جستجوی مقالات مرتبط با کلیدواژه « regression » در نشریات گروه « برق »

Data represents a compendium of information that perpetually expands with each passing moment, contributed by individuals worldwide. Within the domain of medical science, this reservoir of data accumulates at an almost exponential rate, doubling in volume annually. The emergence of advanced machine learning tools and techniques, subsequent to a substantial evolution in data mining strategies, has bestowed the capacity to glean insights and discern concealed patterns from vast datasets, thus enabling extensive analytical pursuits. This study delves into the application of machine learning algorithms to enhance societal well-being by harnessing the transformative potential of machine learning advancements in the domain of blood glucose concentration estimation through regression analysis. The culmination of this investigation involves establishing a correlation between glucose concentration and hematocrit volume. The dataset employed for this research is sourced from clinically validated electrochemical glucose sensors (commonly referred to as glucose strips). It encompasses diverse levels of both glucose concentration and hematocrit volume, the latter being furnished by an undisclosed source to ensure copyright compliance. This dataset comprises four distinct variables, and the aim of this research involves training the dataset using regression techniques to predict two of these variables. Our results indicate that when utilizing linear regression, the R2 score for GC is approximately 0.916, whereas for HV, it reaches around 0.537. In contrast, employing the support vector regressor yielded R2 scores of about 0.961 for GC and 0.506 for HV.

Recently there has been a surge in the usage of metaheuristic algorithms to design materials with optimum performance. In this article, one such recently proposed metaheuristic algorithm called RPSOLC (Repulsive Particle Swarm Optimization with Local search and Chaotic perturbation) has been used to design diamond-like carbon (DLC) thin films having better hardness. Based on a Box-Behnken design, 15 independent experiments on DLC deposition are conducted in a PECVD (plasma-enhanced chemical vapor deposition) setup by varying the CH4-Argon flow rate, hydrogen flow rate and the deposition temperature. The nano-hardness of the DLCs are evaluated using nano-indention tests. The hardness is then expressed as the function of the three process parameters using a polynomial regression metamodel. Finally, the metamodel is optimized using RPSOLC and compared with optimal predictions of a traditional GA. It is seen that RPSOLC has faster convergence and is more reliable than the GA. In general, a high H2 flow rate along with low CH4-Ar flow rate and high temperature is found to be beneficial in improving the hardness.

Most of the studies on phenotype differences, including some diseases, are based on studying the areas of the genome called Single Nucleotide Polymorphism (SNP). Some SNPs on their own and some by interacting with other SNPs play an important role in any phenotype or specific disease. Various models, including regression models, are designed and implemented for prediction of these diseases. As the phenotypes are both quantitative and binary, linear regression is used for models predicting quantitative ones, which is only based on the number of minor alleles per SNP, and logistic regression is used for binary ones like complex diseases. Since complex diseases are not caused only by independent SNPs, but by the interaction of a large number of SNPs, which mostly exceeds the number of samples, penalized logistic regressions are counted to be a better choice. These models, therefore, can overcome the limitation of ordinary logistic regression on high-dimensional SNP datasets. In this paper, three regression models, including Ridge, Lasso and Elastic Net (EN), were implemented on 10000 samples of the SNP datasets of OWKIN-Inserm Institute to predict the risk of a specific disease (undisclosed for confidentiality reasons). Among these three, the Lasso model with minimizer lambda indicated higher accuracy (73.73%) and AUC (83.54%). The model is also less complex since it eliminates less related features as much as possible and keeps only the most informative ones. Besides, getting better results with Lasso indicates that multicollinearity is either not existed between variables or is low that can be neglected.

Fire is a major factor in the development of some plant communities, especially those exposed to lightning. Lightning is almost the main cause of natural fires in most plant communities. Fire is effective in the evolution of various species of forests, pastures, and shrubs in arid regions of the world's Mediterranean regions. Remote sensing and geographic information systems are appropriate in assessing the severity of burns. In this study, the intensity of the fire in Gorgan forests is evaluated and examined. The period of the study area was from 2013 to 2017 and Landsat 8 satellite imagery was used. First, the fire points were identified within an area of 500 meters by the IDW method. Then, by using NDVI, NBR, and dNBR indicators, fire points were evaluated and fire points were marked with red pixels which is clear in the two pictures before and after the fire. Finally, it was concluded that the NBR and dNBR index are the most accurate indicators with an accuracy of more than 74%.

Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase of forecast accuracy. In this paper, different factors were studied in comparison to the previous studies on gold price forecast. In terms of time span, the collected data were divided into three groups of daily, monthly and annually. The conducted tests using new factors indicate accuracy improvement up to 2% in neural networks methods, 7/3% in time series method and 5/6% in linear regression method.