Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of the study is the generated data sets obtained on the basis of theoretical stress relaxation curves. Tables of initial data for training models for all samples are presented, a statistical analysis of the characteristics of the initial data sets is carried out. The total number of numerical experiments for all samples was 346020 variations. When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. As a result of the study, intelligent models were developed to determine the rheological parameters of polymers included in the generalized non-linear Maxwell-Gurevich equation (initial relaxation viscosity, velocity modulus) using generated data sets for the EDT-10 epoxy binder as an example. Based on the results of testing the models, the quality of the models was assessed, graphs of forecasts for trainees and test samples, graphs of forecast errors were plotted. Intelligent models are based on the CatBoost algorithm and implemented in the Jupyter Notebook environment in Python. The constructed models have passed the quality assessment according to the following metrics: MAE, MSE, RMSE, MAPE. The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. Model performance estimates obtained during testing are valid.
Background: Pyogenic granuloma is a hyperplastic benign tumor. The most common intra-oral site is marginal gingiva. It is often occurred in the second decade of life, it has a strong tendency to recur after simple excision.
The aim of study: to evaluate the therapeutic advantages of diode laser (810-980 nm) in intraoral Pyogenic granuloma management.
Materials and method: A total of 28 patients (14 men and 14 females) were enrolled in this study and had their pyogenic granuloma surgically removed using a diode laser. All of the patients were given local anesthetic and went through the identical surgical procedure (cartridge containing 1 percent lidocaine with epinephrine 1:
... Show MoreThe study aimed to identify the treatment of the press image of the Great Return Marches in the French international news agency AFP by knowing the most important issues, their direction and the degree of interest in them. The study belongs to the descriptive research, and used the survey method, within the context of the content analysis method, and the researcher relied on the content analysis form tool and the interview tool to collect data. The study population is represented in the photos published by the French News Agency about the Great Return Marches during the period (end of March / 2018 until the end of November / 2019. The researcher chose an intentional sample using the Complete Census method. The study material represented
... Show MoreThe convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog
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The world witness quality jumps under radical change in the management and work styles, through adopting organizational learning as a process of continuous improvements in response to environmental stimulus, in the knowledge age and turbulent competitive environment the organizational success concept is shift from the view narrow for financial performance and marketshare to long term view which focus on building strategic capabilities that provide a sustainable competitive advantage.
Considering the strategic role assumed for the companies of the Ministry of Construction and Housing to play it, these companies have been chosen to be the field in which questions of th
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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The multiple linear regression model of the important regression models used in the analysis for different fields of science Such as business, economics, medicine and social sciences high in data has undesirable effects on analysis results . The multicollinearity is a major problem in multiple linear regression. In its simplest state, it leads to the departure of the model parameter that is capable of its scientific properties, Also there is an important problem in regression analysis is the presence of high leverage points in the data have undesirable effects on the results of the analysis , In this research , we present some of
... Show MoreBackground: Psychological stress is considered the major etiological factor precipitating myofacial pain and temporomandibular disorders.It is known that stress induce various adaptational responses of physiologic systems. The process includes increase in the activity of the hypothalamic-pituitary-adrenal axis which promotes cortisol secretion. Salivary cortisol has been used as a measure of free circulating cortisol levels.The use of salivary biomarkers has gained increased popularity since collecting samples is non-invasive and painless. The aim of thisstudy was to evaluate the level of cortisol in saliva among sample of university students having myofacial pain, during the final exam period and whether this finding could have a significa
... Show MoreIn light of the corona pandemic, educational institutions have moved to learning and teaching via the Internet and e-learning ,and this is considered a turning point in course of higher education in Iraq in particular and education in general, which generated a great challenge for educational institutions to achieve the highest possible levels in practices and processes to reach the highest quality of their outputs from graduate students to the labor market that auditing performance by adopting e-learning standards is one of the effective tools that help the management of educational institutions by providing information on the ex
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