Explore the latest peer-reviewed research with AI-generated summaries, key findings, and insights for easy understanding. All content is legally sourced from academic metadata with links to original papers.
The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. We provide an overview of...
This research explores perspective survey on deep transfer learning for fault diagn..., contributing new insights to the field of Artificial Intelligence.
Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors' routine tasks, and powering adapti...
This research explores Biological sequence analysis, contributing new insights to the field of Artificial Intelligence.
Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. Finally, we investigate the predictability of NFT sales using simple machine learning alg...
Abstract The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. AlphaFold DB p...
This research explores Predicting cancer outcomes with radiomics and artificial int..., contributing new insights to the field of Artificial Intelligence.
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. Further, in conducting a thorough evaluation of modeling choices, b...
Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. We expect the benchmark largely b...
This special issue covers a wide range of topics from the area of Computer Vision, Pattern Recognition, and Machine Learning. This breadth of scope is reflected by the papers included in this special...
This research explores When and why PINNs fail to train: A neural tangent kernel pe..., contributing new insights to the field of Artificial Intelligence.
Computer vision is becoming an increasingly trendy word in the area of image processing. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its archite...
Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. In addition, we also analyze and summarize the development of DeepAL from an applicat...
Scoping reviews are an increasingly common approach to evidence synthesis with a growing suite of methodological guidance and resources to assist review authors with their planning, conduct and report...
Abstract Species distribution modeling (SDM) is widely used in ecology and conservation. We find that, in general, nonparametric techniques with the capability of controlling for model complexity outp...
SUMMARY: We present several recent improvements to minimap2, a versatile pairwise aligner for nucleotide sequences. Now minimap2 v2.22 can more accurately map long reads to highly repetitive regions a...
Dynamic neural network is an emerging research topic in deep learning. The important research problems of dynamic networks, e.g., architecture design, decision making scheme, optimization technique an...
The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Additionally...
Machine learning techniques used in computer-aided medical image analysis usually suffer from the domain shift problem caused by different distributions between source/reference data and target data....
Light-weight convolutional neural networks (CNNs) are the de-facto for mobile\nvision tasks. On the ImageNet-1k dataset,\nMobileViT achieves top-1 accuracy of 78.4% with about 6 million parameters,\nw...