Leftovers Food Recognition using Deep Neural Network and Regression Approach for Objective Visual Analysis Estimation

2021 ◽  
Author(s):  
Yuita Arum Sari ◽  
Sigit Adinugroho ◽  
Jaya Mahar Maligan ◽  
Ersya Nadia Candra ◽  
Fitri Utaminingrum ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Visual Analysis ◽  
Food Recognition ◽  
Regression Approach

scholarly journals Modeling of Mechanical Properties of Clay-Reinforced Polymer Nanocomposites Using Deep Neural Network

Materials ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 4266
Author(s):  
Bouchaib Zazoum ◽  
Ennouri Triki ◽  
Abdel Bachri
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Deep Neural Network ◽  
Tensile Modulus ◽  
Processing Parameters ◽  
Reinforced Polymer ◽  
Machine Learning Approach ◽  
Regression Approach ◽  
Independent Parameters ◽  
Conventional Regression

Due to the non-linear characteristics of the processing parameters, predicting the desired properties of nanocomposites using the conventional regression approach is often unsatisfactory. Thus, it is essential to use a machine learning approach to determine the optimum processing parameters. In this study, a backpropagation deep neural network (DNN) with nanoclay and compatibilizer content, and processing parameters as input, was developed to predict the mechanical properties, including tensile modulus and tensile strength, of clay-reinforced polyethylene nanocomposites. The high accuracy of the developed model proves that DNN can be used as an efficient tool for predicting mechanical properties of the nanocomposites in terms of four independent parameters.

scholarly journals Deep Neural Network-based Speaker-Aware Information Logging (SAIL) for Augmentative and Alternative Communication

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Gang Hu ◽  
Szu-Han Kay Chen ◽  
Neal Mazur
Keyword(s):  
Neural Network ◽  
Augmentative And Alternative Communication ◽  
Deep Neural Network ◽  
Visual Analysis ◽  
High Tech ◽  
User Performance ◽  
Alternative Communication ◽  
Data Logging ◽  
Communication Needs ◽  
Data Log

People with complex communication needs can use a high-technology Augmentative and Alternative Communication (AAC) device to communicate with others. Currently, researchers and clinicians often use data logging from high-tech AAC devices to analyze AAC user performance. However, existing automated data logging systems cannot differentiate the authorship of the data log when more than one user accesses the device. This issue reduces the validity of the data logs and increases the difficulties of performance analysis. Therefore, this paper presents a solution using a deep neural network-based visual analysis approach to process videos to detect different AAC users in practice sessions. This approach has significant potential to improve the validity of data logs and ultimately to enhance AAC outcome measures.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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