Background & Objectives: Thyroid is a vital gland, which affect all of the body oragans such as heart, digestive system, kidney and so on. The intention of this research is to decreas in wrong determination of normal thyroid gland from abnormal using Boosting Algorithm. This Algorithm is a powerful method in diagnosis and prognosis. It iteratively grows base classifer on a sequence of reweighted datasets then takes a linear combination of consequencs and we hope improves accuracy at final.Material & Methods: A total of 103 patients’ data corrolated to November 2010 until November 2011 from Shoushtar salamat laboratory were analyzed for detemination thyroid gland state. Conventional decision trees and Boosting decision trees were made for diagnosis normal thyroid gland from abnormal thyroid gland using R softwere vedersion 3.0.1.Results: Our findings revealed that for conventional decision trees misclassification rate, sensitivity and specificity with test set were 0.088, 0.91 and 0.92 respectively .However these figures considered by Boosting desion trees were 0.029, 0.955 and 1 crrespondingly.Conclusion: The Boosting decision trees had possibily superior sucsses in diagnosis normal tiroid gland ftom unnormal. So using Boosting decisin trees propose in determination thyroid gland state.

The Boosting Algorithm is a hybrid Algorithm to reduce variance, a family of machine learning Algorithms in supervised learning. This Algorithm is a method to transform weak learning systems into strong systems based on the combination of different results. In this paper, mixture models with random effects are considered for small areas, where the errors follow the AR-GARCH model. To select the variable, machine learning Algorithms, such as Boosting Algorithms, have been proposed. Using simulated and tax liability data, the Boosting Algorithm's performance is studied and compared with classical variable selection methods, such as the step-by-step method.

Introduction: The present study discusses the importance of having a predictive method to determine the prognosis of patients with diseases like Covid-19. This method can assist physicians in making treatment decisions that improve survival rates and avoid unnecessary treatments. This research also highlights the importance of calibration, which is often overlooked in model evaluation. Without proper calibration, incorrect decisions can be made in disease treatment and preventive care. Therefore, the current study compares two highly accurate machine learning Algorithms, Gradient Boosting and Extreme gradient Boosting, not only in terms of prediction accuracy but also in terms of model calibration and speed. Methods: This study involved analyzing data from Covid-19 patients who were admitted to two hospitals in Mashhad city, Razavi Khorasan province, over a span of 18 months. The k-fold cross-validation method was employed on the training dataset (K=5) to conduct the study. The accuracy and calibration of two methods (Gradient Boosting and Extreme gradient Boosting) in predicting survival were compared using the Concordance Index and calibration. Results: The Concordance Index values obtained for gradient Boosting and Extreme gradient Boosting models were 0. 734 and 0. 736, in the imbalanced and In the balanced data, the Concordance Index values were 0. 893 for gradient Boosting and 0. 894 for Extreme gradient Boosting. The surv. calib_beta index, the gradient Boosting model had an estimated value of 0. 59 in the imbalanced data and 0. 66 in the balanced data. The Extreme gradient Boosting model had an estimated value of 0. 86 in the balanced data and 0. 853 in the imbalanced data. The Extreme gradient Boosting model was faster in the learning process compared to the gradient Boosting model. Conclusion: The Gradient Boosting and Extreme gradient Boosting models exhibited similar prediction accuracy and discrimination power, but the Extreme gradient Boosting model demonstrated relatively good calibration compare to Gradient Boosting model.

In this paper, a high-bandwidth low-noise amplifier using gm-Boosting technique and noise cancellation method is presented. In this design, the common gate and input auxiliary amplifier are used in the input stage as the gm-Boosting, and the second stage is used to improve the gain and eliminate noise. This amplifier is designed with a 0. 18μ m CMOS technology, simulation results illustrate the voltage gain of 17. 5 ± 1. 5 dB at 2. 2 to 12. 2 GHz bandwidth, s11.

A procedure for detecting outliers in regression problems is proposed. It is based on information provided by Boosting regression trees. The key idea is to select the most frequently resampled observation along the Boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev's inequality applied to the maximum over the Boosting iterations of the average number of appearances in bootstrap samples. So the procedure is noise distribution free. It allows to select outliers as particularly hard to predict observations. A lot of well-known bench data sets are considered and a comparative study against two well-known competitors allows to show the value of the method.

In this paper, a novel LNA design based on improved noise cancellation technique in the frequency range of 27 to 31 GHz.is presented The proposed LNA is suitable for millimeter wave 5G wireless communication. The first stage of this two-stage LNA is designed with noise cancelation approach to decrease the noise figure of the system. In order to improve the design method, we utilizes a negative feedback by implementing a couple inductor with a transformer connection. The negative feedback provides an acceptable input matching and control the gain to increase the band width. The cascode structure is used in the second stage for its higher gain and stability and better reverse isolation at millimeter wave frequency. Furthermore, an inductor is utilized to boost the gain with neutralizing the capacitance of node between two transistors in a cascode structure. The CMOS silicon on insulator (SOI) is utilized to provide a high level of integration and low power consumption with the minimum cost. The proposed LNA is designed with 130 nm CMOS technology and has 22.14 dB gain with 1.86 dB noise figure at 29 GHz. The 3-dB bandwidth of the designed LNA is 4 GHz (14%) and its DC power consumption is 33.4 mW. The IIP3 is -16dBm and input reflection coefficient is better than -10dBm in the frequency range of interest. The proposed LNA is simulated by ADS software.