Building Endgame Data set to Improve Opponent Modeling Approach

2017 ◽  
Author(s):  
Zhang Jiajia ◽  
Liu Hong
Keyword(s):  
Data Set ◽  
Modeling Approach ◽  
Opponent Modeling

Risk prediction-modeling approach to identify best predictive parameters for urethral strictures after prostate brachytherapy.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 87-87
Author(s):  
Sandeep Singhal ◽  
Matthew Parliament ◽  
Muhammad Faisal Jamaluddin ◽  
Emma Lee ◽  
Ron Sloboda ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Rare Complication ◽  
Stricture Formation ◽  
Radiation Toxicity ◽  
Prostate Brachytherapy ◽  
Retrospective Case ◽  
Data Set ◽  
Prediction Ability ◽  
Modeling Approach ◽  
Urethral Strictures

87 Background: Urethral strictures (US) are a rare complication of prostate brachytherapy (BXT), with prior studies showing radiation dose to the bulbomembranous urethra is associated with stricture formation. This retrospective case-control study explored clinical and dosimetric parameters associated with the development of BXT-related urethral strictures. Methods: A cohort of 34 patients developed urethral strictures after BXT at our institution for the period of 2008-2014. Each case was matched with two controls (68 controls) that had not developed a US according to similar baseline International Prostate Symptom Score (IPSS), planned prostate volume, post-implant prostate V150, and post-implant prostate D90 dosimetry parameters. US development was compared with clinical (i.e. age, IPSS, etc) and dosimetric (i.e. prostate, urethra, urethra segments) variables. Statistical modeling for risk prediction was applied, which included adjusted R2, Mallows’ C Selection (Cp), Schwartz’s information criterion (BIC), forward selection (FS), and backward selection (BS) to identify the parameters with prediction ability of toxicity. CV analysis was performed to select the best subset selection on the full data set in order to obtain the most predictive parameter selection. Results: The results show that the R2statistic increases from 6% (only one) to 33 %, (all of the parameters included). The table demonstrates minimum best-fit parameters in the different models. (See table.) CV with minimum standard error (MSE) identified a model with 5 parameters that included age, baseline IPSS, UD30, UD5, and U5mm V200Apex shows best prediction ability for US. Conclusions: This modeling approach, which is novel in BXT, helped to identify a combination of parameters with some predictive ability of radiation toxicity. Further evaluation is required to validation. [Table: see text]

Modeling seed-origin oak regeneration in the central Appalachians

2006 ◽  
Vol 36 (4) ◽  
pp. 833-844 ◽  
Author(s):  
P J Gould ◽  
K C Steiner ◽  
M E McDill ◽  
J C Finley
Keyword(s):  
Inverse Modeling ◽  
Oak Regeneration ◽  
Top Down ◽  
Data Set ◽  
The Third ◽  
Advanced Regeneration ◽  
Modeling Approach ◽  
Seed Origin ◽  
Level Model ◽  
Term Data

We describe the development of a model to quantify seed-origin oak regeneration potential in advance of complete overstory removal in central Appalachian oak stands. The model was developed using a "top-down" modeling approach that differs significantly from the approaches used to develop similar models for other regions. The modeling approach was designed to take advantage of the best data available for the region. A stand-level model was first fit using a long-term data set from Pennsylvania that was developed, in part, from operational data collected through the course of timber sales. The stand-level model describes the relationship between oak advanced regeneration distribution (the percentage of 4 m2 sample plots that contained at least one oak seedling before harvest) and third-decade seed-origin oak stocking (the percentage of growing space occupied by seed-origin oaks in the third decade after harvest). Inverse modeling was used to fit a plot-level model using a highly detailed short-term data set collected as part of an ongoing study of regeneration development in Pennsylvania. A negative exponential function (1 – e–αx) was used for the plot-level model to simplify the calculation of multiple seedling success probabilities. The plot-level model predicts the probability that a 4 m2 plot will be occupied by an oak during the third decade after harvest based on the sum of the heights of oak advanced regeneration (aggregate height). The top-down inverse modeling approach used here proved to be a feasible alternative to the more common individual seedling modeling approach, which requires more specialized data that are often difficult to obtain.

A probabilistic modeling approach for interpretable data inference and classification

2020 ◽  
pp. 1-17
Author(s):  
Shuaiyu Yao ◽  
Jian-Bo Yang ◽  
Dong-Ling Xu
Keyword(s):  
Statistical Analysis ◽  
Probabilistic Modeling ◽  
Data Set ◽  
Inference Mechanism ◽  
Modeling Approach ◽  
Input Variables ◽  
Data Inference ◽  
The Relationship ◽  
Better Than ◽  
Iris Data

In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana, Haberman’s survival, and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes.

scholarly journals OMBO: An opponent modeling approach

2009 ◽  
Vol 22 (1) ◽  
pp. 21-35 ◽  
Author(s):  
Agapito Ledezma ◽  
Ricardo Aler ◽  
Araceli Sanchis ◽  
Daniel Borrajo
Keyword(s):  
Modeling Approach ◽  
Opponent Modeling

scholarly journals Modeling of Immune Reconstitution Post CD34 Selected Stem Cell Transplantation in Pediatric Patients with Severe Combined Immune Deficiency

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5668-5668
Author(s):  
Jean-Sebastien Diana ◽  
Naim Bouazza ◽  
Chloé Couzin ◽  
Martin Castelle ◽  
Alessandra Magnani ◽  
...  
Keyword(s):  
T Cells ◽  
Stem Cell ◽  
T Cell ◽  
Immune Reconstitution ◽  
Pediatric Patients ◽  
Conditioning Regimen ◽  
Lymphocyte Function ◽  
Inherited Disorders ◽  
Data Set ◽  
Modeling Approach

Severe combined immunodeficiencies (SCID) are a heterogeneous group of inherited disorders characterized by a profound reduction or alteration of T lymphocyte function. They arise from a variety of molecular defects which affect T lymphocytes development and function. The number of infections prior hematopietic stem cells tansplantaton (HSCT), genotype, and the type of donor are described as prognostic factors for stem cell transplants. In this retrospective study, we included 30 pediatric patients suffering from SCID who underwent to CD34+-selected grafts between January 2008 to December 2017 in our center. Diagnosis of reticular dysgenesis, ADA deficiency, or leaky SCIDs and intra thymic deficiency were excluded. A mechanistic mathematical model of all available data was performed and provided a dynamic appreciation of immune reconstitution, while removing bias. T-cell populations were maintained through proliferation and loss model and thymic output have been integrated to the production function. This joint modeling approach aimed to predict rate and extent of T cell immune reconstitution over time (mainly CD3+ T cells, CD3+CD4+ helper T cells, and the CD3+CD4+ CD45RA+ cells). With a median follow-up time of 97.28 months [range 0.85; 131.54], there were 345 points of T cell phenotyping concentrations in total with a median of 12 samples per patient (range, 0 -35 samples) taken post-transplantation. In this data-set, 13 % of the patients (n= 4) died from infections. Time to reach half of the maximal T cell concentrations was estimated to 3.4 months, 95%CI [2.5 - 4.6]. In covariate analysis, genetic diagnosis (p= 0.0047) and conditioning regimen (p= 0.01) were found to be significant pre transplant covariates which impacted the trajectory of T cell concentration with time. This modeling approach appeared to be the best method to learn about the dynamic T cell reconstitution after transplant in patients suffering from SCID. In the context of new cell therapy approach for T cell depletion and in vitro thymic maturation, this mechanistic joint model can be used for the design and analysis of incoming clinical trials. Figure Disclosures Cavazzana: Smartimmune: Other: Founder of Smartimmune.

scholarly journals Identifying and assessing the impact of key neighborhood-level determinants on geographic variation in stroke: a machine learning and multilevel modeling approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jiayi Ji ◽  
Liangyuan Hu ◽  
Bian Liu ◽  
Yan Li
Keyword(s):  
Machine Learning ◽  
Multilevel Modeling ◽  
High Performance ◽  
Learning Algorithm ◽  
Key Factors ◽  
Data Set ◽  
Modeling Approach ◽  
Stroke Prevalence ◽  
The Impact ◽  
Neighborhood Level

Abstract Background Stroke is a chronic cardiovascular disease that puts major stresses on U.S. health and economy. The prevalence of stroke exhibits a strong geographical pattern at the state-level, where a cluster of southern states with a substantially higher prevalence of stroke has been called the stroke belt of the nation. Despite this recognition, the extent to which key neighborhood characteristics affect stroke prevalence remains to be further clarified. Methods We generated a new neighborhood health data set at the census tract level on nearly 27,000 tracts by pooling information from multiple data sources including the CDC’s 500 Cities Project 2017 data release. We employed a two-stage modeling approach to understand how key neighborhood-level risk factors affect the neighborhood-level stroke prevalence in each state of the US. The first stage used a state-of-the-art Bayesian machine learning algorithm to identify key neighborhood-level determinants. The second stage applied a Bayesian multilevel modeling approach to describe how these key determinants explain the variability in stroke prevalence in each state. Results Neighborhoods with a larger proportion of older adults and non-Hispanic blacks were associated with neighborhoods with a higher prevalence of stroke. Higher median household income was linked to lower stroke prevalence. Ozone was found to be positively associated with stroke prevalence in 10 states, while negatively associated with stroke in five states. There was substantial variation in both the direction and magnitude of the associations between these four key factors with stroke prevalence across the states. Conclusions When used in a principled variable selection framework, high-performance machine learning can identify key factors of neighborhood-level prevalence of stroke from wide-ranging information in a data-driven way. The Bayesian multilevel modeling approach provides a detailed view of the impact of key factors across the states. The identified major factors and their effect mechanisms can potentially aid policy makers in developing area-based stroke prevention strategies.

The influence of behavioral and emotional characteristics on academic achievement of middle school students: A growth modeling approach

2019 ◽  
Vol 40 (5) ◽  
pp. 433-455
Author(s):  
Yujeong Park ◽  
Dong Gi Seo ◽  
Jaekook Park ◽  
Byungkeon Kim ◽  
Jeongwook Choi
Keyword(s):  
Middle School ◽  
Social Withdrawal ◽  
Behavioral Control ◽  
Growth Modeling ◽  
Self Esteem ◽  
Middle School Student ◽  
Future Research ◽  
School Students ◽  
Data Set ◽  
Modeling Approach

The purpose of this study was to examine the associations between behavioral and emotional characteristics and middle school student achievement across different grades based on a growth modeling approach. Using a total of 1,874 students, target predictor variables (i.e., attention, aggressiveness, behavioral control, social withdrawal, depression, self-esteem) and dependent variables (i.e., Korean language arts, mathematics) were extracted from a national and longitudinal data set, and four predictor models were formulated to examine the influence of behavioral/emotional characteristics on student growth trajectories. Results showed that (a) students' initial performance at seventh grade did not predict their over-time growth; and (b) self-esteem and behavioral control variables impacted on the seventh graders' achievement as well as their growth from the seventh to ninth grade. Based on the findings, practical implications and future research are discussed.

scholarly journals Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya

2018 ◽  
Vol 7 (11) ◽  
pp. 429 ◽  
Author(s):  
Kyalo Richard ◽  
Elfatih Abdel-Rahman ◽  
Samira Mohamed ◽  
Sunday Ekesi ◽  
Christian Borgemeister ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Land Surface ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Data Set ◽  
Habitat Suitability Modeling ◽  
Modeling Approach ◽  
Trioza Erytreae ◽  
Specific Implementation

Citrus is considered one of the most important fruit crops globally due to its contribution to food and nutritional security. However, the production of citrus has recently been in decline due to many biological, environmental, and socio-economic constraints. Amongst the biological ones, pests and diseases play a major role in threatening citrus quantity and quality. The most damaging disease in Kenya, is the African citrus greening disease (ACGD) or Huanglongbing (HLB) which is transmitted by the African citrus triozid (ACT), Trioza erytreae. HLB in Kenya is reported to have had the greatest impact on citrus production in the highlands, causing yield losses of 25% to 100%. This study aimed at predicting the occurrence of ACT using an ecological habitat suitability modeling approach. Specifically, we tested the contribution of vegetation phenological variables derived from remotely-sensed (RS) data combined with bio-climatic and topographical variables (BCL) to accurately predict the distribution of ACT in citrus-growing areas in Kenya. A MaxEnt (maximum entropy) suitability modeling approach was used on ACT presence-only data. Forty-seven (47) ACT observations were collected while 23 BCL and 12 RS covariates were used as predictor variables in the MaxEnt modeling. The BCL variables were extracted from the WorldClim data set, while the RS variables were predicted from vegetation phenological time-series data (spanning the years 2014–2016) and annually-summed land surface temperature (LST) metrics (2014–2016). We developed two MaxEnt models; one including both the BCL and the RS variables (BCL-RS) and another with only the BCL variables. Further, we tested the relationship between ACT habitat suitability and the surrounding land use/land cover (LULC) proportions using a random forest regression model. The results showed that the combined BCL-RS model predicted the distribution and habitat suitability for ACT better than the BCL-only model. The overall accuracy for the BCL-RS model result was 92% (true skills statistic: TSS = 0.83), whereas the BCL-only model had an accuracy of 85% (TSS = 0.57). Also, the results revealed that the proportion of shrub cover surrounding citrus orchards positively influenced the suitability probability of the ACT. These results provide a resourceful tool for precise, timely, and site-specific implementation of ACGD control strategies.

A UNIFIED APPROACH FOR DEVELOPING SOFTWARE RELIABILITY GROWTH MODEL USING INFINITE SERVER QUEUING MODEL

2010 ◽  
Vol 17 (05) ◽  
pp. 401-424 ◽  
Author(s):  
P. K. KAPUR ◽  
SAMEER ANAND ◽  
SHINJI INOUE ◽  
SHIGERU YAMADA
Keyword(s):  
Software Reliability ◽  
Queuing Theory ◽  
Real Life ◽  
Reliability Growth ◽  
Queuing Model ◽  
Data Set ◽  
Infinite Server ◽  
Modeling Approach ◽  
Stage Process ◽  
Software Reliability Growth

In the past 35 years numerous software reliability growth models (SRGMs) have been proposed under diverse testing and debugging (T&D) environments and applied successfully in many real life software projects but no SRGM can claim to be the best in general as the physical interpretation of the T&D is not general. Unified modeling approach proves to be very successful in this regard and provides an excellent platform for obtaining several existing SRGM following single methodology. It forms the main focus of this paper; here we propose a unification modeling approach applying the infinite server queuing theory based on the basic assumptions of an SRGM defining three level of complexity of faults. Our unification methodology can be used to obtain several existing and new SRGMs which consider testing a one stage process with no fault categorization, two/three stage process with random delay functions and hence categorize faults in two/three level complexity. We have also provided data analysis based on two actual T&D data set for some of the models discussed and proposed in the paper.

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