Simulating Transcranial Magnetic Stimulation during PET with a Large-Scale Neural Network Model of the Prefrontal Cortex and the Visual System

NeuroImage ◽  
2002 ◽  
Vol 15 (1) ◽  
pp. 58-73 ◽  
Author(s):  
F.T. Husain ◽  
G. Nandipati ◽  
A.R. Braun ◽  
L.G. Cohen ◽  
M-A. Tagamets ◽  
...  
Keyword(s):  
Neural Network ◽  
Prefrontal Cortex ◽  
Transcranial Magnetic Stimulation ◽  
Visual System ◽  
Network Model ◽  
Neural Network Model ◽  
Magnetic Stimulation ◽  
Large Scale

scholarly journals A Deep Neural Network Model for Predicting Electric Fields Induced by Transcranial Magnetic Stimulation Coil

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 128381-128392
Author(s):  
Khaleda Akhter Sathi ◽  
Md. Azad Hossain ◽  
Md. Kamal Hosain ◽  
Nguyen Hoang Hai ◽  
Md. Anwar Hossain
Keyword(s):  
Neural Network ◽  
Transcranial Magnetic Stimulation ◽  
Electric Fields ◽  
Network Model ◽  
Neural Network Model ◽  
Magnetic Stimulation ◽  
Deep Neural Network

scholarly journals Image Classification Using Transfer Learning and Deep Learning

2021 ◽  
Vol 10 (9) ◽  
pp. 25394-25398
Author(s):  
Chitra Desai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Deep Neural Network ◽  
Visual Recognition ◽  
Classification Of Images

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used.   This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

scholarly journals Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

2020 ◽  
Vol 12 (8) ◽  
pp. 137
Author(s):  
Bo Jiang ◽  
Yanbai He ◽  
Rui Chen ◽  
Chuanyan Hao ◽  
Sijiang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Teaching And Learning ◽  
Large Scale ◽  
Small Scale ◽  
Teaching Improvement ◽  
Data Feedback ◽  
Learning Data

Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.

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