Monitoring of the technical condition of tracks based on machine learning

2019 ◽  
Vol 234 (7) ◽  
pp. 702-708
Author(s):  
Bartosz Firlik ◽  
Maciej Tabaszewski
Keyword(s):  
Machine Learning ◽  
Threshold Value ◽  
Technical Condition ◽  
Learning System ◽  
Mean Square ◽  
Vibration Acceleration ◽  
Vehicle Velocity ◽  
Track Condition ◽  
The Cost ◽  
The Impact

This paper presents the concept of a simple system for the identification of the technical condition of tracks based on a trained learning system in the form of three independent neural networks. The studies conducted showed that basic measurements based on the root mean square of vibration acceleration allow for monitoring the track condition provided that the rail type has been included in the information system. Also, it is necessary to select data based on the threshold value of the vehicle velocity. In higher velocity ranges (above 40 km/h), it is possible to distinguish technical conditions with a permissible error of 5%. Such selection also enables to ignore the impact of rides through switches and crossings. Technical condition monitoring is also possible at lower ride velocities; however, this comes at the cost of reduced accuracy of the analysis.

scholarly journals Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Qingsong Xi ◽  
Qiyu Yang ◽  
Meng Wang ◽  
Bo Huang ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Endometrial Thickness ◽  
Learning System ◽  
Embryo Selection ◽  
Selection Strategy ◽  
Application Study ◽  
Vitro Fertilization ◽  
The Impact

Abstract Background To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes. Results For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

scholarly journals Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 647
Author(s):  
Meijun Shang ◽  
Hejun Li ◽  
Ayaz Ahmad ◽  
Waqas Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Mean Square Error ◽  
Coarse Aggregate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Environmental Damage ◽  
Fine Aggregate ◽  
Mean Square ◽  
The Impact

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

scholarly journals Improvement of anisotropic porosity models with a merging technique

2018 ◽  
Vol 40 ◽  
pp. 06023
Author(s):  
Martin Bruwier ◽  
Pierre Archambeau ◽  
Sébastien Erpicum ◽  
Michel Pirotton ◽  
Benjamin Dewals
Keyword(s):  
Shallow Water ◽  
Flow Characteristics ◽  
Threshold Value ◽  
Computational Grid ◽  
Computational Time ◽  
Time Step ◽  
Shallow Water Models ◽  
The Cost ◽  
The Impact ◽  
Porosity Model

Anisotropic porosity shallow-water models are used to take into account detailed topographic information through porosity parameters multiplying the various terms of the shallow-water equations. A storage porosity is assigned to each cell to reflect the void fraction in the cell and a conveyance porosity is used at each edge to reproduce the impact of subgrid obstacles on the flux terms. To guaranty the numerical stability, the time step depends on the value of the porosity parameters. This may hamper severely the computational efficiency in the presence of cells with low values of storage porosity. Cartesian grids are particularly sensitive to such a case since the meshing stems directly from the choice of the grid size. In this paper, this problem is addressed by using an original merging technique consisting in merging cells with a storage porosity lower than a threshold value with neighbouring cells. The model was tested for modelling a prismatic channel with different orientations between the Cartesian computational grid and the channel direction. The results show that the standard anisotropic porosity model (without merging) improves the reproduction of the flow characteristics; but at the cost of a significantly higher computational time. In contrast, the computational time is drastically reduced and the accuracy preserved when the merging technique is used with the porosity model.

scholarly journals Individualized Embryo Selection Strategy Developed by Stacking Machine Learning Model for Better In Vitro Fertilization Outcomes: An Application Study.

2021 ◽  
Author(s):  
Qingsong XI ◽  
Qiyu YANG ◽  
Meng WANG ◽  
Bo HUANG ◽  
Bo ZHANG ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Endometrial Thickness ◽  
Learning System ◽  
Embryo Selection ◽  
Selection Strategy ◽  
Application Study ◽  
Vitro Fertilization ◽  
The Impact

Abstract Background: To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods: This was an application study including 7887 patients with 8585 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.Results: For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1+P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1×P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion: Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

Acceptance of Rules Generated by Machine Learning among Medical Experts

2001 ◽  
Vol 40 (05) ◽  
pp. 380-385 ◽  
Author(s):  
S. Mani ◽  
W. R. Shankle ◽  
M. J. Pazzani
Keyword(s):  
Machine Learning ◽  
Care Delivery ◽  
Medical Knowledge ◽  
Rule Learning ◽  
Learning System ◽  
Data Sets ◽  
Medical Databases ◽  
Monotonicity Constraints ◽  
Cost Of Health Care ◽  
The Cost

Summary Objectives: The aim was to evaluate the potential for monotonicity constraints to bias machine learning systems to learn rules that were both accurate and meaningful. Methods: Two data sets, taken from problems as diverse as screening for dementia and assessing the risk of mental retardation, were collected and a rule learning system, with and without monotonicity constraints, was run on each. The rules were shown to experts, who were asked how willing they would be to use such rules in practice. The accuracy of the rules was also evaluated. Results: Rules learned with monotonicity constraints were at least as accurate as rules learned without such constraints. Experts were, on average, more willing to use the rules learned with the monotonicity constraints. Conclusions: The analysis of medical databases has the potential of improving patient outcomes and/or lowering the cost of health care delivery. Various techniques, from statistics, pattern recognition, machine learning, and neural networks, have been proposed to “mine” this data by uncovering patterns that may be used to guide decision making. This study suggests cognitive factors make learned models coherent and, therefore, credible to experts. One factor that influences the acceptance of learned models is consistency with existing medical knowledge.

scholarly journals Credibility Detection on Twitter News Using Machine Learning Approach

2021 ◽  
Vol 13 (3) ◽  
pp. 1-10
Author(s):  
Marina Azer ◽  
◽  
Mohamed Taha ◽  
Hala H. Zayed ◽  
Mahmoud Gadallah
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Learning System ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Sources Of Information ◽  
Supervised Machine Learning Classifiers ◽  
False News ◽  
The Impact

Social media presence is a crucial portion of our life. It is considered one of the most important sources of information than traditional sources. Twitter has become one of the prevalent social sites for exchanging viewpoints and feelings. This work proposes a supervised machine learning system for discovering false news. One of the credibility detection problems is finding new features that are most predictive to better performance classifiers. Both features depending on new content, and features based on the user are used. The features' importance is examined, and their impact on the performance. The reasons for choosing the final feature set using the k-best method are explained. Seven supervised machine learning classifiers are used. They are Naïve Bayes (NB), Support vector machine (SVM), Knearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Maximum entropy (ME), and conditional random forest (CRF). Training and testing models were conducted using the Pheme dataset. The feature's analysis is introduced and compared to the features depending on the content, as the decisive factors in determining the validity. Random forest shows the highest performance while using user-based features only and using a mixture of both types of features; features depending on content and the features based on the user, accuracy (82.2 %) in using user-based features only. We achieved the highest results by using both types of features, utilizing random forest classifier accuracy(83.4%). In contrast, logistic regression was the best as to using features that are based on contents. Performance is measured by different measurements accuracy, precision, recall, and F1_score. We compared our feature set with other studies' features and the impact of our new features. We found that our conclusions exhibit high enhancement concerning discovering and verifying the false news regarding the discovery and verification of false news, comparing it to the current results of how it is developed.

scholarly journals The methodology for assessing the level of localization of agricultural tractors production

2020 ◽  
Vol 1 (5) ◽  
pp. 18-24
Author(s):  
Z.A. Godzhayev ◽  
◽  
A.V. Lavrov ◽  
V.G. Shevtsov ◽  
V.A. Zubina ◽  
...  
Keyword(s):  
Technological Development ◽  
Import Substitution ◽  
Threshold Value ◽  
Specific Model ◽  
Spare Parts ◽  
Russian Economy ◽  
The Government ◽  
Agricultural Tractors ◽  
The Cost ◽  
The Impact

The existing list of requirements for agricultural tractors to classify them as products manufac-tured in Russia is considered. An assessment of the impact of the requirements of the Government of the Russian Federation decree on the fulfillment of tasks for the development of the Russian economy was made. It is proposed to stimulate the creation of jobs by setting a threshold value for the level of localization in terms of cost indicator. An assessment of the technological need for agri-cultural tractors for the primary stimulation of the production of the most scarce equipment was car-ried out. An approach to motivate the technological development of production and improve the technical level of products of enterprises is proposed. It is indicated that it is necessary to take into account in the methodology the directions of the manufacturer's activity that are important for the agricultural consumer. An improved methodology for assessing the level of localization of produc-tion of agricultural mobile energy products is presented. The basic methodology was significantly adjusted with the transition from an arbitrary list of obsolete technological operations to the cost assessment of modern technologies, quantitative accounting of the scarcity of manufactured equip-ment, the volume of service, the development of R&D works, the operational provision of spare parts, etc. The level of localization, which is sufficient to recognize a specific model as produced in Russia, is calculated as the sum of the shares of the cost of the tractor using a number of reducing factors that take into account the progressive influence of localized production: the scarcity ratio of the manufactured model; the service network coefficient; R&D funding ratio; spare parts warehouse availability factor. The implementation of this methodology contributes to the solution of tasks for the development of the Russian economy: job creation; organization of production of scarce equip-ment (import substitution); promotion of innovative technologies; maintenance of service; devel-opment of research and development work; prompt provision of spare parts.

The Future of Advertising

2021 ◽  
pp. 151-166
Author(s):  
Elissa Moses ◽  
Kimberly Rose Clark ◽  
Norman J. Jacknis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Processing ◽  
Emotional Response ◽  
Optimum Placement ◽  
The Future ◽  
Effective Advertising ◽  
The Cost ◽  
Development Assessment ◽  
The Impact

This chapter summarizes the role that artificial intelligence and machine learning (AI/ML) are expected to play at every stage of advertising development, assessment, and execution. Together with advances in neuroscience for measuring attention, cognitive processing, emotional response, and memory, AI/ML have advanced to a point where analytics can be used to identify variables that drive more effective advertising and predict enhanced performance. In addition, the cost of computation has declined, making platforms to apply these tools much less expensive and within reach. The authors then offer recommendations for 1) understanding the clients/customers and users of the products and services that will be advertised, 2) aiding creativity in the process of designing advertisements, 3) testing the impact of advertisements, and 4) identifying the optimum placement of advertisements.

scholarly journals The Impact of Psychopathology, Social Adversity and Stress-relevant DNAm on Prospective Risk for Post-traumatic Stress: A Machine Learning Approach

2020 ◽  
Author(s):  
Agaz H. Wani ◽  
Allison E. Aiello ◽  
Grace S. Kim ◽  
Fei Xue ◽  
Chantel L. Martin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mean Square Error ◽  
Traumatic Stress ◽  
High Accuracy ◽  
Mean Square ◽  
Post Traumatic Stress ◽  
Social Adversity ◽  
Post Traumatic ◽  
Prospective Risk ◽  
The Impact

AbstractBackgroundA range of factors have been identified that contribute to greater incidence, severity, and prolonged course of post-traumatic stress disorder (PTSD), including: comorbid and/or prior psychopathology; social adversity such as low socioeconomic position, perceived discrimination, and isolation; and biological factors such as genomic variation at glucocorticoid receptor regulatory network (GRRN) genes. This complex etiology and clinical course make identification of people at higher risk of PTSD challenging. Here we leverage machine learning (ML) approaches to identify a core set of factors that may together predispose persons to PTSD.MethodsWe used multiple ML approaches to assess the relationship among DNA methylation (DNAm) at GRRN genes, prior psychopathology, social adversity, and prospective risk for PTS severity (PTSS).ResultsML models predicted prospective risk of PTSS with high accuracy. The Gradient Boost approach was the top-performing model with mean absolute error of 0.135, mean square error of 0.047, root mean square error of 0.217, and R2 of 95.29%. Prior PTSS ranked highest in predicting the prospective risk of PTSS, accounting for >88% of the prediction. The top ranked GRRN CpG site was cg05616442, in AKT1, and the top ranked social adversity feature was loneliness.ConclusionMultiple factors including prior PTSS, social adversity, and DNAm play a role in predicting prospective risk of PTSS. ML models identified factors accounting for increased PTSS risk with high accuracy, which may help to target risk factors that reduce the likelihood or course of PTSD, potentially pointing to approaches that can lead to early intervention.

scholarly journals Cloud Computing of E-learning

2018 ◽  
Vol 12 (8) ◽  
pp. 11 ◽  
Author(s):  
Tamara Almarabeh ◽  
Yousef Kh. Majdalawi
Keyword(s):  
Cloud Computing ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Learning System ◽  
Educational Institutions ◽  
Strong Trend ◽  
Information And Communication ◽  
E Learning ◽  
The Cost ◽  
The Impact

With considerable improvement in Information and Communication Technology (ICT) field, many areas of our lives have been affected, including the learning environmentE-learning is one such inventions and is linked to the use of electronic methods to support the E-learning process, which has become increasingly popular and has become a strong trend due to the enormous benefits it offers to learning environments. However, E-learning systems and its management require huge investments in information technology, and many educational institutions don't have enough budget to afford the cost, therefore cloud computing is the finest solution. It provides an effective mechanism which can allow of building a new mode of E-learning system. This paper, discusses the features of the E-learning system, describes the strategy of Cloud computing, and analyzes the impact of using Cloud Computing in E-learning.

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