## What is/are Real Life Data?

Real Life Data - Our simulations on a real-life dataset shows that pelte can accurately predict privacy settings even when a user has shared a few images with others, the images have only a few tags or the user’s friends have varying privacy preferences.^{[1]}To compare the clinical and laboratory findings of multisystem inflammatory syndrome in children (MIS-C), patients with Kawasaki disease (KD) and with macrophage activating syndrome due to systemic juvenile idiopathic arthritis (sJIA-MAS) on real-life data.

^{[2]}However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation.

^{[3]}Extensive performance analyses of all the algorithms are observed over several real-life datasets and impressive results are found compared to the existing research.

^{[4]}However, little real-life data have been reported on its use in the elderly population.

^{[5]}An experiment is conducted on a real-life dataset Foursquare, of which the result shows it improves top-1 accuracy by 12.

^{[6]}Using real-life datasets and GNN models GCN, PinSage and MAGNN, we verify that NAU makes FlexGraph more expressive than prior frameworks (e.

^{[7]}To explore predictors of in-hospital mortality (IHM), post discharge early mortality [1-month mortality (1mM)] and late mortality [1-year mortality (1yM)] and early and late readmission, respectively 1-month readmission (1mRA) and 1-year readmission (1yRA), in our center population, using real-life data.

^{[8]}Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID).

^{[9]}This research uses real-life data of an international fashion retailer.

^{[10]}One real-life data set is analyzed to illustrate the evaluation of proposed methods of estimation.

^{[11]}tn (by using real-life datasets from anti-leprosy vaccine trial conducted in south India).

^{[12]}The model is verified through the case study with the real-life data originating from a significant number of organizations from one region.

^{[13]}Besides, comparative studies with previous publications and real-life data analysis are carried out to illustrate the robustness and practicability of the models and strategies.

^{[14]}BACKGROUND There are limited real-life data on isavuconazole prophylaxis and treatment of invasive mold infections (IMI) in hematological patients and allogeneic hematopoietic cell transplant (HCT) recipients.

^{[15]}Since then few real-life data are available.

^{[16]}A simulation study based on selected hypothetical distributions and a real-life data set showed that IPRSS is more precise than RSS, Paired RSS (PRSS) or Extreme RSS (ERSS).

^{[17]}We allow a mixed uncertain environment by considering uncertain-random parameters in the proposed model to express ambiguity in real-life data.

^{[18]}OBJECTIVES To report real-life data on the use of DTG-based dual therapies in treatment-experienced patients.

^{[19]}Context: Real-life data consist of exhaustive and unbiased data to study drug-safety profiles but are underused because of their complex temporality (i.

^{[20]}These classification algorithms are not always efficient when it comes to real-life datasets due to class distributions.

^{[21]}But the real-life data does not follow such a rule properly in various domains.

^{[22]}Objectives: To depict contemporary real-life data regarding the work-related burden of disease among Greek patients with RD.

^{[23]}We use the nascent decentralized insurance market as a laboratory to apply our model based on real-life data and show that relatively small stakes are sufficient to maximize network throughput, which is equivalent to welfare.

^{[24]}Extensive simulations over both synthetic and real-life datasets are conducted to confirm the efficiency of all proposed schemes.

^{[25]}Initial real-life data suggest variable degree of adherence with higher adherence resulting in reduced adverse outcomes.

^{[26]}Actigraphy has the ability to collect real-life data without interfering with clinical practice and give clinicians a new measure of performance that is currently not available.

^{[27]}The applications of the new WIR distribution were demonstrated on three real-life data sets.

^{[28]}The real-life data application is also a part of this study.

^{[29]}The usefulness of the new model is illustrated by means of three real-life data sets.

^{[30]}We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images).

^{[31]}Our proposed framework was trained and evaluated using a real-life dataset of users interacting spontaneously with a social robot in a dynamic environment.

^{[32]}However, using real-life training data alone or in combination with semi-structured data generated better results for older adults who had high real-life data quality.

^{[33]}In our real-life data study, the median onset time of grade III-IV irAES was 128 days after the initiation of immune checkpoint inhibitors (ICI) therapy.

^{[34]}Finally, we experimentally evaluate and compare the scalability and accuracy of our approaches on several real-life datasets.

^{[35]}Experiments on real-life datasets of 4 cities demonstrate that PSRNet has significant advantages over 8 baselines by reducing RMSE and MAE for more than 25%.

^{[36]}The numerical results based on real-life data sets show that the proposed method can achieve high predicted accuracy of BP while saving training time.

^{[37]}We verify the effectiveness of PC-TD on real-life datasets.

^{[38]}The aims of this study were to provide real-life data about the effect of COVID-19 pandemic on the practice of anti-VEGF injections and to evaluate the safety of the modifications in the injection protocol imposed during the ongoing pandemic on the anatomical and functional outcome of patients.

^{[39]}Two real-life datasets were used to evaluate the model’s performance compared to the previous related works.

^{[40]}Extensive experiments based on real-life datasets demonstrate that the proposed method achieves higher accuracy than several typical baseline methods.

^{[41]}The study aimed to compare the prolonged cytopenias depending on fitness and report real-life data on dose reduction measures and efficacy.

^{[42]}Experimental results on real-life datasets show that SMoTAS not only achieves substantial improvement of accuracy over existing methods in discovering significant places, but also exhibits superior adaptability to different trajectories and application scenarios.

^{[43]}The purpose of our study was to present real-life data of chronic hepatitis C (CHC) infected patients with genotypes 2 and 3 who were treated with glecaprevir/pibrentasvir regimen.

^{[44]}Findings This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture.

^{[45]}Experimental results on three real-life datasets show that our approach achieves better performance and outperforms previous non-pretrained methods on the ZS-MTC task.

^{[46]}Real-life data and head-to-head clinical trials will be needed to confirm its efficacy and safety.

^{[47]}However, real-life data and long-term survival data are lacking.

^{[48]}Vaccination has been proven to be the crucial pillar for preventing asymptomatic infections and real-life data will continue to exhibit the effects of community vaccination in breaking the transmission chain of SARS-CoV-2 infections.

^{[49]}However, these examples are not based on real-life data since the lack of parameter estimation method for fuzzy differential equation driven by Liu process.

^{[50]}

## extensive experimental evaluation

Our extensive experimental evaluation using both synthetic and real-life data sets and workloads shows that (a) the Adaptive KD-Tree reduces total workload time by up to a factor 2 compared to the state-of-the-art, (b) the Progressive KD-Tree achieves predictable convergence with up to one order of magnitude lower initial query cost, and (c) the Greedy Progressive KDTree exhibits the lowest query variance up to three orders of magnitude lower than the state-of-the-art.^{[1]}In an extensive experimental evaluation using two real-life datasets, we show that our novel techniques are indeed capable of producing clustering solutions that are more justifiable without a substantial negative impact on their quality.

^{[2]}Extensive experimental evaluations on three real-life datasets show that our framework E2DTC achieves superior accuracy and efficiency, compared with classical clustering methods (i.

^{[3]}An extensive experimental evaluation was conducted on real-life datasets to compare the clustering quality of k-CMM with state-of-the-art clustering algorithms.

^{[4]}

## post discharge early

To explore predictors of in-hospital mortality (IHM), post discharge early mortality [1-month mortality (1mM)] and late mortality [1-year mortality (1yM)] and early and late readmission, respectively 1-month readmission (1mRA) and 1-year readmission (1yRA), in our center population, using real-life data.^{[1]}To validate ACTION-ICU score in AHF as predictor of in-hospital M (IHM), post discharge early M [1-month mortality (1mM)] and 1-month readmission (1mRA), in our center population, using real-life data.

^{[2]}To validate GRACE score in AHF and to compare GRACE and GWTG-HF scores as predictors of IHM, post discharge early and late M [1-month mortality (1mM) and 1-year M (1yM)], 1-month readmission (1mRA) and 1-year readmission (1yRA), in our center population, using real-life data.

^{[3]}

## 1 month mortality

To validate ACTION-ICU score in AHF and to compare ACTION-ICU to GWTG-HF as predictors of in-hospital M (IHM), early M [1-month mortality (1mM)] and 1-month readmission (1mRA), using real-life data.^{[1]}

## soft β rough

Soft rough set theory has been presented as a basic mathematical model for decision-making for many real-life data However, soft rough sets are based on a possible fusion of rough sets and soft sets which were proposed by Feng et al [20] The main contribution of the present article is to introduce a modification and a generalization for Feng's approximations, namely, soft β -rough approximations, and some of their properties will be studied A comparison between the suggested approximations and the previous one [20] will be discussed Some examples are prepared to display the validness of these proposals Finally, we put an actual example of the infections of coronavirus (COVID-19) based on soft β -rough sets This application aims to know the persons most likely to be infected with COVID-19 via soft β -rough approximations and soft β -rough topologies [ABSTRACT FROM AUTHOR] Copyright of Turkish Journal of Mathematics is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all s ).^{[1]}

## Two Real Life Data

Our claims are validated by experiments on two real life data sets, the public domain Epinions.^{[1]}In practice, experiments conducted over two real life datasets also could demonstrate the significant superiority of IRAI, which could provide high performance in real life applications.

^{[2]}Two real life data sets were analyzed for the case when all the three parameters are unknown.

^{[3]}Two real life data sets are analyzed for illustration.

^{[4]}Effectiveness of the proposed PSDK-Means algorithm has been validated on four synthetic and two real life datasets where it is observed to outperform conventional DK-Means algorithm.

^{[5]}A Monte Carlo simulation study and two real life data sets are presented to illustrate and compare the proposed prediction sets.

^{[6]}

## Three Real Life Data

Three real life data sets are then analyzed.^{[1]}These models are fitted to three real life datasets using Bayesian estimation methods and compared using the Bayesian Information Criteria (AIC, BIC, and DIC) and the Bayes Factor.

^{[2]}The potentials of the distributions are explored through three real life data sets and are compared with similar compounded distributions, viz.

^{[3]}

## Some Real Life Data

Some real life data has been used to get approximate PH representations for channel idle and busy period variates, which in turn are used for numerical illustrations.^{[1]}Some real life data like human gene have an inherent structure of hierarchy, which embeds multi-layer feature groups.

^{[2]}Some real life data sets have been analyzed for illustrative purposes.

^{[3]}

## Certain Real Life Data

We discuss the estimation of parameters of the location-scale extended class of DLD and illustrate the procedures with the help of certain real life data sets.^{[1]}Our method is simple in comparison to known methods, and gives good enough estimates to make it useful in certain real life datasets that arise in data mining scenarios.

^{[2]}

## Collecting Real Life Data

Since the dataset requires videos recorded using thermal camera of gas leakage, collecting real life data has its own barriers (safety reason, availability, etc.^{[1]}Since the dataset requires videos recorded using thermal camera of gas leakage, collecting real life data has its own barriers (safety reason, availability, etc.

^{[2]}

## real life data set

In order to illustrate the usefulness of the SinIW model, an application to real life data set is carried out.^{[1]}Our claims are validated by experiments on two real life data sets, the public domain Epinions.

^{[2]}The proposed method is successfully tested on one simulated data set and two publicly available real life data sets.

^{[3]}Three real life data sets are then analyzed.

^{[4]}We discuss the estimation of parameters of the location-scale extended class of DLD and illustrate the procedures with the help of certain real life data sets.

^{[5]}The proposed estimation procedure is applied to a real life data set.

^{[6]}A real life data set and a Monte Carlo simulation are used to study the performance of the estimators derived in the article.

^{[7]}Experimental evaluation using variety of random and real life data sets over shared memory multi-core systems and graphic processing units (GPUs) show that NvPD outperforms state-of-the-art parallel edit distance algorithms.

^{[8]}Finally, applications to real life data sets are presented to illustrate our results.

^{[9]}IMPLICATIONS FOR PRACTICE This study aimed to define activity and safety of stereotactic body radiotherapy (SBRT) in a very large, real life data set of patients with metastatic, persistent, recurrent ovarian cancer (MPR-OC).

^{[10]}Two real life data sets were analyzed for the case when all the three parameters are unknown.

^{[11]}The practical application of the class of models is illustrated with a real life data set.

^{[12]}The Proposed hybrid algorithm is simulated on seven real life data sets.

^{[13]}The construction as a mixed Poisson process by specifying a joint distribution for the inter-arrival times and its application is illustrated by a fit to real life data set.

^{[14]}Experiments conducted on a real life data set show that the proposed method achieves 83.

^{[15]}Suitability of the suggested family of distributions is established by using real life data sets from different areas of application.

^{[16]}The benchmark is validated in a prototype implementation which is applied to a real life data set.

^{[17]}Two real life data sets are analyzed for illustration.

^{[18]}Real life data sets are however dynamic by nature.

^{[19]}Four-real life data sets are considered for illustrating the advantages of the proposed distribution over some other well-known discrete distributions.

^{[20]}This method has been tested on a variety of simulated experiments and real life data sets and the results are compared with the traditional Relief method and some of the well known recent distance based feature selection methods.

^{[21]}Some real life data sets have been analyzed for illustrative purposes.

^{[22]}A real life data set as well as simulated data have been analyzed for illustrative purposes.

^{[23]}Firstly, the performance of basic HPSOM is evaluated on six real life data sets and is also compared with some known evolutionary clustering techniques in terms of SWCD and Convergence speed.

^{[24]}A Monte Carlo simulation study and two real life data sets are presented to illustrate and compare the proposed prediction sets.

^{[25]}The parameters of the distribution are estimated by the method of maximum likelihood and illustrated using real life data sets.

^{[26]}The proposed approach has been applied on a real life data set to verify its viability.

^{[27]}The potentials of the distributions are explored through three real life data sets and are compared with similar compounded distributions, viz.

^{[28]}

## real life data example

To assess the suitability and application of the proposed model, a real life data example is also discussed in this article.^{[1]}A real life data example is also studied to illustrate the usefulness of the ASP.

^{[2]}With a simulation study and a real life data example, we demonstrate that compared to state-of-the-art matrix factorization, BMCD makes personalized recommendations with similar accuracy, while achieving much higher level of diversity, and producing interpretable and actionable uncertainty estimation.

^{[3]}