scholarly journals RESEARCH OF APPLICATIONS OF MACHINE LEARNING ALGORITHMS IN IMPROVING OPC SOLUTIONS

2020 ◽  
Author(s):  
Pavel Tryasoguzov ◽  
Georgiy Teplov ◽  
Alexey Kuzovkov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Network Models ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Neural Network Models ◽  
Machine Learning Methods ◽  
Applications Of Machine Learning ◽  
Topological Drawing

In this paper the effectiveness of machine learning methods for solving OPC problems was consider. The task was to determine the direction of displacement and the amount of displacement of the boundary of the segment of the topological drawing. The generated training database was used to train regression, random forest, gradient boosting, and feedforward convolutional neural network models.

scholarly journals COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS AND REGRESSIONS FOR CAR PRICE PREDICTION

2019 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Random Forest ◽  
Network Models ◽  
Gradient Boosting ◽  
Learning Models ◽  
Neural Network Models ◽  
Boosting Algorithms ◽  
Machine Learning Models

The purpose of the research described in this article is a comparative analysis of the predictive qualities of some models of machine learning and regression. The factors for models are the consumer characteristics of a used car: brand, transmission type, drive type, engine type, mileage, body type, year of manufacture, seller's region in Ukraine, condition of the car, information about accident, average price for analogue in Ukraine, engine volume, quantity of doors, availability of extra equipment, quantity of passenger’s seats, the first registration of a car, car was driven from abroad or not. Qualitative variables has been encoded as binary variables or by mean target encoding. The information about more than 200 thousand cars have been used for modeling. All models have been evaluated in the Python Software using Sklearn, Catboost, StatModels and Keras libraries. The following regression models and machine learning models were considered in the course of the study: linear regression; polynomial regression; decision tree; neural network; models based on "k-nearest neighbors", "random forest", "gradient boosting" algorithms; ensemble of models. The article presents the best in terms of quality (according to the criteria R2, MAE, MAD, MAPE) options from each class of models. It has been found that the best way to predict the price of a passenger car is through non-linear models. The results of the modeling show that the dependence between the price of a car and its characteristics is best described by the ensemble of models, which includes a neural network, models using "random forest" and "gradient boosting" algorithms. The ensemble of models showed an average relative approximation error of 11.2% and an average relative forecast error of 14.34%. All nonlinear models for car price have approximately the same predictive qualities (the difference between the MAPE within 2%) in this research.

scholarly journals Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2927
Author(s):  
Jiyeong Hong ◽  
Seoro Lee ◽  
Joo Hyun Bae ◽  
Jimin Lee ◽  
Woon Ji Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Dam Inflow

Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

scholarly journals An Assessment of Machine Learning Integrated Autonomous Waste Detection and Sorting of Municipal Solid Waste

2021 ◽  
Vol 20 (4) ◽  
Author(s):  
Sonam Chaturvedi ◽  
Bikarama Prasad Yadav ◽  
Nihal Anwar Siddiqui
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Network Models ◽  
Machine Learning Algorithms ◽  
Health It ◽  
Neural Network Models ◽  
The Public ◽  
Air Jet

Municipal solid waste deposition in metropolitan areas has become a major concern that, if not addressed, can lead to environmental degradation and possibly endanger human health. It is important to adopt a smart waste management system in place to cope with a range of waste materials. This research aims to develop a smart modelling method that could accurately predict and forecast the production of municipal solid waste. An integrated convolution neural network and air-jet system-based framework developed for pre-processing and data integration were developed. The results showed that machine learning algorithms could be used to detect different types of waste with high accuracy. The best performers were obtained from neural network models, which captured 72% of the information variation. The method proposed in this study demonstrates the feasibility of developing tools to assist urban waste through the supply, pre-processing, integration, and modelling of data accessible to the public from a variety of sources.

scholarly journals Development of digital twin of CNC unit based on machine learning methods

2019 ◽  
Vol 19 (1) ◽  
pp. 45-55 ◽  
Author(s):  
Yu. G. Kabaldin ◽  
D. A. Shatagin ◽  
M. S. Anosov ◽  
A. M. Kuzmishina
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dynamic Stability ◽  
Network Models ◽  
Sensor System ◽  
Cnc Machine ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Digital Twins

Introduction. It is shown that the digital twin (electronic passport) of a CNC machine is developed as a cyber-physical system. The work objective is to create neural network models to determine the operation of a CNC machine, its performance and dynamic stability under cutting.Materials and Methods. The development of mathematical models of machining processes using a sensor system and the Industrial Internet of Things is considered. Machine learning methods valid for the implementation of the above tasks are evaluated. A neural network model of dynamic stability of the cutting process is proposed, which enables to optimize the machining process at the stage of work preparation. On the basis of nonlinear dynamics approaches, the attractors of the dynamic cutting system are reconstructed, and their fractal dimensions are determined. Optimal characteristics of the equipment are selected by input parameters and debugging of the planned process based on digital twins.Research Results. Using machine learning methods allowed us to create and explore neural network models of technological systems for cutting, and the software for their implementation. The possibility of applying decision trees for the problem of diagnosing and classifying malfunctions of CNC machines is shown.Discussion and Conclusions. In real production, the technology of digital twins enables to optimize processing conditions considering the technical and dynamic state of CNC machines. This provides a highly accurate assessment of the production capacity of the enterprise under the development of the production program. In addition, equipment failures can be identified in real time on the basis of the intelligent analysis of the distributed sensor system data.

scholarly journals Neural Network Models for Empirical Finance

2020 ◽  
Vol 13 (11) ◽  
pp. 265
Author(s):  
Hector F. Calvo-Pardo ◽  
Tullio Mancini ◽  
Jose Olmo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Asset Allocation ◽  
Ensemble Methods ◽  
Network Models ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Out Of Sample

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to alleviate the problem of overfitting. We also review other features of machine learning methods, such as the selection of hyperparameters, the role of the architecture of a deep neural network for model prediction, or the importance of using different optimization routines for model selection. The review also considers the issue of model uncertainty and presents state-of-the-art methods for constructing prediction intervals using ensemble methods, such as bootstrap and Monte Carlo dropout. These methods are illustrated in an out-of-sample empirical forecasting exercise that compares the performance of machine learning methods against conventional time series models for different financial indices. These results are confirmed in an asset allocation context.

scholarly journals On increasing the productive time of drilling oil and gas wells using machine learning methods

Georesursy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 79-85
Author(s):  
Anatoliy N. Dmitrievsky ◽  
Alexander G. Sboev ◽  
Nikolai A. Eremin ◽  
Alexander D. Chernikov ◽  
Aleksandr V. Naumov ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drilling Process ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods

The article is devoted to the development of a hybrid method for predicting and preventing the development of troubles in the process of drilling wells based on machine learning methods and modern neural network models. Troubles during the drilling process, such as filtrate leakoff; gas, oil and water shows and sticking, lead to an increase in unproductive time, i.e. time that is not technically necessary for well construction and is caused by various violations of the production process. Several different approaches have been considered, including based on the regression model for predicting the indicator function, which reflects an approach to a developing trouble, as well as anomaly extraction models built both on basic machine learning algorithms and using the neural network model of deep learning. Showing visualized examples of the work of the developed methods on simulation and real data. Intelligent analysis of Big Geodata from geological and technological measurement stations is based on well-proven machine learning algorithms. Based on these data, a neural network model was proposed to prevent troubles and emergencies during the construction of wells. The use of this method will minimize unproductive drilling time.

scholarly journals FEATURES OF CONSTRUCTION AND BASIC DIRECTIONS OF DEVELOPMENT OF VIRTUAL DIGITAL ASSISTANTS

2020 ◽  
Vol 1 (9) ◽  
pp. 140-148
Author(s):  
Oleksandra Tsyra ◽  
Nataliia Punchenko ◽  
Oleksii Fraze-Frazenko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Network Models ◽  
Large Data ◽  
Unmanned Vehicles ◽  
Machine Learning Algorithms ◽  
Information Modeling ◽  
Main Task ◽  
Neural Network Models

The article analyzes the main aspects of creating virtual assistants that are part of intelligent computer programs – artificial intelligence systems (AI). The main task of “artificial intelligence” is to ensure effective communication of intelligent robotic systems (including unmanned vehicles) with humans. The basis of the above is in-depth training (systematic machine translation, speech recognition, processing of complex texts in natural languages, computer vision, automation of driving, etc.). This machine learning subsystem can be characterized using neural network models that mimic the brain. Any neural network model learns from large data sets, so it acquires some “skills”, but how it uses them remains for engineers, which ultimately becomes one of the most important problems for many deep learning applications. The reason is that such a model is formal and without an understanding of the logic of its actions. This raises the question: is it possible to increase the level of trust in such systems based on machine learning? Machine learning algorithms are complex mathematical descriptions and procedures and have a growing impact on people's lives. As the decision is increasingly determined by the algorithms, they become less transparent and understandable. Based on the foregoing, the paper considers the issues of the technological component and the algorithms of virtual digital assistants, conducts information modeling based on the conceptual model of the interaction of the virtual assistant with the database, and analyzes the scope and further development of the IT-sphere.

scholarly journals Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45993-45999
Author(s):  
Ung Yang ◽  
Seungwon Oh ◽  
Seung Gon Wi ◽  
Bok-Rye Lee ◽  
Sang-Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Network Models ◽  
Convolution Neural Network ◽  
Neural Network Models

Prediction of sports attendance: A comparative analysis

2020 ◽  
pp. 175433712098313
Author(s):  
Mehmet Şahin ◽  
Murat Uçar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
National Football League ◽  
Relevant Literature ◽  
Performance Evaluations ◽  
Influential Factor ◽  
Gradient Boosting ◽  
Home Team ◽  
Machine Learning Methods

In this study, a comparative analysis for predicting sports attendance demand is presented based on econometric, artificial intelligence, and machine learning methodologies. Data from more than 20,000 games from three major leagues, namely the National Basketball Association (NBA), National Football League (NFL), and Major League Baseball (MLB), were used for training and testing the approaches. The relevant literature was examined to determine the most useful variables as potential regressors in forecasting. To reveal the most effective approach, three scenarios containing seven cases were constructed. In the first scenario, each league was evaluated separately. In the second scenario, the three possible combinations of league pairings were evaluated, while in the third scenario, all three leagues were evaluated together. The performance evaluations of the results suggest that one of the machine learning methods, Gradient Boosting, outperformed the other methods used. However, the Artificial Neural Network, deep Convolutional Neural Network, and Decision Trees also provided productive and competitive predictions for sports games. Based on the results, the predictions for the NBA and NFL leagues are more satisfactory than the predictions of the MLB, which may be caused by the structure of the MLB. The results of the sensitivity analysis indicate that the performance of the home team is the most influential factor for all three leagues.

scholarly journals Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 771
Author(s):  
Toshiya Arakawa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Markov Models ◽  
Tracking System ◽  
Video Tracking ◽  
Training Data ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.

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