An Application of Feature Engineering and Machine Learning Algorithms on Condition Monitoring of SiC Converters

2021 ◽  
Author(s):  
Afshin Loghmani Moghaddam Toussi ◽  
Amir Sajjad Bahman ◽  
Francesco Iannuzzo ◽  
Frede Blaabjerg
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Feature Engineering

scholarly journals An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Muhammad Waqar ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Nadeem Majeed ◽  
Ameen Banjar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Heart Attack ◽  
High Reliability ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Unequal Distribution ◽  
The Given

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

scholarly journals Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1461 ◽  
Author(s):  
Taeheum Cho ◽  
Unang Sunarya ◽  
Minsoo Yeo ◽  
Bosun Hwang ◽  
Yong Seo Koo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sleep Disorders ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Model Based ◽  
Wrist Actigraphy ◽  
End To End ◽  
Wake Detection

Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices.

scholarly journals Research on Engineering Geomechanics Characteristics and CFRP Reinforcement Technology Based on Machine Learning Algorithms

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Baoqi Yan ◽  
Nuoya Zhang ◽  
Ganggang Lu ◽  
Yue Hui
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Learning Algorithms ◽  
Experimental Studies ◽  
Machine Learning Algorithms ◽  
Natural Exposure ◽  
Condition Monitoring System

We have completed the design of an early warning and evaluation analysis module based on machine learning algorithms. Aiming at the prestressed CFRP-strengthened reinforced concrete bridges under natural exposure, we developed a theoretical model to analyze the long-term prestress loss of reinforced parts and the adhesion behavior of the CFRP-concrete interface under natural exposure conditions. The analysis deeply reveals the technical and engineering geomechanics characteristics of the D bridge. At the same time, through a series of experimental studies on the D bridge condition monitoring system, the data acquisition and transmission, processing and control of the D bridge condition monitoring system, and the bridge condition monitoring and evaluation software are provided. Regarding how to repair the engineering geomechanical characteristics of D bridge, we mentioned the prestressed CFRP reinforcement technology. The prestressed carbon fiber reinforced composite (CFRP) structure made of reinforced concrete (RC) makes better use of the high-strength characteristics of CFRP and changes. It strengthens the stress distribution of the components and improves the overall strength of the components. It is more supported by engineers in the civil engineering and transportation departments. However, most prestressed CFRP-reinforced RC structures are located in natural exposure environments, and the effect of natural exposure environments on the long-term mechanical properties of prestressed C FRP-reinforced RC components is still unclear. This article mainly uses the research on the engineering geomechanics characteristics and reinforcement technology of the bridge body, so that people have a deep understanding of its concept, and provides reasonable use methods and measures for the maintenance and protection of the bridge body in the future. This paper studies the characteristics of engineering geomechanics based on machine learning algorithms and applies them to the research of CFRP reinforcement technology, aiming to promote its better development.

scholarly journals Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification

2020 ◽  
pp. 1-9
Author(s):  
Supun Nakandala ◽  
Marta M. Jankowska ◽  
Fatima Tuz-Zahra ◽  
John Bellettiere ◽  
Jordan A. Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Logistic Regression ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Free Living ◽  
Data Set

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

scholarly journals Feature engineering coupled machine learning algorithms for epileptic seizure forecasting from intracranial EEGs

2017 ◽  
Author(s):  
Rishav Kumar ◽  
Rishi Raj Singh Jhelumi ◽  
Achintye Madhav Singh ◽  
Prasoon Kumar
Keyword(s):  
Machine Learning ◽  
Epileptic Seizure ◽  
Fourier Transforms ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Data Set ◽  
Suggested Approach

AbstractEpilepsy is one of the major neurological disorders affecting nearly 1 percentage of the global population. The major blunt is born by under developed and developing countries due to expensive treatment of epileptic conditions. Further, the lack of proper forecasting methods for an occurrence of epileptic seizures in epileptic-drug resistant patients or patients not amenable for surgery affects their psychological behaviour and restricts their daily activities. The forecasting is usually performed by human experts that leave a wide gap for human-bias and human error. Therefore, in the current work, we have evaluated the efficiency of several machine learning algorithms to automatically identify the preictal patterns corresponding to epileptic seizures from intracranial EEG signals. The robustness of the machine learning algorithms were tested after the data set was pre-processed using carefully chosen feature engineering strategies viz. denoised Fourier transforms as well as cross-correlation across electrodes in time and frequency domain. Extensive experimentations were carried out to determine the best combination of feature engineering techniques and machine learning algorithms. The best combination of feature engineering techniques and machine learning algorithm resulted in 0.7685 AUC (Area under the Receiver Operating Characteristic curve) on the random test samples. The suggested approach was fairly good at prediction of epilepsy in random samples and therefore, it can be used in epileptic seizure forecasting in patients where medication/surgery is ineffective. Eventually, our strategy reveals a robust method for brain disorders forecasting from EEGs.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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