Recent Study on Breast Cancer Prediction Based on Deep Neural Network Model Implemented AWS Machine Learning Platform

2021 ◽  
pp. 22-32
Author(s):  
Le Dinh Phu Cuong ◽  
Dong Wang ◽  
Duyen The Hoang ◽  
Le Mai Nhu Uyen
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Cancer Prediction ◽  
Learning Platform

scholarly journals Breast Cancer Prediction based on Deep Neural Network Model Implemented AWS Machine Learning Platform

2020 ◽  
Vol 9 (2) ◽  
pp. 868-873
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Breast Tissue ◽  
Model Accuracy ◽  
Cancer Prediction ◽  
Learning Platform ◽  
Public Dataset ◽  
And Performance

Breast cancer in women is one of the most dangerous cancers leading to death in women by developing breast tissue. In this work, the application of the Deep Neural Network (DNN) model is implemented on AWS machine learning platform, besides, a comparison with other ML techniques includes XGBoost and Random Forest on a public dataset. Breast cancer prediction based on DNN model with Hyperparameter tuning has the best results of the plot of model accuracy for the training and validation sets and performance evaluation metrics to test the model.

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

scholarly journals Sustaining the Effectiveness of IoT-Driven Intrusion Detection over Time: Defeating Concept and Data Drifts

2021 ◽  
Author(s):  
Omar Abdul Wahab
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Concept Drift ◽  
Address Data ◽  
Independent Features ◽  
Over Time

<p>This paper addresses the challenge of sustaining the intrusion detection effectiveness of machine learning-based intrusion detection systems in the Internet of Things (IoT) in the presence of concept and data drifts. Data drift is a phenomenon which embodies the change that happens in the relationships among the independent features, which is mainly due to changes in the data quality over time. Concept drift is a phenomenon which depicts the change in the relationships between input and output data in the machine learning model over time. To address data drifts, we first propose a series of data preparation steps that help improve the quality of the data and avoid inconsistencies. To counter concept drifts, we capitalize on an online deep neural network model that relies on an ensemble of varying depth neural networks that cooperate and compete together to enable the model to steadily learn and adapt as new data come, thus allowing for stable and long-lasting learning. Experiments conducted on a real-world IoT-based intrusion detection dataset, designed to address concept and data drifts, suggest that our solution stabilizes the performance of the intrusion detection on both the training and testing data compared to the static deep neural network model, which is widely used for intrusion detection.</p>

scholarly journals Deep Neural Network Classifier in Breast Cancer Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 2106-2109
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Machine Learning Algorithms ◽  
Neural Network Classifier ◽  
Cancer Prediction ◽  
Neural Network Classifiers ◽  
Work First ◽  
Erroneous Data

The Breast cancer is the most life menacing disease among women. Early prophecy assurances the endurance of patients. In this work, first Deep neural network classifiers with different hidden layers with different nodes are used to explore the anthropometric information and blood investigation strictures and to predict the disease. Then machine learning algorithms such as SVM and Decision tree are also trained with the same data. Finally the performance of each classifier was deliberated. The pre-processed data of admitted patients with the breast cancer perception are used to train and test the classifiers. This article shack glow on the concert estimation based on right and erroneous data classification

Using deep learning to identify the severity of pipeline dents

2020 ◽  
Vol 4 (2) ◽  
pp. 90-96
Author(s):  
Ishita Charkraborty ◽  
◽  
Brent Vyvial ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Data Set ◽  
Machine Learning Model ◽  
Unseen Data ◽  
Risk Categories

With the advent of machine learning, data-based models can be used to increase efficiency and reduce cost for the characterization of various anomalies in pipelines. In this work, artificial intelligence is used to classify pipeline dents directly from the in-line inspection (ILI) data according to their risk categories. A deep neural network model is built with available ILI data, and the resulting machine learning model requires only the ILI data as an input to classify dents in different risk categories. Using a machine learning based model eliminates the need for conducting detailed engineering analysis to determine the effects of dents on the integrity of the pipeline. Concepts from computer vision are used to build the deep neural network using the available data. The deep neural network model is then trained on a sub set of the available ILI data and the model is tested for accuracy on a previously unseen set of the available data. The developed model predicts risk factors associated with a dent with 94% accuracy for a previously unseen data set.

scholarly journals Sustaining the Effectiveness of IoT-Driven Intrusion Detection over Time: Defeating Concept and Data Drifts

2021 ◽  
Author(s):  
Omar Abdul Wahab
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Concept Drift ◽  
Address Data ◽  
Independent Features ◽  
Over Time

<p>This paper addresses the challenge of sustaining the intrusion detection effectiveness of machine learning-based intrusion detection systems in the Internet of Things (IoT) in the presence of concept and data drifts. Data drift is a phenomenon which embodies the change that happens in the relationships among the independent features, which is mainly due to changes in the data quality over time. Concept drift is a phenomenon which depicts the change in the relationships between input and output data in the machine learning model over time. To address data drifts, we first propose a series of data preparation steps that help improve the quality of the data and avoid inconsistencies. To counter concept drifts, we capitalize on an online deep neural network model that relies on an ensemble of varying depth neural networks that cooperate and compete together to enable the model to steadily learn and adapt as new data come, thus allowing for stable and long-lasting learning. Experiments conducted on a real-world IoT-based intrusion detection dataset, designed to address concept and data drifts, suggest that our solution stabilizes the performance of the intrusion detection on both the training and testing data compared to the static deep neural network model, which is widely used for intrusion detection.</p>

scholarly journals Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

10.2196/17364 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17364 ◽  
Author(s):  
Can Hou ◽  
Xiaorong Zhong ◽  
Ping He ◽  
Bin Xu ◽  
Sha Diao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Deep Neural Network ◽  
Chinese Women ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Age At First Birth ◽  
Cancer Prediction

Background Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. Objective This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. Methods A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. Conclusions The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

scholarly journals Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development (Preprint)

2019 ◽  
Author(s):  
Can Hou ◽  
Xiaorong Zhong ◽  
Ping He ◽  
Bin Xu ◽  
Sha Diao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Deep Neural Network ◽  
Chinese Women ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Age At First Birth ◽  
Cancer Prediction

BACKGROUND Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. OBJECTIVE This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. METHODS A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. RESULTS The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. CONCLUSIONS The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

Sign in / Sign up

Export Citation Format

Share Document