## What is/are Relationship Modeling?

Relationship Modeling - The relationship modeling between WiFi received signal and the number of people uses polynomial regression.^{[1]}Herein, we extend a molecular representation, or set of descriptors, first developed for quantitative structure-property relationship modeling by Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER).

^{[2]}In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling.

^{[3]}More precise molecular docking (CDOCKER) and 3-Dimensional Quantitative Structure-Activity Relationship Modeling Study(3D-QSAR) pharmacophore generation were implemented to research and explore these compounds' binding mechanism with Dopamine receptor.

^{[4]}With the efficient spatio-temporal relationship modeling, it is possible not only to uncover contextual information in each frame, but to directly capture inter-frame dependencies as well.

^{[5]}The research of multi-category learning behaviors is a hot issue in interactive learning environment, and there are many challenges in data statistics and relationship modeling.

^{[6]}Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels.

^{[7]}This paper presents a weakly supervised object detection method based on activity label and relationship modeling, which is motivated by the assumption that configuration of human and object are similar in same activity, and joint modeling of human, active object and activity could leverage the recognition of them.

^{[8]}DenvInD is the first useful repository of its kind which would augment the DENV drug discovery research by providing essential information on known DENV inhibitors for molecular docking, computational screening, pharmacophore modeling and quantitative structure-activity relationship modeling.

^{[9]}Quantitative structure-property relationship modeling method developed from the quantum four-element concept of electronic attributes is validated by the accurate prediction of the redox potentials, deprotonation constants, stability constants and the maximum biosorption capacity of various metal ions and verification of the toxicological endpoint of soil nematodes and mouse.

^{[10]}The modeling process includes exception sample removal, sample spectral preprocessing, sample set partition and relationship modeling.

^{[11]}Existing works have either focused on designing or learning complex regional feature representations, or delved into various types of AU relationship modeling.

^{[12]}Quantitative structure-property relationship modeling revealed C9-CPs have high octanol-water partition coefficients (log Kow 5.

^{[13]}Finally, we use the predicted parameter map to guide the co-occurrence relationship modeling in different regions of the input low-resolution (LR) face image.

^{[14]}The final evaluation of the established QSAR models and designed inhibitors was carried out using molecular docking studies, bringing to light an excellent correlation with the quantitative structure–activity relationship modeling results.

^{[15]}In the case of a susceptible-infected-susceptible (SIS) host model this algebraic equation is a hyperbolic relationship modeling a saturated incidence rate.

^{[16]}Further, a salient feature of the proposed method is the enhanced expressiveness in transfer learning — as a byproduct of flexible inter-task relationship modelings across different experts.

^{[17]}Seven D–A−π–A-based indoline (IND) dyes that were designed via quantitative-structure–property relationship modeling have been comprehensively investigated using computational approaches to evaluate their prospect of application in future dye-sensitized solar cells (DSSCs).

^{[18]}In the domain of conceptual modeling, which emerged from entity–relationship modeling, models are usually explicit, describing the concepts (e.

^{[19]}Traffic prediction is a complex, nonlinear spatiotemporal relationship modeling task with the randomness of traffic demand, the spatial and temporal dependency between traffic flows, and other recu.

^{[20]}The database design was done using entity relationship modeling (ERM) technique and subsequently developed a data warehouse (DWH) that contained records of the vehicles such as insurance type, servicing type, make and model etc.

^{[21]}The relationship modeling between these latent variables and the student satisfaction was done by using the Partial Least Square-Structural Equation Modeling (PLS-SEM).

^{[22]}The purpose of this study was to unveil the causal relationship modeling of the implicit theories of emotion and emotion regulation in view of cognitive reappraisal strategy and happiness for the students of the Jordanian University of Science and Technology (JUST).

^{[23]}Preliminary quantitative structure-activity relationship modeling indicates that pharmacokinetic predictors capture only one-quarter of all chemical features that are important for antiproliferative activity itself.

^{[24]}Predictive BSEP classification models, constructed through multiple quantitative structure–activity relationship modeling approaches, exhibit significant anomalies with differences in experimental IC50 values of three orders of magnitude.

^{[25]}The goal is to find a quantitative relationship modeling the peak friction angle and maximum dilatancy angle of wheat stored in silos.

^{[26]}The least square regression method is often used in traditional relationship modeling.

^{[27]}In total, 14 compounds were acquired using the F‐NiB methodology, 3D quantitative structure–activity relationship modeling, and pharmacophore modeling.

^{[28]}Six ML algorithms, including the Support Vector Regression (SVR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), Artificial Neural Network (ANN), Bayesian Ridge Regression (BRR) and Kernel Ridge Regression (KRR), are adopted for the relationship modeling to predict crack closure percentage (CCP).

^{[29]}Finally, fingerprint-activity relationship modeling, which was capable of discovering the bioactive markers used in the quality evaluation, was investigated by the chemical fingerprints and the hepatoprotective activities utilizing multivariate statistical analysis, gray correlation analysis (GCA) and bivariate correlation analysis (BCA).

^{[30]}Our findings successfully combine the molecular solvation theory with the machine learning approach to address one of the most daunting challenges in predictive structure–activity relationship modeling.

^{[31]}

## Activity Relationship Modeling

More precise molecular docking (CDOCKER) and 3-Dimensional Quantitative Structure-Activity Relationship Modeling Study(3D-QSAR) pharmacophore generation were implemented to research and explore these compounds' binding mechanism with Dopamine receptor.^{[1]}DenvInD is the first useful repository of its kind which would augment the DENV drug discovery research by providing essential information on known DENV inhibitors for molecular docking, computational screening, pharmacophore modeling and quantitative structure-activity relationship modeling.

^{[2]}The final evaluation of the established QSAR models and designed inhibitors was carried out using molecular docking studies, bringing to light an excellent correlation with the quantitative structure–activity relationship modeling results.

^{[3]}Preliminary quantitative structure-activity relationship modeling indicates that pharmacokinetic predictors capture only one-quarter of all chemical features that are important for antiproliferative activity itself.

^{[4]}Predictive BSEP classification models, constructed through multiple quantitative structure–activity relationship modeling approaches, exhibit significant anomalies with differences in experimental IC50 values of three orders of magnitude.

^{[5]}In total, 14 compounds were acquired using the F‐NiB methodology, 3D quantitative structure–activity relationship modeling, and pharmacophore modeling.

^{[6]}Finally, fingerprint-activity relationship modeling, which was capable of discovering the bioactive markers used in the quality evaluation, was investigated by the chemical fingerprints and the hepatoprotective activities utilizing multivariate statistical analysis, gray correlation analysis (GCA) and bivariate correlation analysis (BCA).

^{[7]}Our findings successfully combine the molecular solvation theory with the machine learning approach to address one of the most daunting challenges in predictive structure–activity relationship modeling.

^{[8]}

## Property Relationship Modeling

Herein, we extend a molecular representation, or set of descriptors, first developed for quantitative structure-property relationship modeling by Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER).^{[1]}Quantitative structure-property relationship modeling method developed from the quantum four-element concept of electronic attributes is validated by the accurate prediction of the redox potentials, deprotonation constants, stability constants and the maximum biosorption capacity of various metal ions and verification of the toxicological endpoint of soil nematodes and mouse.

^{[2]}Quantitative structure-property relationship modeling revealed C9-CPs have high octanol-water partition coefficients (log Kow 5.

^{[3]}Seven D–A−π–A-based indoline (IND) dyes that were designed via quantitative-structure–property relationship modeling have been comprehensively investigated using computational approaches to evaluate their prospect of application in future dye-sensitized solar cells (DSSCs).

^{[4]}

## relationship modeling method

Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels.^{[1]}Quantitative structure-property relationship modeling method developed from the quantum four-element concept of electronic attributes is validated by the accurate prediction of the redox potentials, deprotonation constants, stability constants and the maximum biosorption capacity of various metal ions and verification of the toxicological endpoint of soil nematodes and mouse.

^{[2]}