scholarly journals A Study on Training Data Selection Method for EEG Emotion Analysis using Machine Learning Algorithm

2018 ◽  
Vol 119 ◽  
pp. 79-88 ◽  
Author(s):  
Jong-Seob Yun ◽  
Jin Heon Kim
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Selection Method ◽  
Training Data ◽  
Data Selection ◽  
Machine Learning Algorithm ◽  
Emotion Analysis ◽  
Training Data Selection

scholarly journals Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2860 ◽  
Author(s):  
Jee-Heon Kim ◽  
Nam-Chul Seong ◽  
Wonchang Choi
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Machine Learning Algorithm ◽  
Air Conditioning System ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Input Variables

This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

A Machine Learning Algorithm to Predict Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors in the Real World (Preprint)

2021 ◽  
Author(s):  
Jincheng Yang
Keyword(s):  
Machine Learning ◽  
Adverse Reaction ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Total Drug

BACKGROUND Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. Support Vector Machine (SVM) is a type of supervised learning method which analyzes data and recognizes patterns, mainly used for statistical classification and regression. OBJECTIVE From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. METHODS Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of SVM was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. RESULTS Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. CONCLUSIONS The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making. CLINICALTRIAL N/A

A Machine Learning Algorithm to Predict Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors in the Real World

2020 ◽  
Author(s):  
Jincheng Yang ◽  
Weilong Lin ◽  
Liming Shi ◽  
Ming Deng ◽  
Wenjing Yang
Keyword(s):  
Machine Learning ◽  
Adverse Reaction ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Total Drug

Abstract Background: Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. Methods: Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of Support Vector Machine (SVM) was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. Results: Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. Conclusions: The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making.

Automatic microseismic event picking via unsupervised machine learning

2020 ◽  
Vol 222 (3) ◽  
pp. 1750-1764 ◽  
Author(s):  
Yangkang Chen
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
State Of The Art ◽  
The State ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Unsupervised Machine Learning ◽  
Earthquake Data

SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

scholarly journals Slope Inspection System: Using image processed by machine learning algorithm to determine risk of slope failure

2020 ◽  
Vol 7 (2) ◽  
pp. 106-118
Author(s):  
Sabril Haziq Mat Shukor ◽  
Syamsul Bahrin Abdul Hamid ◽  
Norhidayu Kasim ◽  
Zuraidah Ab. Moin
Keyword(s):  
Machine Learning ◽  
Conventional Method ◽  
Test Data ◽  
Laser Scanning ◽  
Slope Failure ◽  
Learning Algorithm ◽  
Training Data ◽  
Economic Losses ◽  
Inspection System ◽  
Machine Learning Algorithm

Malaysia is a tropical country that experiences rainy and hot weather throughout the year. The higher rainfall intensity leads to higher landslide occurrences in Malaysia. Landslides that occur nearby human settlements increase the risk and hazard to the public and properties that lead to significant economic losses. There are various methods in surveying the risk and hazard of landslide areas such as terrestrial laser scanning (TLS) and global positioning system (GPS). Most of the past research uses the conventional method which requires an in-situ field survey, lab analysis, and an additional software package to determine the hazard level for a slope. The conventional method is inefficient and time-consuming. In this paper, the potential of a machine-learning algorithm to improve the conventional approach in detecting the hazard in a landslide is discussed. The algorithm assesses the level of risk based on trained supervised images identified by experts in the field. Using the trained model, it was found that Convolution Neural Network (CNN) is able to perform better than Fully Connected Layer within the reduced processing time, with increased accuracy of 22%. However, the accuracy of the CNN on test data in IIUM could still be improved further. Currently, there is an actual prediction accuracy is at 50% for the test data in IIUM, a 17% error compared to CNN prediction. More training data could be added to the CNN to improve the current accuracy.

On Parallel Online Learning for Adaptive Embedded Systems

2014 ◽  
pp. 262-281
Author(s):  
Tapio Pahikkala ◽  
Antti Airola ◽  
Thomas Canhao Xu ◽  
Pasi Liljeberg ◽  
Hannu Tenhunen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adaptive Systems ◽  
Learning Algorithm ◽  
Parallel Implementation ◽  
Recognition Task ◽  
Accurate Solution ◽  
Training Data ◽  
Machine Learning Algorithm ◽  
Systems Learning ◽  
On Chip

This chapter considers parallel implementation of the online multi-label regularized least-squares machine-learning algorithm for embedded hardware platforms. The authors focus on the following properties required in real-time adaptive systems: learning in online fashion, that is, the model improves with new data but does not require storing it; the method can fully utilize the computational abilities of modern embedded multi-core computer architectures; and the system efficiently learns to predict several labels simultaneously. They demonstrate on a hand-written digit recognition task that the online algorithm converges faster, with respect to the amount of training data processed, to an accurate solution than a stochastic gradient descent based baseline. Further, the authors show that our parallelization of the method scales well on a quad-core platform. Moreover, since Network-on-Chip (NoC) has been proposed as a promising candidate for future multi-core architectures, they implement a NoC system consisting of 16 cores. The proposed machine learning algorithm is evaluated in the NoC platform. Experimental results show that, by optimizing the cache behaviour of the program, cache/memory efficiency can improve significantly. Results from the chapter provide a guideline for designing future embedded multi-core machine learning devices.

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