A Framework for an Artificial-Neural-Network-Based Electronic Nose

Electronic Nose ◽

Learning Algorithm ◽

General Purpose ◽

Food Spoilage ◽

Standard Data ◽

Semiconductor Gas Sensor ◽

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.

Box Office Success Prediction Through Artificial Neural Network and Machine Learning Algorithm

SSRN Electronic Journal ◽

10.2139/ssrn.3753059 ◽

2020 ◽

Author(s):

Jay Bhatt ◽

Saurav Verma

Keyword(s):

Neural Network ◽

Machine Learning ◽

Learning Algorithm ◽

Success Prediction ◽

Machine Learning Algorithm ◽

Box Office ◽

Artificial neural network models for coronary artery disease

Current Bioinformatics ◽

10.2174/1574893615666200214102837 ◽

2020 ◽

Vol 15 ◽

Author(s):

Elham Shamsara ◽

Sara Saffar Soflaei ◽

Mohammad Tajfard ◽

Ivan Yamshchikov ◽

Habibollah Esmaili ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Coronary Artery Disease ◽

Pattern Recognition ◽

Coronary Artery ◽

Diagnostic Model ◽

Early Prediction ◽

Artificial Neural ◽

Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among diﬀerent machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Diﬀerent methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant diﬀerence among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among diﬀerent methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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

Journal of Marine Science and Engineering ◽

10.3390/jmse8100766 ◽

2020 ◽

Vol 8 (10) ◽

pp. 766

Author(s):

Dohan Oh ◽

Julia Race ◽

Selda Oterkus ◽

Bonguk Koo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Machine Learning Techniques ◽

Burst Pressure ◽

Comparison Results ◽

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.

Machine learning-based network modeling: An artificial neural network model vs a theoretical inspired model

2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN) ◽

10.1109/icufn.2017.7993839 ◽

2017 ◽

Cited By ~ 5

Author(s):

Josep Carner ◽

Albert Mestres ◽

Eduard Alarcon ◽

Albert Cabellos

Keyword(s):

Neural Network ◽

Machine Learning ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Network Modeling ◽

2018 International Conference on Information Management and Technology (ICIMTech) ◽

Development of a Web Based Corruption Case Mapping Using Machine Learning with Artificial Neural Network

10.1109/icimtech.2018.8528150 ◽

2018 ◽

Author(s):

Noerlina ◽

Retno Dewanti ◽

Tirta Nugraha Mursitama ◽

Sheila Putri Fajrianti ◽

Desi Maya Kristin ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Web Based ◽

Corruption Case ◽

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Mathematical Problems in Engineering ◽

10.1155/2021/6647829 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Tuan Vu Dinh ◽

Hieu Nguyen ◽

Xuan-Linh Tran ◽

Nhat-Duc Hoang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Soil Erosion ◽

Differential Evolution ◽

Influencing Factors ◽

Support Vector ◽

Adaptive Differential Evolution ◽

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).