scholarly journals Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling

2021 ◽  
Vol 18 (15) ◽  
pp. 7799
Author(s):  
Essam A. Rashed ◽  
Akimasa Hirata
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Economic Effects ◽  
Training Data ◽  
Healthcare Facilities ◽  
Forecasting Models ◽  
Machine Learning Model ◽  
Recent Emergence ◽  
Alpha Variant

The significant health and economic effects of COVID-19 emphasize the requirement for reliable forecasting models to avoid the sudden collapse of healthcare facilities with overloaded hospitals. Several forecasting models have been developed based on the data acquired within the early stages of the virus spread. However, with the recent emergence of new virus variants, it is unclear how the new strains could influence the efficiency of forecasting using models adopted using earlier data. In this study, we analyzed daily positive cases (DPC) data using a machine learning model to understand the effect of new viral variants on morbidity rates. A deep learning model that considers several environmental and mobility factors was used to forecast DPC in six districts of Japan. From machine learning predictions with training data since the early days of COVID-19, high-quality estimation has been achieved for data obtained earlier than March 2021. However, a significant upsurge was observed in some districts after the discovery of the new COVID-19 variant B.1.1.7 (Alpha). An average increase of 20–40% in DPC was observed after the emergence of the Alpha variant and an increase of up to 20% has been recognized in the effective reproduction number. Approximately four weeks was needed for the machine learning model to adjust the forecasting error caused by the new variants. The comparison between machine-learning predictions and reported values demonstrated that the emergence of new virus variants should be considered within COVID-19 forecasting models. This study presents an easy yet efficient way to quantify the change caused by new viral variants with potential usefulness for global data analysis.

scholarly journals A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Gaurav Vishwakarma ◽  
Mohammad Atif Faiz Afzal ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Organic Molecules ◽  
Learning Model ◽  
Training Data ◽  
Refractive Indices ◽  
Learning Models ◽  
Deep Learning Model ◽  
Machine Learning Models

<div><div><div><p>We present a multitask, physics-infused deep learning model to accurately and efficiently predict refractive indices (RIs) of organic molecules, and we apply it to a library of 1.5 million compounds. We show that it outperforms earlier machine learning models by a significant margin, and that incorporating known physics into data-derived models provides valuable guardrails. Using a transfer learning approach, we augment the model to reproduce results consistent with higher-level computational chemistry training data, but with a considerably reduced number of corresponding calculations. Prediction errors of machine learning models are typically smallest for commonly observed target property values, consistent with the distribution of the training data. However, since our goal is to identify candidates with unusually large RI values, we propose a strategy to boost the performance of our model in the remoter areas of the RI distribution: We bias the model with respect to the under-represented classes of molecules that have values in the high-RI regime. By adopting a metric popular in web search engines, we evaluate our effectiveness in ranking top candidates. We confirm that the models developed in this study can reliably predict the RIs of the top 1,000 compounds, and are thus able to capture their ranking. We believe that this is the first study to develop a data-derived model that ensures the reliability of RI predictions by model augmentation in the extrapolation region on such a large scale. These results underscore the tremendous potential of machine learning in facilitating molecular (hyper)screening approaches on a massive scale and in accelerating the discovery of new compounds and materials, such as organic molecules with high-RI for applications in opto-electronics.</p></div></div></div>

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals Development of a Cable Damage Detection Deep Learning Method based on Acceleration Response of Cable-Stayed Bridge

2021 ◽  
Vol 12 (6) ◽  
pp. 638-647
Author(s):  
Park Gi-Hun Et.al
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Damage Detection ◽  
Learning Model ◽  
Learning Method ◽  
Acceleration Response ◽  
Damage Location ◽  
Cable Stayed Bridge ◽  
Machine Learning Model ◽  
Cable Damage

The purpose of this thesis was to select a cable-stayed bridge to which external force may cause damage as the subject, to develop a damage detection deep learning method capable of detecting cable damage, and to test and verify the developed damage detection deep learning method. The damage detection method was developed as a system that utilizes the acceleration response of a structure measured for maintenance purposes. To extract information capable of identifying the damage locations from among the measured acceleration responses, a CNN ID was used to develop the damage detection deep learning method. The developed damage detection deep learning method was developed in a way not independently arranging 1 machine learning model per each measuring point and finally predicting the damage location based on the decision-making results collected from each machine learning model. The developed damage detection deep learning method performed the learning per each machine learning model by utilizing the acceleration response of a structure acquired based on the preliminary damage test. Finally, the damage detection deep learning method that completed the learning verified the cable damage location detection performance by utilizing the data acquired based on the cable-stayed bridge damage test. As a result, it was confirmed that the developed damage detection deep learning method predicted the damage location of a cable-stayed bridge at an average accuracy of 89%. In the current research, only the cable-stayed bridge of the Seohaegyo Bridge was studied, but in the improved study, the research will be conducted on other bridges and damage assessment will be conducted on all cables.

scholarly journals A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Sijie Chen ◽  
Wenjing Zhou ◽  
Jinghui Tu ◽  
Jian Li ◽  
Bo Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Training Data ◽  
Diagnostic Efficiency ◽  
Metastatic Tumors ◽  
Pathological Conditions ◽  
Machine Learning Model ◽  
Independent Test ◽  
Tumor Types ◽  
Fold Cross Validation

PurposeEstablish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency.MethodsAfter deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set.ResultsSelecting features with around 800 genes for training, theR2-score of a 10-fold CV of training data can reach 96.38%, and theR2-score of test data can reach 83.3%.ConclusionThese findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.

scholarly journals Quantitative Toxicity Prediction via Ensembling of Heterogeneous Predictors

2019 ◽  
Author(s):  
Abdul Karim ◽  
Vahid Riahi ◽  
Avinash Mishra ◽  
Abdollah Dehzangi ◽  
M. A. Hakim Newton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Models ◽  
Individual Performance ◽  
Learning Model ◽  
Data Representation ◽  
Toxicity Prediction ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Benchmark Datasets

Abstract Representing molecules in the form of only one type of features and using those features to predict their activities is one of the most important approaches for machine-learning-based chemical-activity-prediction. For molecular activities like quantitative toxicity prediction, the performance depends on the type of features extracted and the machine learning approach used. For such cases, using one type of features and machine learning model restricts the prediction performance to specific representation and model used. In this paper, we study quantitative toxicity prediction and propose a machine learning model for the same. Our model uses an ensemble of heterogeneous predictors instead of typically using homogeneous predictors. The predictors that we use vary either on the type of features used or on the deep learning architecture employed. Each of these predictors presumably has its own strengths and weaknesses in terms of toxicity prediction. Our motivation is to make a combined model that utilizes different types of features and architectures to obtain better collective performance that could go beyond the performance of each individual predictor. We use six predictors in our model and test the model on four standard quantitative toxicity benchmark datasets. Experimental results show that our model outperforms the state-of-the-art toxicity prediction models in 8 out of 12 accuracy measures. Our experiments show that ensembling heterogeneous predictor improves the performance over single predictors and homogeneous ensembling of single predictors.The results show that each data representation or deep learning based predictor has its own strengths and weaknesses, thus employing a model ensembling multiple heterogeneous predictors could go beyond individual performance of each data representation or each predictor type.

Deep Learning Applications in Medical Imaging

2021 ◽  
pp. 178-208
Author(s):  
S. Sasikala ◽  
S. J. Subhashini ◽  
P. Alli ◽  
J. Jane Rubel Angelina
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Learning Model ◽  
Learning Models ◽  
Driving System ◽  
Machine Learning Model ◽  
Car Driving ◽  
Machine Learning Models

Machine learning is a technique of parsing data, learning from that data, and then applying what has been learned to make informed decisions. Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way, but it has different capabilities. The main difference between deep and machine learning is, machine learning models become well progressively, but the model still needs some guidance. If a machine learning model returns an inaccurate prediction, then the programmer needs to fix that problem explicitly, but in the case of deep learning, the model does it by itself. Automatic car driving system is a good example of deep learning. On other hand, Artificial Intelligence is a different thing from machine learning and deep learning. Deep learning and machine learning both are the subsets of AI.

scholarly journals Numerical Investigations on the Shape Optimization of Stainless-Steel Ring Joint with Machine Learning

2020 ◽  
Vol 11 (1) ◽  
pp. 223
Author(s):  
Minsoo Kim ◽  
Sarang Yi ◽  
Seokmoo Hong
Keyword(s):  
Machine Learning ◽  
Finite Element ◽  
Learning Model ◽  
Training Data ◽  
Metal Ring ◽  
Rubber Ring ◽  
Water Pipes ◽  
Metal Rings ◽  
Machine Learning Model ◽  
Joint Method

Since pipes used for water pipes are thin and difficult to fasten using welding or screws, they are fastened by a crimping joint method using a metal ring and a rubber ring. In the conventional crimping joint method, the metal ring and the rubber ring are arranged side by side. However, if water leaks from the rubber ring, there is a problem that the adjacent metal ring is rapidly corroded. In this study, to delay and minimize the corrosion of connected water pipes, we propose a spaced crimping joint method in which metal rings and rubber rings are separated at appropriate intervals. This not only improves the contact performance between the connected water pipes but also minimizes the load applied to the crimping jig during crimping to prevent damage to the jig. For this, finite element analyses were performed for the crimp tool and process analysis, and the design parameters were set as the curling length at the top of the joint, the distance between the metal rings and rubber rings, and the crimp jig radius. Through FEA of 100 cases, data to be trained in machine learning were acquired. After that, training data were trained on a machine learning model and compared with a regression model to verify the model’s performance. If the number of training data is small, the two methods are similar. However, the greater the number of training data, the higher the accuracy predicted by the machine learning model. Finally, the spaced crimping joint to which the derived optimal shape was applied was manufactured, and the maximum pressure and pressure distribution applied during compression were obtained using a pressure film. This is almost similar to the value obtained by finite element analysis under the same conditions, and through this, the validity of the approach proposed in this study was verified.

scholarly journals A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Gaurav Vishwakarma ◽  
Mohammad Atif Faiz Afzal ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Organic Molecules ◽  
Learning Model ◽  
Training Data ◽  
Refractive Indices ◽  
Learning Models ◽  
Deep Learning Model ◽  
Machine Learning Models

<div><div><div><p>We present a multitask, physics-infused deep learning model to accurately and efficiently predict refractive indices (RIs) of organic molecules, and we apply it to a library of 1.5 million compounds. We show that it outperforms earlier machine learning models by a significant margin, and that incorporating known physics into data-derived models provides valuable guardrails. Using a transfer learning approach, we augment the model to reproduce results consistent with higher-level computational chemistry training data, but with a considerably reduced number of corresponding calculations. Prediction errors of machine learning models are typically smallest for commonly observed target property values, consistent with the distribution of the training data. However, since our goal is to identify candidates with unusually large RI values, we propose a strategy to boost the performance of our model in the remoter areas of the RI distribution: We bias the model with respect to the under-represented classes of molecules that have values in the high-RI regime. By adopting a metric popular in web search engines, we evaluate our effectiveness in ranking top candidates. We confirm that the models developed in this study can reliably predict the RIs of the top 1,000 compounds, and are thus able to capture their ranking. We believe that this is the first study to develop a data-derived model that ensures the reliability of RI predictions by model augmentation in the extrapolation region on such a large scale. These results underscore the tremendous potential of machine learning in facilitating molecular (hyper)screening approaches on a massive scale and in accelerating the discovery of new compounds and materials, such as organic molecules with high-RI for applications in opto-electronics.</p></div></div></div>

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