Classification of Metaphase Chromosomes Using Deep Learning Neural Network

2018 ◽  
Author(s):  
P Kiruthika ◽  
K B Jayanthi ◽  
Madian Nirmala
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Metaphase Chromosomes ◽  
Deep Learning Neural Network

scholarly journals APPLICATION OF DEEP LEARNING NEURAL NETWORK FOR CLASSIFICATION OF BIG DATA OF ASTRONOMIC OBSERVATIONS

2017 ◽  
Vol 22 (4) ◽  
pp. 270-275
Author(s):  
A. A. Gorbunov ◽  
◽  
E. A. Isaev ◽  
V. A. Samodurov ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Deep Learning Neural Network

scholarly journals On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java

MATEMATIKA ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 83-90
Author(s):  
Nita Cahyani ◽  
Kartika Fithriasari ◽  
Irhamah Irhamah ◽  
Nur Iriawan
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Deep Learning ◽  
Binary Logistic Regression ◽  
Training Data ◽  
Regression Methods ◽  
Logistic Regression Method ◽  
Validation Procedure ◽  
Deep Learning Neural Network

Neural Network and Binary Logistic Regression are modern and classical data mining analysis tools that can be used to classify data on Bidikmisi scholarship acceptance in East Java Province, Indonesia. One form of Neural Network model available for various applications is the Resilient Backpropagation Neural Network (Resilient BPNN). This study aims to compare the performance of the Resilient BPNN method as a Deep Learning Neural Network and Binary Logistic Regression method in determining the classification of Bidikmisi scholarship acceptance in East Java Province. After preprocessing data and dividing them into two parts, i.e. sets of testing and training data, with 10-foldcross-validation procedure, the Resilient BPNN and Binary Logistic Regression methods are implemented. The result shows that Resilient BPNN with two hidden layers is the best platformnetwork model. The classificationG-mean resulted by these both methods is that Resilient BPNN with two hidden layers is more representative with better performance than Binary Logistic Regression. The Resilient BPNN is recommended to be used topredict acceptance of Bidikmisi applicants yearly.

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 Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0925
Author(s):  
Asroni Asroni ◽  
Ku Ruhana Ku-Mahamud ◽  
Cahya Damarjati ◽  
Hasan Basri Slamat
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Classification ◽  
Deep Learning Neural Network ◽  
Voice Data ◽  
The Voice

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.

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

scholarly journals Heart Diseases Prediction using Deep Learning Neural Network Model

2020 ◽  
Vol 9 (3) ◽  
pp. 2244-2248
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Heart Diseases ◽  
Medical Science ◽  
Disease Prediction ◽  
Health Issues ◽  
Deep Learning Neural Network

Deep learning plays an important role in the field of medical science in solving health issues and diagnosing various diseases. So in this paper, we will discuss heart disease. We proposed a model for heart disease prediction. Heart Disease is on of key area where Deep Neural Network can be used so we can improve the overall quality of the classification of heart disease. The classification can be performed on the various ways like KNN, SVM, Naïve Bayes, Random Forest. Heart Disease UCI dataset will be used to demonstrate Talos Hyper-parameter optimization is more efficient than others.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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