scholarly journals Inception-V3 Architecture in Dermatoglyphics-Based Temperament Classification

2020 ◽  
Vol 3 (2) ◽  
pp. 173-174
Author(s):  
Mary Gift D. Dionson ◽  
El Jireh P. Bibangco
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Computer Science ◽  
Neural Network Model ◽  
Human Body ◽  
Fingerprint Analysis ◽  
Behavioral Psychology ◽  
Personality Classification

Personality classification is one of the areas of behavioral psychology that focuses on categorizing individuals. Different factors constitute the main currents of human personality. These factors turned out to be complicated and sometimes yield a biased result. Meanwhile, the entire human body reflects the character of its possessor more accurately than any set of questionnaires. Dermatoglyphics is the scientific study of fingerprints. Fingerprint patterns and ridge density are the viable bases in the classification of the personality of an individual. This uniqueness has expanded through research confirming parents' ability to identify their children's unique potentials through fingerprint analysis. Bridging the gap between computer science and psychology is one of the biggest challenges of the study. Exploring the possibilities revolves around image processing, where fingerprints served as image input and a deep learning convolutional neural network model implemented in the Inception-v3 architecture is used to analyze and classify different fingerprint patterns finally associate with the classified prints to its corresponding temperament type.

scholarly journals Nonlinear Hyperparameter Optimization of a Neural Network in Image Processing for Micromachines

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1504
Author(s):  
Mingming Shen ◽  
Jing Yang ◽  
Shaobo Li ◽  
Ansi Zhang ◽  
Qiang Bai
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Dropout Rate ◽  
Adjustment Process ◽  
Correlation Model ◽  
Mathematical Correlation ◽  
Verification Methods

Deep neural networks are widely used in the field of image processing for micromachines, such as in 3D shape detection in microelectronic high-speed dispensing and object detection in microrobots. It is already known that hyperparameters and their interactions impact neural network model performance. Taking advantage of the mathematical correlations between hyperparameters and the corresponding deep learning model to adjust hyperparameters intelligently is the key to obtaining an optimal solution from a deep neural network model. Leveraging these correlations is also significant for unlocking the “black box” of deep learning by revealing the mechanism of its mathematical principle. However, there is no complete system for studying the combination of mathematical derivation and experimental verification methods to quantify the impacts of hyperparameters on the performances of deep learning models. Therefore, in this paper, the authors analyzed the mathematical relationships among four hyperparameters: the learning rate, batch size, dropout rate, and convolution kernel size. A generalized multiparameter mathematical correlation model was also established, which showed that the interaction between these hyperparameters played an important role in the neural network’s performance. Different experiments were verified by running convolutional neural network algorithms to validate the proposal on the MNIST dataset. Notably, this research can help establish a universal multiparameter mathematical correlation model to guide the deep learning parameter adjustment process.

RECOGNITION OF THE TEXT BY MEANS OF DEEP LEARNING

2020 ◽  
Vol 69 (1) ◽  
pp. 378-383
Author(s):  
T.A. Nurmukhanov ◽  
◽  
B.S. Daribayev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Input Data ◽  
Optimization Methods ◽  
Text Recognition ◽  
Activation Functions

Using neural networks, various variations of the classification of objects can be performed. Neural networks are used in many areas of recognition. A big area in this area is text recognition. The paper considers the optimal way to build a network for text recognition, the use of optimal methods for activation functions, and optimizers. Also, the article checked the correctness of text recognition with different optimization methods. This article is devoted to the analysis of convolutional neural networks. In the article, a convolutional neural network model will be trained with a teacher. Teaching with a teacher is a type of training for neural networks in which you provide the input data and the desired result, that is, the student looking at the input data will understand that you need to strive for the result that was provided to him.

scholarly journals Classification of Indramayu’s Mangoes Based on Shape Features Using Neural Network and Rule Based Programming

2017 ◽  
Vol 1 ◽  
pp. 245-248
Author(s):  
Sumarudin Sumarudin ◽  
Iryanto Iryanto ◽  
Eka Ismantohadi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Learning Approach ◽  
Shape Features ◽  
Rule Based ◽  
General Classification ◽  
Result Show ◽  
Better Than

Object classification using image processing simplifies the process. Many approaches have been used to classify the object. In general, classification of mangoes uses image of leaves. In this research, we do a slightly different approach using image of mango itself. Here, two kinds of method are used to classify the object.  Implementations of deep learning using neural network and rule based programming are used in the process. Comparative study of the methods are presented in the article. Our result show that accuracy of deep learning approach is better than the rule based programming. The accuracy is 80% and 8% for neural network and rule based programming, respectively.

scholarly journals Covid-19 Detection & Classification of chest X-rays using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 2614-2619
Author(s):  
Aditya Singh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Neural Network Model ◽  
X Rays ◽  
X Ray ◽  
Short Period ◽  
Medical Researchers ◽  
Entire World ◽  
Difficult Times

Abstract: The deadly Covid-19 virus, also known as the Coronavirus has affected the entire world in a short period of time. This pandemic has affected a lot of people in the entire world and caused many deaths. In these difficult times, it is important for the doctors and the medical researchers to differentiate accurately between positive cases and negative cases. This CNN (Convolutional Neural Network) model will allow us to classify X-ray images into positive cases and the normal ones. This dataset is collected from different public sources as well as from some hospitals and physicians. Our goal is to take help from these X- ray images and develop a model where it predicts and classifies the infected cases. Keywords: CNN, Prediction, Classification, Features, Training, Testing, Deep Learning

scholarly journals Using deep learning neural network model to solve the problems of classification of unwanted content in social media

2021 ◽  
pp. 56-62
Author(s):  
А.С. Бобин
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Classification Problems ◽  
Deep Learning Neural Network

При решении задач классификации с использование глубокого обучения сталкиваются с проблемой сходимости модели. Такая проблема возникает из за ограниченного объема данных в выборках. When solving classification problems using deep learning, they face the problem of model convergence. This problem occurs due to the limited amount of data in the samples.

scholarly journals Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model

iScience ◽  
2020 ◽  
Vol 23 (3) ◽  
pp. 100886 ◽  
Author(s):  
Tsai-Min Chen ◽  
Chih-Han Huang ◽  
Edward S.C. Shih ◽  
Yu-Feng Hu ◽  
Ming-Jing Hwang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Cardiac Arrhythmias ◽  
Deep Learning Neural Network
Sign in / Sign up

Export Citation Format

Share Document