regression

摘要： In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.

摘要： In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based on the experimental data.The algorithm is performed by choosing k nearest points and training dataset to find the optimal k value.The proposed method is applied to impove and adjust pathloss model at 28 GHz in Keangnam area,Hanoi,Vietnam.The experiments in both line-of-sight and non-line-of-sight scenarios used many combinations of transmit and receive antennas at different transmit antenna heights and random locations of receive antenna have been carried out using Wireless Insite Software.The results have been compared with 3GPP and NYU Wireless Path Loss Models in order to verify the performance of the proposed approach.

摘要： Nowadays,we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single task.In this work,we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking,essay grading,and question answering systems.We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset.The used schemes include lexical-based similarity features,frequency-based features,and pre-trained model-based features.Also,we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers(AraBERT).We used the AraBERT model in two different variants.First,as a feature extractor in addition to the text vectorization schemes’features.We fed those features to various regression models to make a prediction value that represents the relevancy score between Arabic text units.Second,AraBERT is adopted as a pre-trained model,and its parameters are fine-tuned to estimate the relevancy scores between Arabic textual sentences.To evaluate the research results,we conducted several experiments to compare the use of the AraBERT model in its two variants.In terms of Mean Absolute Percentage Error(MAPE),the results showminor variance between AraBERT v0.2 as a feature extractor(21.7723)and the fine-tuned AraBERT v2(21.8211).On the other hand,AraBERT v0.2-Large as a feature extractor outperforms the finetuned AraBERT v2 model on the used data set in terms of the coefficient of determination(R2)values(0.014050,−0.032861),respectively.

摘要： This paper presents the predictive models for biped robot trajectory generation.Predictive models are parametrizing as a continuous function of joint angle trajectories.In a previous work,the authors had collected the human locomotion dataset at RAMAN Lab,MNIT,Jaipur,India.The MNIT gait dataset consists of walking data on a plane surface of 120 human subjects from different age groups and genders.Thirty-two machine learning models(linear,support vector,k-nearest neighbor,ensemble,probabilistic,and deep learning)trained using the collected dataset.In addition,two types of mapping,(a)one-to-one and(b)many-to-one,are presented for each model.These mapping models act as a reference policy for the control of joints and prediction of state for the next time instant in advance if the onboard sensor fails.Results show that the deep learning and probabilistic learning models perform better for both types of mappings.Also,the probabilistic model outperforms the deep learning-based models in terms of maximum error,because the probabilistic model effectively deals with the prediction uncertainty.In addition,many-to-one outperforms the one-to-one mapping because it captures the correlation between knee,hip,and ankle trajectories.Therefore,this study suggests a many-to-one mapping using the probabilistic model for biped robot trajectory generation.

摘要： BACKGROUND Numerous anterior cruciate ligament(ACL) clinical outcome measures exist.However,the result of one score does not equate to the findings of another even when evaluating the same patient group.AIM To investigate if statistically derived formulae can be used to predict the outcome of one knee scoring system when the result of another is known in patients with ACL rupture before and after reconstruction.METHODS Fifty patients with ACL rupture were evaluated using nine clinical outcome measures.These included Tegner Activity Score,Lysholm Knee Score,Cincinnati Knee Score,International Knee Documentation Committee(IKDC) Objective Knee Score,Tapper and Hoover Meniscal Grading Score,IKDC Subjective Knee Score,Knee Outcome Survey-Activities of Daily Living Scale(KOS-ADLS),Short Form-12 Item Health Survey and Knee Injury and Osteoarthritis Outcome Score.Thirtyfour patients underwent an ACL reconstruction and were reassessed post-operatively.RESULTS The mean total of each of the nine outcome scores appreciably differed from each other.Significant correlations and regressions were found between most of the outcome scores and were stronger post-operatively.The strongest correlation was found between Cincinnati and KOS-ADLS (r=0.91,P<0.001).The strongest regression formula was also found between Cincinnati and KOS-ADLS (R~2=0.84,P<0.001).CONCLUSION The formulae produced from this study can be used to predict the outcome of one knee score when the results of the other are known.These formulae could facilitate the conduct of systematic reviews and meta-analysis in studies relating to ACL injuries by allowing the pooling of substantially more data.

摘要： A substantial amount of the Indian economy depends solely on agriculture.Rainfall,on the other hand,plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing,if the amount is inordinate or scant,it can ruin the entire hard work of the farmers.In this work,the rainfall dataset of the Vellore region,of Tamil Nadu,India,in the years 2021 and 2022 is forecasted using several machine learning algorithms.Feature engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the data.On removal of autocorrelation,the data could be used for performing operations on the time-series data,which otherwise could only be performed on any other regular regression data.The work uses forecasting techniques like the AutoRegessive Integrated Moving Average(ARIMA)and exponential smoothening,and then the time-series data is further worked on using Long Short Term Memory(LSTM).Later,regression techniques are used by manipulating the dataset.The work is benchmarked with several evaluation metrics on a test dataset,where XGBoost Regression technique outperformed the test.The uniqueness of this work is that it forecasts the daily rainfall for the year 2021 and 2022 in Vellore region.This work can be extended in the future to predict rainfall over a bigger region based on previously recorded time-series data,which can help the farmers and common people to plan accordingly and take precautionary measures.

摘要： The Visual tracking problem can usually be solved in two parts.The first part is to extract the feature of the target and get the candidate region.The second part is to realize the classification of the target and the regression of the bounding box.In recent years,Siameses network in visual tracking problem has always been a frontier research hotspot.In this work,it applies two branches namely search area and tracking template area for similar learning to track.Some related researches prove the feasibility of this network structure.According to the characteristics of two branch shared networks in Siamese network,we also propos a new fully convolutional Siamese network to solve the visual tracking problem.Based on the Siamese network structure,the network we designed adopt a new fusion module,which realizes the fusion of multiple feature layers at different depths.We also devise a better target state estimation criterion.The overall structure is simple,efficient and has wide applicability.We extensive experiments on challenging benchmarks including generic object tracking-10k(GOT-10K),online object tracking benckmark2015(OTB2015)and unmanned air vehicle123(UAV123),and comparisons with state-of-the-art trackers and the fusion module commonly used in the past,Finally,our network performed better under the same backbone,and achieved good tracking effect,which proved the effectiveness and universality of our designed network and feature fusion method.

摘要： Data Mining, also known as knowledge discovery in data (KDC), is the process of uncovering patterns and other valuable information from large data sets. According to https://www.geeksforgeeks.org/data-mining/, it can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. With advance research in health sector, there is multitude of Data available in healthcare sector. The general problem then becomes how to use the existing information in a more useful targeted way. Data Mining therefore is the best available technique. The objective of this paper is to review and analyse some of the different Data Mining Techniques such as Application, Classification, Clustering, Regression, etc. applied in the Domain of Healthcare.

摘要： BACKGROUND The established cardiovascular risk factors cannot explain the overall risk of coronary artery disease(CAD),especially in women.Therefore,there is a growing need for the assessment of novel biomarkers to identify women at risk.The receptor for advanced glycation end products(RAGE)and its interaction with the advanced glycation end product(AGE)ligand have been associated with atherogenesis.The soluble fraction of RAGE(sRAGE)antagonizes RAGE signaling and exerts an antiatherogenic effect.AIM The study aim was to explore the association between plasma levels of sRAGE and CAD in nondiabetic postmenopausal women.METHODS This case-control study included 110 nondiabetic postmenopausal women who were enrolled in two groups.Group I included 55 angiographically proven CAD subjects with>50%stenosis in at least one of the major coronary arteries and Group II included 55 healthy control women who did not have CAD or had<50%stenosis of the coronary arteries.Stenosis was confirmed by invasive angiography.Plasma sRAGE was determined by an enzyme-linked immunosorbent assay.RESULTS We observed significantly lower plasma sRAGE concentrations in subjects with CAD vs healthy controls(P<0.05).Univariate and multivariate logistic regression analysis also revealed a significant correlation between plasma sRAGE levels and CAD(P=0.01).Multivariate odds ratios for CAD revealed that subjects with sRAGE concentrations below 225 pg/mL(lowest quartile)had a 6-fold increase in CAD prevalence independent of other risk factors.CONCLUSION Our findings indicated that low sRAGE levels were independently associated with CAD in nondiabetic postmenopausal women.Risk assessment of CAD in postmenopausal women can be improved by including sRAGE along with other risk factors.

摘要： This work,examines the Surface Roughness(SR)of composite consisting Aluminium alloy(AA6061),Magnesium and Rock dust during turning process.To study the performance,three different test specimens with different constituents of Al 6061-T6,AZ31 and Rock dust were prepared by stir casting method.Turning experiments were carried out using MTAB Siemens-CNC lathe.The input parameters for machining are speed,depth of cut&feed and output response is surface roughness For each test specimen,there are 15 turning operations were performed using Box-Ben hen Design approach.To analyze the process parameters for SR,the models of ANOVA and Decision Tree(DT)algorithms were performed.Both algorithms are confirmed that,speed is the most significant factor for SR.The addition of AZ 31 with 1%and rock dust of 2%in AA6061 produced better surface finish.Regression models of linear regression,multilayer perception and support vector regression from data science were formulated to find the relationship between variables.Among these models multi layer perception produced minimum root mean square error.

摘要： LEA 电子枪是我公司自行开发的一个新产品，在彩管中它被称为显像管的心脏。而LEA 电子枪的G1/K、G1/G2间隙设置对显像管的电特性，尤其是EKCO(截止电压)、8”EWT(8”发射)及40”EWT(40”发射)等电特性有直接影响。因此，确定LEA 电子枪的工艺最佳G1/K、G1/G2问隙设置，对确定显像管的最佳电特性值意义重大。文章阐述了LEA 电子枪工艺设计阶段MSA、Regression、ANOVA、SPC等质量工具的运用，并借助MINITAB软件的帮助，成功地实践了六西格玛DMAIC过程，找到了LEA 电子枪G1/K、G1/G2间隙的最佳工艺设置，为新产品的开发成功及顺利量产奠定了基础。

摘要： LEA 电子枪是我公司自行开发的一个新产品，在彩管中它被称为显像管的心脏。而LEA 电子枪的G1/K、G1/G2间隙设置对显像管的电特性，尤其是EKCO(截止电压)、8”EWT(8”发射)及40”EWT(40”发射)等电特性有直接影响。因此，确定LEA 电子枪的工艺最佳G1/K、G1/G2问隙设置，对确定显像管的最佳电特性值意义重大。文章阐述了LEA 电子枪工艺设计阶段MSA、Regression、ANOVA、SPC等质量工具的运用，并借助MINITAB软件的帮助，成功地实践了六西格玛DMAIC过程，找到了LEA 电子枪G1/K、G1/G2间隙的最佳工艺设置，为新产品的开发成功及顺利量产奠定了基础。

摘要： LEA 电子枪是我公司自行开发的一个新产品，在彩管中它被称为显像管的心脏。而LEA 电子枪的G1/K、G1/G2间隙设置对显像管的电特性，尤其是EKCO(截止电压)、8”EWT(8”发射)及40”EWT(40”发射)等电特性有直接影响。因此，确定LEA 电子枪的工艺最佳G1/K、G1/G2问隙设置，对确定显像管的最佳电特性值意义重大。文章阐述了LEA 电子枪工艺设计阶段MSA、Regression、ANOVA、SPC等质量工具的运用，并借助MINITAB软件的帮助，成功地实践了六西格玛DMAIC过程，找到了LEA 电子枪G1/K、G1/G2间隙的最佳工艺设置，为新产品的开发成功及顺利量产奠定了基础。

摘要： LEA 电子枪是我公司自行开发的一个新产品，在彩管中它被称为显像管的心脏。而LEA 电子枪的G1/K、G1/G2间隙设置对显像管的电特性，尤其是EKCO(截止电压)、8”EWT(8”发射)及40”EWT(40”发射)等电特性有直接影响。因此，确定LEA 电子枪的工艺最佳G1/K、G1/G2问隙设置，对确定显像管的最佳电特性值意义重大。文章阐述了LEA 电子枪工艺设计阶段MSA、Regression、ANOVA、SPC等质量工具的运用，并借助MINITAB软件的帮助，成功地实践了六西格玛DMAIC过程，找到了LEA 电子枪G1/K、G1/G2间隙的最佳工艺设置，为新产品的开发成功及顺利量产奠定了基础。

摘要： LEA 电子枪是我公司自行开发的一个新产品，在彩管中它被称为显像管的心脏。而LEA 电子枪的G1/K、G1/G2间隙设置对显像管的电特性，尤其是EKCO(截止电压)、8”EWT(8”发射)及40”EWT(40”发射)等电特性有直接影响。因此，确定LEA 电子枪的工艺最佳G1/K、G1/G2问隙设置，对确定显像管的最佳电特性值意义重大。文章阐述了LEA 电子枪工艺设计阶段MSA、Regression、ANOVA、SPC等质量工具的运用，并借助MINITAB软件的帮助，成功地实践了六西格玛DMAIC过程，找到了LEA 电子枪G1/K、G1/G2间隙的最佳工艺设置，为新产品的开发成功及顺利量产奠定了基础。

摘要： LEA 电子枪是我公司自行开发的一个新产品，在彩管中它被称为显像管的心脏。而LEA 电子枪的G1/K、G1/G2间隙设置对显像管的电特性，尤其是EKCO(截止电压)、8”EWT(8”发射)及40”EWT(40”发射)等电特性有直接影响。因此，确定LEA 电子枪的工艺最佳G1/K、G1/G2问隙设置，对确定显像管的最佳电特性值意义重大。文章阐述了LEA 电子枪工艺设计阶段MSA、Regression、ANOVA、SPC等质量工具的运用，并借助MINITAB软件的帮助，成功地实践了六西格玛DMAIC过程，找到了LEA 电子枪G1/K、G1/G2间隙的最佳工艺设置，为新产品的开发成功及顺利量产奠定了基础。

摘要： LEA 电子枪是我公司自行开发的一个新产品，在彩管中它被称为显像管的心脏。而LEA 电子枪的G1/K、G1/G2间隙设置对显像管的电特性，尤其是EKCO(截止电压)、8”EWT(8”发射)及40”EWT(40”发射)等电特性有直接影响。因此，确定LEA 电子枪的工艺最佳G1/K、G1/G2问隙设置，对确定显像管的最佳电特性值意义重大。文章阐述了LEA 电子枪工艺设计阶段MSA、Regression、ANOVA、SPC等质量工具的运用，并借助MINITAB软件的帮助，成功地实践了六西格玛DMAIC过程，找到了LEA 电子枪G1/K、G1/G2间隙的最佳工艺设置，为新产品的开发成功及顺利量产奠定了基础。

摘要： LEA 电子枪是我公司自行开发的一个新产品，在彩管中它被称为显像管的心脏。而LEA 电子枪的G1/K、G1/G2间隙设置对显像管的电特性，尤其是EKCO(截止电压)、8”EWT(8”发射)及40”EWT(40”发射)等电特性有直接影响。因此，确定LEA 电子枪的工艺最佳G1/K、G1/G2问隙设置，对确定显像管的最佳电特性值意义重大。文章阐述了LEA 电子枪工艺设计阶段MSA、Regression、ANOVA、SPC等质量工具的运用，并借助MINITAB软件的帮助，成功地实践了六西格玛DMAIC过程，找到了LEA 电子枪G1/K、G1/G2间隙的最佳工艺设置，为新产品的开发成功及顺利量产奠定了基础。

摘要： 本发明公开了一种基于LD和ANN‑SoftMax Regressor的中医药短文本多分类方法，主要是基于已经构建好的人工实体标注的文本标签数据，对中医药短文本数据进行数据处理，首先采用jieba分词工具进行分词然后采用词袋(Bag‑of‑words)模型进行处理，再通过LD的方式建立病症同义词词典，对构建词袋后的文本onehot向量化表示利用SVD和PCA降维处理后进行ANN‑SoftMax Regressor多分类训练，最终得到中医药短文本多分类的方法。本发明方法适用于中医药和短文本分类领域可有效的使疾病的模糊症状分类更加准确。

摘要： 应用general regression结构无损预测机采眉茶车色样品等级的方法，包括：样品收集与分类；扫描获得不同等级眉茶车色样品的近红外光谱；对样品光谱进行预处理剔除噪声信息后，将样品光谱转化为成对的数据点；再将全部光谱数据均划分为20个子区间，分别建立每个子区间数据的粒子群优化算法模型，筛选出建模的最佳子区间数据；应用主成分分析法抽提、压缩最佳光谱子区间信息；以主成分得分为输入值，不断调整神经元个数和传递函数，建立general regression结构人工神经网络预测模型；模型稳健性检验。实现了湖北眉茶车色样品等级的快速、准确、客观预测。