Papers by Keyword: Prediction

Abstract: Stress relaxation is one of the methods used to characterize polymer foam materials. Stress relaxation data are valuable and provide important information, as they can prevent failure or unsafe usage of these materials under different loads. The aim of this study is to introduce the artificial neural network (ANN) technique for predicting the stress relaxation of polymer foam over time. The neural network model was constructed with relaxation time, stress, and strain as input parameters, and normalized stress relaxation as the output. The results demonstrate that the ANN model achieved highly accurate predictions for stress.

Abstract: Near InfraRed Spectroscopy (NIRS) has emerged as a promising non-destructive method for wood analysis. In this study, the efficacy of NIRS in predicting the wood specific gravity (WSG) of Ravenala madagascariensis, an endemic non-woody species of Madagascar was assessed. The optimal model, employing "SNV (standard normal variate) + DT (detrending)" pre-treatment and utilizing 11 latent variables, exhibited interesting performance metrics, including an RMSEcv of 0.013 g.cm^-3, R²cv of 0.73, and RPDcv of 2.76. Additionally, in independent validation, the model achieved an R² of 0.70 and an RPD of 2.17, with 11 numbers of latent variables. The predictive model's application unveiled significant radial variability in WSG within Ravenala madagascariensis. Specifically, the central zone exhibited lower density (average of 0.082 g.cm^-³) than the peripheral zone (0.12 g.cm^-³), with a highly significant difference (>0.1% threshold). Furthermore, there was a significant interaction effect between radial portion and compartment on WSG, exceeding a threshold of 1%. However, no such significant effects were observed for radial portion×sites interaction at the 5% significance level. This study contributes valuable insights into the wood properties of this endemic species, enhancing the understanding of its ecological and physical significance.

Abstract: Comparative analysis of two models for predicting residual resource of polymer composite, one proposed by specialists from RS Technologies Inc and the other proposed by Institute of Oil and Gas Problems SB RAS (Russia), was carried out to obtain service life of composite poles for power transmission lines manufactured by RS Technologies Inc (Canada) for cold climate of Republic of Sakha (Yakutia). Models are based on experimental studies of strength of materials during accelerated and full-scale climatic tests. The difference lies in the presence of parameters of climatic zone and test methods in the first model, while the second model considers changes in physical and mechanical properties and structure of materials during aging under conditions of full-scale exposure and accelerated climatic tests. Comparison of results of predicting the durability of fiberglass products in cold climate of Yakutsk (Russia) according to Institute of Oil and Gas Problems SB RAS model and similar products in Calgary (Canada) climate (the closest in terms of climate conditions) according to RS Technologies Inc model showed the same results. Service life of composite support material was approximately 120 years with specified level of permissible decrease in characteristic property index of 75% of the original.

Abstract: Clinical decision-making in health care is even now inspired by data-driven computer forecasts or suggestions. A range of machine learning functions has recently been shown in clinical works, particularly for result prediction patterns spanning from humanity to stroke. We investigate the state of the art in relevant subjects such as data point treatment, interpretation, and simulation assessment in the framework of outcome prediction models improved utilizing data as automated health data. We also look at the flaws in widely used modeling assumptions and offer suggestions for further research

Abstract: Crime prediction is a unique approach to identify and to find pattern trends of crime. Prediction means, using analysis and learning techniques, to find predictive actions of a specific activity and this is found to be effective in doing predictive analysis for various tasks such as crime prediction. The aim of this paper is to implement an approach for the problem in predicting the number of cases of crime happening in different parts of India. During the research we considered the machine learning model Random Forest and used the same for the prediction for crime. The prediction metrics used in this model are taken from feature selection technique. This technique increases the efficiency and accuracy of the prediction and also to avoid the model from over fitting. This model was tested on the crime data of India.

Abstract: The increase in new cars and customers' economic inability, global sales of old cars are expanding. As a result, there exists a pressing need for a second-hand automobile method for predicting prices that accurately calculates the value of a car based on number of factors. In the current circumstance, the existing system involves a mechanism in which a seller sets a price at random and the buyer has no knowledge of the car or its value. In fact, the seller doesn't even know the current value of the car or the price at which the car should be sold. To solve this problem, we have developed a very effective model. Regression algorithms are used to provide continuous values as output rather than classified values. This allows for the prediction of the car's real price rather than its price range.

Abstract: The worldwide society was devastated by the 2019 coronavirus illness (COVID19) epidemic in Wuhan, China, which overloaded advanced medical systems around the world. The World Health Organization (WHO) is constantly monitoring and responding to the pandemic. The current rapid and exponential development in patient numbers necessitates the use of AI technology to forecast possible outcomes of infected individuals in order to provide suitable therapy. The goal is to find the machine learning-based solution that best fits the Covid19 vaccination predictions with the highest accuracy. Variable identification, univariate analysis, bivariate and multivariate analysis, missing value handling and data validation analysis, data cleaning / preparation, and data validation analysis are all accomplished using supervised machine learning technology (SMLT). Various types of data, such as visualisation, are gathered. For the entire given dataset. Proposal of a machine learning-based method for accurately predicting the suitability of Covid19 vaccine prediction.

Abstract: Anticipating an alloy's corrosion resistance is essential to avoid product failure and reduce costs. Research and analyze the corrosion resistance of Al-Cu, Al-Zn and Al-Cu-Zn alloys based on the analytical balance of the elements according to weight, thermodynamic, metallurgical rules on metal alloys, kinetic and other properties. The purpose of this study is to determine the corrosion resistance of Al-5-wt% Cu, Al-5-wt% Zn and Al-5-wt% Cu-5-wt% Zn alloys based on the analytical calculation. Based on the analytical calculation results, the Al-Zn-Cu alloy has the best corrosion resistance with a corrosion rate of 0.4375 mmpy. Next is the Al-Cu alloy with a corrosion rate of 0.4634 mmpy. While Al-Zn alloy has the lowest strength with a corrosion rate of 0.4828 mmpy. Based on standard EMF potential values for these three alloys. Al-Zn alloys are most active with an value of-1.61 V, followed by Al-Zn-Cu alloys with an value of - 1.60 V, and the noblest Al-Cu alloy has the most positive value of-1.56 V. Faraday's law to get corrosion rates of the anode and cathode materials. In the third reaction, the exothermic alloy has a positive value of so the exothermic reaction occurs.

Abstract: Pyrolysis of combustible municipal solid waste (MSW) is an environmentally friendly waste to energy process that produces an ecological bio-oil with a high-energy value. However, the challenge is to obtain the desired products in considerable quantities, of good quality, and at low cost. The present work objective is to evaluate combustible MSW potential available in Morocco and their recovery in bio-oil produced by pyrolysis. An evaluation was conducted based on the MSW characterization for different Moroccan cities. It shows that Morocco has significant potential in good quality RDF, having a high calorific value and a low environmental impact. The yield of bio-oil that can be produced by pyrolysis of the dry part of municipal waste for different Moroccan cities was estimated using an appropriate model. The average total bio-oil yield estimated for each city is 45 wt%. Besides, the high calorific value fraction of bio-oil derived from Moroccan RDF will cover ~45% of the country's fuel-oil needs.

Abstract: A total of seven beam specimens were carried out to investigate the shear behavior of reinforced concrete (RC) beams strengthened by carbon fiber reinforced polymer (CFRP) grid with epoxy mortar, considering various shear span ratios, concrete strengths and intervals of CFRP grid. According to the test results, the nonlinear regression analysis was conducted and further a prediction method of shear capacity was proposed for CFRP grid-strengthened RC beams. The results showed that the shear behavior of strengthened beams were obviously improved, mainly due to CFRP grid. The shear strengthening effects of CFRP grid not only had a relation to the intervals of grids, but also were closely related to the shear span ratios and the concrete strengths, and thus both of them also cannot be ignored in design. The novel prediction method proposed in this paper, was verified by collecting data and regarded as a good predictor.