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Prediction of the logarithmic of partition coefficients (log P) of some organic compounds by least square-support vector machine (LS-SVM)

Nasser Goudarzi, Mohammad Goodarzi
2008 Molecular Physics  
Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and  ...  At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve  ...  GRNN is a nonparametric estimator that calculates a weighted average of the target values of training patterns by the probability density function using Parzen's nonparametric estimator.  ... 
doi:10.1080/00268970802577834 fatcat:vrcqryzjvrenfdcpjlqifnrw7y

Current Mathematical Methods Used in QSAR/QSPR Studies

Peixun Liu, Wei Long
2009 International Journal of Molecular Sciences  
Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and  ...  At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve  ...  GRNN is a nonparametric estimator that calculates a weighted average of the target values of training patterns by the probability density function using Parzen's nonparametric estimator.  ... 
doi:10.3390/ijms10051978 pmid:19564933 pmcid:PMC2695261 fatcat:bzrzjp6ofbdrnmfpbykkzq2wom

Receiver Operator Characteristic Analysis of Biomarkers Evaluation in Diagnostic Research

Karimollah Hajian-Tilaki
2018 Journal of Clinical and Diagnostic Research  
It includes their early use for rating data and the recent developments for quantitative data with a discussion of choice of model selection in parametric ROC analysis compared with non-parametric approach  ...  The recent new development and the gaps in knowledge concerning their behaviours in actual applications for medical researches and a guideline for future research have been discussed.  ...  For example a large number of computer based diagnostic program have been developed to advise a physician on patient diagnosis and to evaluate the extent to which these information systems can improve  ... 
doi:10.7860/jcdr/2018/32856.11609 fatcat:wbxiadbmfve33grfnu64wsbic4

Estimating Radar Precipitation in Cold Climates: The role of Air Temperature within a Nonparametric Framework

Kuganesan Sivasubramaniam, Ashish Sharma, Knut Alfredsen
2018 Hydrology and Earth System Sciences Discussions  
A nonparametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables, and gauge precipitation as an observed response using a k-nearest neighbour (k-nn  ...  In the present study, we investigate the use of air temperature within a nonparametric predictive framework to improve radar precipitation estimation for cold climates.  ...  Much appreciation goes to the Water Research Centre at the University of New South Wales (UNSW) in Sydney, Australia, for hosting the first author for a research practicum.  ... 
doi:10.5194/hess-2018-351 fatcat:y4wedsun6jhntgy3c7acvzczie

Evaluating a Metric to Predict the Academic and Clinical Success of Master's Students in Speech-Language Pathology

Joshua Troche, University of Central Florida, Jacqueline Towson, University of Central Florida
2018 Teaching and Learning in Communication Sciences & Disorders  
At this time over 120 programs use centralized applications systems such as the Communication Sciences and Disorders Centralized Application Service and the number of programs using the system is expected  ...  The Pearson correlation was then used to determine the weighting for each beta-weight across Master's GPA Model 3 and Clinical Checkpoint Model 3.  ... 
doi:10.30707/tlcsd2.2troche fatcat:6etj3zxw6bfc3pqmavevsewflq

Versatile prediction and fast estimation of Architectural Vulnerability Factor from processor performance metrics

Lide Duan, Bin Li, Lu Peng
2009 2009 IEEE 15th International Symposium on High Performance Computer Architecture  
In this paper, we propose to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation across workloads, execution phases and processor configurations  ...  Therefore, the awareness of the AVF especially at early design stage is greatly helpful to achieve a trade-off between system performance and reliability.  ...  Conclusions In this paper, we have proposed to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation (across different workloads, execution phases  ... 
doi:10.1109/hpca.2009.4798244 dblp:conf/hpca/DuanLP09 fatcat:3r6x3oleqbb63al6t43ufvlaxq

Predicting Risk of Death in General Surgery Patients on the Basis of Preoperative Variables Using American College of Surgeons National Surgical Quality Improvement Program Data

Sachin Vaid
2012 The Permanente Journal  
PMP score is an accurate and simple tool for predicting operative survival or death using only preoperative variables that are readily available at the bedside.  ...  , liver, and coagulopathy), steroid use, and weight loss.  ...  We compared the PMP to the NMP using Spearman correlation, a nonparametric test to determine the strength of association between two variables, with scores ranging from -1 (no correlation) through 1 (perfect  ... 
doi:10.7812/tpp/12-019 pmid:23251111 pmcid:PMC3523928 fatcat:lop455wfnjhrzkcm2bwr6two7y

Estimating radar precipitation in cold climates: the role of air temperature within a non-parametric framework

Kuganesan Sivasubramaniam, Ashish Sharma, Knut Alfredsen
2018 Hydrology and Earth System Sciences  
The relative importance of the two predictors is ascertained using an information theory-based weighting.  ...  A non-parametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables and gauge precipitation as an observed response using a <span class="inline-formula  ...  Much appreciation goes to the Water Research Centre at the University of New South Wales (UNSW) in Sydney, Australia, for hosting the first author for a research practicum.  ... 
doi:10.5194/hess-22-6533-2018 fatcat:ytvp5324qzbsvksab5gkanizqu

Forecasting medical waste generation using short and extra short datasets: Case study of Lithuania

Aistė Karpušenkaitė, Tomas Ruzgas, Gintaras Denafas
2016 Waste Management & Research  
Artificial neural networks, multiple linear regression, partial least squares, support vector machines, nonparametric regression and time series methods were used in this research.  ...  It is very doubtful that results would be so precise using data outside of currently used data set range and due to this reason further testing using 2014-2015 data is needed.  ...  A support vector machine (SVM) as an intelligence tool, combined with partial least squares (PLS) as a feature selection tool were used to produce a weekly prediction of MSW generated in Tehran, Iran.  ... 
doi:10.1177/0734242x16628977 pmid:26879908 fatcat:rrhgoitn55bspiw55hlxiip66a

Optimizing dispersal and corridor models using landscape genetics

CLINTON W. EPPS, JOHN D. WEHAUSEN, VERNON C. BLEICH, STEVEN G. TORRES, JUSTIN S. BRASHARES
2007 Journal of Applied Ecology  
a geographical information system (GIS).  ...  Better tools are needed to predict population connectivity in complex landscapes.  ...  We used a fixed kernel density estimator (Beyer 2004) to define the 95% density kernel, and increased the amount of smoothing until a single 95% density polygon was achieved for each of those three populations  ... 
doi:10.1111/j.1365-2664.2007.01325.x fatcat:rn27j6k3lvds3fymtc7tox7vta

Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

Fouzi Harrou, Ahmed Saidi, Ying Sun, Sofiane Khadraoui
2021 IEEE Journal of Photovoltaics  
Data-based procedures for monitoring the operating performance of a PV system are proposed in this paper.  ...  Using data from a 20 MWp grid-connected PV system, the considered faults were successfully traced using the developed procedures.  ...  Then, residuals of MPP current, voltage, and power are verified by the univariate and multivariate exponentially weighted moving average (EWMA) schemes for uncovering faults and partial shading [20] .  ... 
doi:10.1109/jphotov.2021.3057169 fatcat:j5kidczmozcptdoqa4d5kuqvjm

The White Matter Structural Network Underlying Human Tool Use and Tool Understanding

Y. Bi, Z. Han, S. Zhong, Y. Ma, G. Gong, R. Huang, L. Song, Y. Fang, Y. He, A. Caramazza
2015 Journal of Neuroscience  
Behavioral abilities were assessed by a tool use task, a range of conceptual tasks, and control tasks.  ...  The ability to recognize, create, and use complex tools is a milestone in human evolution.  ...  A predicted accuracy score for each patient was acquired by introducing his or her demographic information into the model, and it was used to generate a discrepancy value (Discrepancy patient ) (i.e.,  ... 
doi:10.1523/jneurosci.3709-14.2015 pmid:25926458 fatcat:zzul3jhcprc67e3nbeilyr6efm

Prediction of protein mutant stability using classification and regression tool

Liang-Tsung Huang, K. Saraboji, Shinn-Ying Ho, Shiow-Fen Hwang, M.N. Ponnuswamy, M. Michael Gromiha
2007 Biophysical Chemistry  
The correlation between the experimental and predicted stability change is 0.61 for ΔΔG and 0.44 for ΔΔG H 2 O .  ...  These differences in amino acid properties have been related to protein stability (ΔΔG and ΔΔG H2O ) and are used to train with classification and regression tool for predicting the stability of protein  ...  K.S. acknowledges the Council of Scientific and Industrial Research (CSIR), Govt. of India, for the award of Senior Research Fellowship.  ... 
doi:10.1016/j.bpc.2006.10.009 pmid:17113702 fatcat:pmatvs6nujclzgfr52tupl37m4

Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting

Jelena Fiosina, Maksims Fiosins
2017 Applied Computational Intelligence and Soft Computing  
Forecasting in big datasets is a common but complicated task, which cannot be executed using the well-known parametric linear regression.  ...  We present distributed parallel versions of some nonparametric and semiparametric regression models.  ...  Second, it can predict observations without a reference to a fixed parametric model. Third, it provides a tool for finding spurious observations by studying the influence of isolated points.  ... 
doi:10.1155/2017/5134962 fatcat:luct7fqbybghffwluzoz623xhy

Current Modeling Methods Used in QSAR/QSPR [chapter]

Liew Chin Yee, Yap Chun Wei
2012 Statistical Modelling of Molecular Descriptors in QSAR/QSPR  
Subsequently, for tuning and validation of the QSAR model, the full data set is divided into a training set and a testing set prior to learning.  ...  There are many types of molecular descriptors but not all will be useful for a particular modeling task.  ...  /) SYBYL, as a basic program, provides a wide range of molecular modeling tools which includes tools in structure building, optimization, and comparison (and visualization) of structures and related data  ... 
doi:10.1002/9783527645121.ch1 fatcat:5p74k42kf5hsfaa7ictsgemo5q
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