Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Medical Virology"
DOI: 10.1002/jmv.28693
Abstract: Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo… read more here.
Keywords: machine learning; drug repurposing; deep learning; learning deep ... See more keywords
Transfer learning of deep material network for seamless structure–property predictions
Sign Up to like & getrecommendations! Published in 2019 at "Computational Mechanics"
DOI: 10.1007/s00466-019-01704-4
Abstract: Modern materials design requires reliable and consistent structure–property relationships. The paper addresses the need through transfer learning of deep material network (DMN). In the proposed learning strategy, we store the knowledge of a pre-trained network… read more here.
Keywords: learning deep; structure property; transfer learning; structure ... See more keywords
A robust weakly supervised learning of deep Conv-Nets for surface defect inspection
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-04819-5
Abstract: Automatic defect detection is a challenging task owing to the complex textured background with non-uniform intensity distribution, weak differences between defects and background, diversity of defect types, and high cost of annotated samples. In order… read more here.
Keywords: robust weakly; learning deep; weakly supervised; segmentation ... See more keywords
Unsupervised Binary Representation Learning with Deep Variational Networks
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-019-01166-4
Abstract: Learning to hash is regarded as an efficient approach for image retrieval and many other big-data applications. Recently, deep learning frameworks are adopted for image hashing, suggesting an alternative way to formulate the encoding function… read more here.
Keywords: representation learning; learning deep; deep variational; unsupervised binary ... See more keywords
A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges
Sign Up to like & getrecommendations! Published in 2020 at "Archives of Computational Methods in Engineering"
DOI: 10.1007/s11831-020-09496-0
Abstract: Internet of Things (IoT) is widely accepted technology in both industrial as well as academic field. The objective of IoT is to combine the physical environment with the cyber world and create one big intelligent… read more here.
Keywords: ids iot; learning deep; machine learning; security issues ... See more keywords
Learning deep hierarchical and temporal recurrent neural networks with residual learning
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-020-01063-0
Abstract: Learning both hierarchical and temporal dependencies can be crucial for recurrent neural networks (RNNs) to deeply understand sequences. To this end, a unified RNN framework is required that can ease the learning of both the… read more here.
Keywords: learning deep; neural networks; hierarchical temporal; residual learning ... See more keywords
A primer for understanding radiology articles about machine learning and deep learning.
Sign Up to like & getrecommendations! Published in 2020 at "Diagnostic and interventional imaging"
DOI: 10.1016/j.diii.2020.10.001
Abstract: The application of machine learning and deep learning in the field of imaging is rapidly growing. Although the principles of machine and deep learning are unfamiliar to the majority of clinicians, the basics are not… read more here.
Keywords: learning deep; machine learning; deep learning; radiology ... See more keywords
Learning deep representation of imbalanced SCADA data for fault detection of wind turbines
Sign Up to like & getrecommendations! Published in 2019 at "Measurement"
DOI: 10.1016/j.measurement.2019.03.029
Abstract: Abstract Numerous intelligent fault diagnosis models have been developed on supervisory control and data acquisition (SCADA) systems of wind turbines, so as to process massive SCADA data effectively and accurately. However, there is a problem… read more here.
Keywords: wind turbines; learning deep; deep representation; class ... See more keywords
Learning deep event models for crowd anomaly detection
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.09.063
Abstract: Abnormal event detection in video surveillance is extremely important, especially for crowded scenes. In recent years, many algorithms have been proposed based on hand-crafted features. However, it still remains challenging to decide which kind of… read more here.
Keywords: learning deep; detection; deep event; model ... See more keywords
When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry
Sign Up to like & getrecommendations! Published in 2023 at "ACS Omega"
DOI: 10.1021/acsomega.2c07722
Abstract: Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information… read more here.
Keywords: food databases; food; chemistry; machine learning ... See more keywords
Learning in deep neural networks and brains with similarity-weighted interleaved learning
Sign Up to like & getrecommendations! Published in 2022 at "Proceedings of the National Academy of Sciences of the United States of America"
DOI: 10.1073/pnas.2115229119
Abstract: Significance Unlike humans, artificial neural networks rapidly forget previously learned information when learning something new and must be retrained by interleaving the new and old items; however, interleaving all old items is time-consuming and might… read more here.
Keywords: neural networks; similarity; old items; similarity weighted ... See more keywords