Results 21 to 30 of about 267,347 (270)

Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT

Frontiers in Medicine, 2023
BackgroundAttenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT).
Yu Du, Yu Du, Han Jiang, Ching-Ni Lin, Zhengyu Peng, Jingzhang Sun, Pai-Yi Chiu, Guang-Uei Hung, Greta S. P. Mok, Greta S. P. Mok   +9 more
doaj   +1 more source

Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach

BMC Bioinformatics, 2021
Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions.
Pegah Mavaie, Lawrence Holder, Daniel Beck, Michael K. Skinner   +3 more
doaj   +1 more source

A Survey on Attacks and Their Countermeasures in Deep Learning: Applications in Deep Neural Networks, Federated, Transfer, and Deep Reinforcement Learning

IEEE Access, 2023
Deep Learning (DL) techniques are being used in various critical applications like self-driving cars. DL techniques such as Deep Neural Networks (DNN), Deep Reinforcement Learning (DRL), Federated Learning (FL), and Transfer Learning (TL) are prone to ...
Haider Ali, Dian Chen, Matthew Harrington, Nathaniel Salazar, Mohannad Al Ameedi, Ahmad Faraz Khan, Ali R. Butt, Jin-Hee Cho   +7 more
doaj   +1 more source

Deep Learning Meets Cognitive Radio: Predicting Future Steps [PDF]

, 2020
Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations.
Ahmadi, Hamed, Khan, Zaheer, Shenfield, Alex   +2 more
core   +3 more sources

Quantum distributed deep learning architectures: Models, discussions, and applications

ICT Express, 2023
Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem,
Yunseok Kwak, Won Joon Yun, Jae Pyoung Kim, Hyunhee Cho, Jihong Park, Minseok Choi, Soyi Jung, Joongheon Kim   +7 more
doaj   +1 more source

Deep learning approaches in flow visualization

Advances in Aerodynamics, 2022
With the development of deep learning (DL) techniques, many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL methods.
Can Liu, Ruike Jiang, Datong Wei, Changhe Yang, Yanda Li, Fang Wang, Xiaoru Yuan   +6 more
doaj   +1 more source

A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction

Applied Artificial Intelligence, 2022
Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five
Yuchen Dang, Ziqi Chen, Heng Li, Hai Shu
doaj   +1 more source

Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

Applied Sciences, 2022
Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest.
Muhammad Shahid Iqbal, Rashid Abbasi, Md Belal Bin Heyat, Faijan Akhtar, Asmaa Sayed Abdelgeliel, Sarah Albogami, Eman Fayad, Muhammad Atif Iqbal   +7 more
doaj   +1 more source

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure–Activity Relationships

Molecules, 2023
A deep learning-based quantitative structure–activity relationship analysis, namely the molecular image-based DeepSNAP–deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three ...
Yasunari Matsuzaka, Yoshihiro Uesawa
doaj   +1 more source

Driver Drowsiness Detection using Evolutionary Machine Learning: A Survey [PDF]

BIO Web of Conferences
One of the factors that kills hundreds of people every year is driving accidents caused by drowsy drivers. There are different methods to prevent this type of accidents. Recently Machine Learning (ML) and Deep Learning (DL) have emerged as very effective
Yasir Jumhaa Maha, Majeed Osama, Taima Alaa   +2 more
doaj   +1 more source