Improving Prediction Accuracy for Debonding Quantification in Stiffened Plate by Meta-Learning Model

2020 ◽  
pp. 51-63
Author(s):  
Abhijeet Kumar ◽  
Anirban Guha ◽  
Sauvik Banerjee
Keyword(s):  
Prediction Accuracy ◽  
Learning Model ◽  
Stiffened Plate ◽  
Meta Learning

scholarly journals Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

2020 ◽  
Vol 12 (7) ◽  
pp. 1095 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Karsten Schmidt ◽  
Alireza Amirian-Chakan ◽  
Tobias Rentschler ◽  
Mojtaba Zeraatpisheh ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Learning Model ◽  
Weighted Averaging ◽  
Climatic Regions ◽  
Meta Learning

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions.

Hypervolemia Screening for Dialysis Patient Healthcare Using Meta Learning Model-Based Intelligent Scaler

Smart Science ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 16-27
Author(s):  
Wei-Ling Chen ◽  
Hsiang-Yueh Lai ◽  
Pi-Yun Chen ◽  
Chung-Dann Kan ◽  
Chia-Hung Lin
Keyword(s):  
Dialysis Patient ◽  
Learning Model ◽  
Model Based ◽  
Meta Learning

scholarly journals A Joint Optimization Framework of the Embedding Model and Classifier for Meta-Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongyu Liu ◽  
Xu Chu ◽  
Yan Lu ◽  
Wanli Yu ◽  
Shuguang Miao ◽  
...  
Keyword(s):  
Texture Features ◽  
Current Method ◽  
Optimization Method ◽  
Learning Model ◽  
Feature Representation ◽  
Joint Optimization ◽  
Small Samples ◽  
Semantic Features ◽  
Quantum Genetic Algorithm ◽  
Meta Learning

The aim of meta-learning is to train the machine to learn quickly and accurately. Improving the performance of the meta-learning model is important in solving the problem of small samples and in achieving general artificial intelligence. A meta-learning method based on feature embedding that exhibits good performance on the few-shot problem was previously proposed. In this method, the pretrained deep convolution neural network was used as the embedding model of sample features, and the output of one layer was used as the feature representation of samples. The main limitation of the method is the inability to fuse low-level texture features and high-level semantic features of the embedding model and joint optimization of the embedding model and classifier. Therefore, a multilayer adaptive joint training and optimization method of the embedding model was proposed in the current study. The main characteristics of the current method include using multilayer adaptive hierarchical loss to train the embedding model and using the quantum genetic algorithm to jointly optimize the embedding model and classifier. Validation was performed based on multiple public datasets for meta-learning model testing. The proposed method shows higher accuracy compared with multiple baseline methods.

scholarly journals Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

2019 ◽  
Vol 8 (6) ◽  
pp. 4368-4373
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI

2019 ◽  
Vol 98 (11) ◽  
pp. 1234-1238 ◽  
Author(s):  
S. Yamaguchi ◽  
C. Lee ◽  
O. Karaer ◽  
S. Ban ◽  
A. Mine ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Composite Resin ◽  
Area Under The Curve ◽  
Learning Model ◽  
Calculation Time ◽  
Computer Aided ◽  
Cad Cam ◽  
The Mean ◽  
F Measure

A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology—that is, the deep learning with a CNN method established in this study—demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.

Biodanza in the Development of High- Performance Teams: Embodying the Appreciative Paradigm

2021 ◽  
Vol 23 (3) ◽  
pp. 16-31
Author(s):  
Felipe Losada Carrasco ◽  
Gittith Sánchez Padilla
Keyword(s):  
High Performance ◽  
Learning Model ◽  
The Body ◽  
Integration System ◽  
Form Part ◽  
Meta Learning ◽  
Performance Teams

Cada día sentimos en nuestro cuerpo sensaciones agradables o desagradables, agitación o calma, influenciando la conexión con las demás personas que forman parte del equipo. La práctica del sistema de integración humana “Biodanza” aumenta la conexión del equipo, lo que se refleja en el aumento de positividad y la reducción de la negatividad de los afectos medidos a través del test PANAS, indicadores que caracterizan a los equipos de alto desempeño según el modelo Meta Learning. El cuerpo y las experiencias integradoras son los protagonistas de este proceso. Every day in our bodies we feel pleasant or unpleasant sensations, agitation, or calm, which influence the connections we have with other people who form part of a team. Practising the ‘Biodanza’ human integration system increases the connections within a team, which is reflected in increased positivity and reduced negativity of affect as measured with the PANAS test, indicators that characterise high-performance teams according to the Meta Learning model. The body and the integrative experiences are the protagonists in this process.

Enhanced Data Stream Classification by Optimized Weight Updated Meta-learning: Continuous learning-based on Concept-Drift

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maisnam Niranjan Singh ◽  
Samitha Khaiyum
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Concept Drift ◽  
Learning Model ◽  
Streaming Data ◽  
Spotted Hyena ◽  
Continuous Learning ◽  
Content Type ◽  
Data Stream Classification ◽  
Meta Learning

Purpose The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives. Design/methodology/approach Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models. Findings From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification. Originality/value This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.

scholarly journals Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy

2019 ◽  
Vol 60 (6) ◽  
pp. 818-824 ◽  
Author(s):  
Takuya Mizutani ◽  
Taiki Magome ◽  
Hiroshi Igaki ◽  
Akihiro Haga ◽  
Kanabu Nawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Malignant Glioma ◽  
Survival Time ◽  
Treatment Duration ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Learning Model ◽  
Machine Learning Model ◽  
Prescription Dose ◽  
Leave One Out

ABSTRACT The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual patient by using a machine learning model. A total of 35 patients with malignant glioma were included in this study. The candidate features included 12 clinical features and 192 dose–volume histogram (DVH) features. The appropriate input features and parameters of the support vector machine (SVM) were selected using the genetic algorithm based on Akaike’s information criterion, i.e. clinical, DVH, and both clinical and DVH features. The prediction accuracy of the SVM models was evaluated through a leave-one-out cross-validation test with residual error, which was defined as the absolute difference between the actual and predicted survival times after radiotherapy. Moreover, the influences of various values of prescription dose and treatment duration on the predicted survival time were evaluated. The prediction accuracy was significantly improved with the combined use of clinical and DVH features compared with the separate use of both features (P < 0.01, Wilcoxon signed rank test). Mean ± standard deviation of the leave-one-out cross-validation using the combined clinical and DVH features, only clinical features and only DVH features were 104.7 ± 96.5, 144.2 ± 126.1 and 204.5 ± 186.0 days, respectively. The prediction accuracy could be improved with the combination of clinical and DVH features, and our results show the potential to optimize the treatment strategy for individual patients based on a machine learning model.

scholarly journals Deep Learning Generates Custom-made Logistic Regression Models for Explaining how Breast Cancer Subtypes are Classified

2021 ◽  
Author(s):  
Takuma Shibahara ◽  
Chisa Wada ◽  
Yasuho Yamashita ◽  
Kazuhiro Fujita ◽  
Masamichi Sato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Deep Learning ◽  
Linear Model ◽  
Regression Models ◽  
Learning Model ◽  
Logistic Regression Models ◽  
Custom Made ◽  
Meta Learning ◽  
Deep Learning Model

Abstract Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis but does not explain each subtype’s mechanism. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. To reveal the mechanisms embedded in the PAM50 subtypes, we developed an explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample. We developed an explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample. Logistic regression is familiar to physicians, and we can use it to analyze which genes are important for prediction. The custom-made logistic regression models generated by the point-wise linear model used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. Analyzing the point-wise linear model’s inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways.

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