Production, 2021 - Elsevier
The growing human population in this modern society hugely depends on the energy to
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …
Deep transfer learning approaches for Monkeypox disease diagnosis
Monkeypox has become a significant global challenge as the number of cases increases
daily. Those infected with the disease often display various skin symptoms and can spread …
daily. Those infected with the disease often display various skin symptoms and can spread …
Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
Model complexity of deep learning: A survey
Abstract Model complexity is a fundamental problem in deep learning. In this paper, we
conduct a systematic overview of the latest studies on model complexity in deep learning …
conduct a systematic overview of the latest studies on model complexity in deep learning …
Do imagenet classifiers generalize to imagenet?
We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have
been the focus of intense research for almost a decade, raising the danger of overfitting to …
been the focus of intense research for almost a decade, raising the danger of overfitting to …
Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review
Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …
images, we have witnessed a growing interest of the remote sensing community in …
Meta-weight-net: Learning an explicit mapping for sample weighting
Current deep neural networks (DNNs) can easily overfit to biased training data with
corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to …
corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to …
A theoretical analysis of deep Q-learning
Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …
foundation is less well understood. In this work, we make the first attempt to theoretically …
High-frequency component helps explain the generalization of convolutional neural networks
We investigate the relationship between the frequency spectrum of image data and the
generalization behavior of convolutional neural networks (CNN). We first notice CNN's …
generalization behavior of convolutional neural networks (CNN). We first notice CNN's …