Large Scale Simulations of a Neural Network Model for the Graph Bisection Problem on Geometrically Connected Graphs

2004 ◽  
Vol 18 ◽  
pp. 151-156
Author(s):  
Gonzalo Hernandez ◽  
Luis Salinas
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Connected Graphs ◽  
Graph Bisection ◽  
Large Scale Simulations

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.

scholarly journals Large-scale design and refinement of stable proteins using sequence-only models

2021 ◽  
Author(s):  
Jedediah M. Singer ◽  
Scott Novotney ◽  
Devin Strickland ◽  
Hugh K. Haddox ◽  
Nicholas Leiby ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Amino Acid Sequences ◽  
Novel Proteins ◽  
Wide Range ◽  
The Stability ◽  
Scale Design ◽  
Future Work

AbstractEngineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

scholarly journals Spark-Based Parallel Deep Neural Network Model for Classification of Large Scale RNAs into piRNAs and Non-piRNAs

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 136978-136991
Author(s):  
Salman Khan ◽  
Mukhtaj Khan ◽  
Nadeem Iqbal ◽  
Maozhen Li ◽  
Dost Muhammad Khan
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Deep Neural Network

Large-scale neural network model for multiclass pattern recognition

1993 ◽  
Author(s):  
Thomas T. Lu ◽  
Freddie S. Lin ◽  
Hung Chou ◽  
Andrew A. Kostrzewski ◽  
Jenkin C. Chen
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale
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