Data mining-based adaptive regression for developing equilibrium speed–density relationships

2010 ◽  
Vol 37 (3) ◽  
pp. 389-400 ◽  
Author(s):  
Lu Sun ◽  
Jun Yang ◽  
Hani Mahmassani ◽  
Wenjun Gu ◽  
Bum-Jin Kim
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Computer Software ◽  
Model Parameters ◽  
Traffic Data ◽  
Traffic Operations ◽  
Density Relation ◽  
Best Fitting ◽  
Adaptive Regression ◽  
Fitting Model

In this paper, we developed a methodological framework to deal with traffic-stream modeling based on data mining, steepest-ascend algorithm, and genetic algorithm. The new method is adaptive in nature and has a greater flexibility and generality compared with existing methods. It provides an optimum overall fitting of the observed data. Specifically, the advantages of adaptive regression are that (1) knot positions and model parameters are estimated optimally and simultaneously using genetic algorithm, and presetting of knot positions can be performed in terms of either density or speed; (2) the method is automatic and data driven, and it will always find out the best fitting model to site-dependent actual traffic data; and (3) the user has a great flexibility to specify the degree-model continuity and to define and add new basis functions that are parsimonious and fit better into the traffic data in some regime of speed–density relation. The proposed method and developed computer software package MiningFlow will be beneficial to traffic operations and traffic simulation.

An Expanded Assessment of Data Mining Approaches for Analyzing Actuarial Student Success Rate

2016 ◽  
Vol 3 (1) ◽  
pp. 22-44 ◽  
Author(s):  
Alan Olinsky ◽  
Phyllis Schumacher ◽  
John Quinn
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Student Success ◽  
Regression Model ◽  
Predictive Models ◽  
Drop Out ◽  
New Approach ◽  
Best Fitting ◽  
Input Variables ◽  
Fitting Model

One way to enhance the likelihood that more university students will graduate within the specific major that they begin with is to attract the type of students who have typically (historically) done well in that field of study. This paper expands upon a study that utilizes data mining techniques to analyze the characteristics of students who enroll as actuarial students and then either drop out of the major or graduate as actuarial students. Several predictive models including logistic regression, neural networks and decision trees are obtained using input variables describing academic attributes of the students. The models are then compared and the best fitting model is determined. The regression model turns out to be the best predictor. Since this is a very well understood method, it can easily be explained. The decision tree, although its underpinnings are somewhat difficult to explain, gives a clear and well understood output. In addition, the non-predictive method of cluster analysis is applied in order to group these students into distinct classifications based on the values of the input variables. Finally, a new approach to modeling in SAS®, called Rapid Predictive Modeler (RPM), is described and utilized. The results of the RPM also select the regression model as the best predictor.

Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate

Data Mining ◽  
2013 ◽  
pp. 1819-1834
Author(s):  
Alan Olinsky ◽  
Phyllis A. Schumacher ◽  
John Quinn
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Logistic Regression ◽  
Decision Tree ◽  
Student Success ◽  
Predictive Models ◽  
Drop Out ◽  
Predicting Success ◽  
Best Fitting ◽  
Fitting Model

One way to enhance the likelihood that more students will graduate within the specific major that they begin with is to attract the type of students who have typically (historically) done well in that field of study. This chapter details a study that utilizes data mining techniques to analyze the characteristics of students who enroll as actuarial students and then either drop out of the major or graduate as actuarial students. Several predictive models including logistic regression, neural networks and decision trees are obtained. The models are then compared and the best fitting model is determined. The regression model turns out to be the best predictor. Since this is a very well understood method, it can easily be explained. The decision tree, although its underpinnings are somewhat difficult to explain, gives a clear and well understood output. Not only is the resulting model a good one for predicting success in the major, it also allows us the ability to better counsel students.

scholarly journals Climatic suitability of the eastern paralysis tick, Ixodes holocyclus, and its likely geographic distribution in the year 2050

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ram K. Raghavan ◽  
Z. Koestel ◽  
R. Ierardi ◽  
A. Townsend Peterson ◽  
Marlon E. Cobos
Keyword(s):  
Spatial Distribution ◽  
Western Australia ◽  
Statistical Significance ◽  
Model Parameters ◽  
Moderate Increase ◽  
Climatic Suitability ◽  
Ixodes Holocyclus ◽  
Best Fitting ◽  
Paralysis Tick ◽  
Fitting Model

AbstractThe eastern paralysis tick, Ixodes holocyclus is one of two ticks that cause potentially fatal tick paralysis in Australia, and yet information on the full extent of its present or potential future spatial distribution is not known. Occurrence data for this tick species collected over the past two decades, and gridded environmental variables at 1 km2 resolution representing climate conditions, were used to derive correlative ecological niche models to predict the current and future potential distribution. Several hundreds of candidate models were constructed with varying combinations of model parameters, and the best-fitting model was chosen based on statistical significance, omission rate, and Akaike Information Criterion (AICc). The best-fitting model matches the currently known distribution but also extends through most of the coastal areas in the south, and up to the Kimbolton peninsula in Western Australia in the north. Highly suitable areas are present around south of Perth, extending towards Albany, Western Australia. Most areas in Tasmania, where the species is not currently present, are also highly suitable. Future spatial distribution of this tick in the year 2050 indicates moderate increase in climatic suitability from the present-day prediction but noticeably also moderate to low loss of climatically suitable areas elsewhere.

Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate

2011 ◽  
pp. 169-185
Author(s):  
Alan Olinsky ◽  
Phyllis A. Schumacher ◽  
John Quinn
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Logistic Regression ◽  
Decision Tree ◽  
Student Success ◽  
Predictive Models ◽  
Drop Out ◽  
Predicting Success ◽  
Best Fitting ◽  
Fitting Model

One way to enhance the likelihood that more students will graduate within the specific major that they begin with is to attract the type of students who have typically (historically) done well in that field of study. This chapter details a study that utilizes data mining techniques to analyze the characteristics of students who enroll as actuarial students and then either drop out of the major or graduate as actuarial students. Several predictive models including logistic regression, neural networks and decision trees are obtained. The models are then compared and the best fitting model is determined. The regression model turns out to be the best predictor. Since this is a very well understood method, it can easily be explained. The decision tree, although its underpinnings are somewhat difficult to explain, gives a clear and well understood output. Not only is the resulting model a good one for predicting success in the major, it also allows us the ability to better counsel students.

scholarly journals The KBC void and Hubble tension contradict ΛCDM on a Gpc scale − Milgromian dynamics as a possible solution

2020 ◽  
Vol 499 (2) ◽  
pp. 2845-2883 ◽  
Author(s):  
Moritz Haslbauer ◽  
Indranil Banik ◽  
Pavel Kroupa
Keyword(s):  
Local Group ◽  
A Priori ◽  
Model Parameters ◽  
Microwave Background ◽  
Sterile Neutrinos ◽  
Best Fitting ◽  
The Many ◽  
First Time ◽  
The Universe ◽  
Fitting Model

ABSTRACT The KBC void is a local underdensity with the observed relative density contrast δ ≡ 1 − ρ/ρ0 = 0.46 ± 0.06 between 40 and 300 Mpc around the Local Group. If mass is conserved in the Universe, such a void could explain the 5.3σ Hubble tension. However, the MXXL simulation shows that the KBC void causes 6.04σ tension with standard cosmology (ΛCDM). Combined with the Hubble tension, ΛCDM is ruled out at 7.09σ confidence. Consequently, the density and velocity distribution on Gpc scales suggest a long-range modification to gravity. In this context, we consider a cosmological MOND model supplemented with $11 \, \rm {eV}/c^{2}$ sterile neutrinos. We explain why this νHDM model has a nearly standard expansion history, primordial abundances of light elements, and cosmic microwave background (CMB) anisotropies. In MOND, structure growth is self-regulated by external fields from surrounding structures. We constrain our model parameters with the KBC void density profile, the local Hubble and deceleration parameters derived jointly from supernovae at redshifts 0.023−0.15, time delays in strong lensing systems, and the Local Group velocity relative to the CMB. Our best-fitting model simultaneously explains these observables at the $1.14{{\ \rm per\ cent}}$ confidence level (2.53σ tension) if the void is embedded in a time-independent external field of ${0.055 \, a_{_0}}$. Thus, we show for the first time that the KBC void can naturally resolve the Hubble tension in Milgromian dynamics. Given the many successful a priori MOND predictions on galaxy scales that are difficult to reconcile with ΛCDM, Milgromian dynamics supplemented by $11 \, \rm {eV}/c^{2}$ sterile neutrinos may provide a more holistic explanation for astronomical observations across all scales.

Network Coordinated Ramp Metering Control Strategy: Optimization and Evaluation

2014 ◽  
Vol 587-589 ◽  
pp. 2084-2088
Author(s):  
Jie Fang
Keyword(s):  
Control Strategy ◽  
Evaluation Method ◽  
Control Strategies ◽  
Evaluation Process ◽  
Ramp Metering ◽  
Model Parameters ◽  
Traffic Data ◽  
Traffic Operations ◽  
Microscopic Simulation ◽  
Effective Measure

The Ramp Metering (RM) control has been proven to be an effective measure for improving the efficiency of expressway operations. However, due to the complexity of network-wide coordinated RM control strategies, the model parameters in the control model requires to be carefully calibrated and optimized using the local traffic data. Establishing a quantitative evaluation method with reliable Measurement of Effectiveness (MOE)s is also a crucial step for optimizing of the control strategy. In this research, the author presents the procedures of optimizing the RM control strategy for implementing network-wide coordinated control as well as its evaluation methods. A field-data based microscopic simulation site was built to reproduce the traffic environment and implement the optimized control strategies. It was proved that the optimized control strategy can substantially improve the efficiency of traffic operations in the implemented network. Therefore, the proposed control strategy optimization and evaluation process is validated.

A Survey on Data Mining using Genetic Algorithm

2019 ◽  
Vol 7 (6) ◽  
pp. 888-891
Author(s):  
Mariya Khatoon ◽  
Abhay Kumar Agarwal
Keyword(s):  
Data Mining ◽  
Genetic Algorithm

165 Effects of Digestible Lysine Level on Growth Performance and Economics of Grow-finish Pigs

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 58-59
Author(s):  
Larissa L Becker ◽  
Emily E Scholtz ◽  
Joel M DeRouchey ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
...  
Keyword(s):  
Phase 1 ◽  
Information Criterion ◽  
Response Curves ◽  
Phase 3 ◽  
Ileal Digestibility ◽  
Distillers Dried Grains ◽  
Feed Cost ◽  
Best Fitting ◽  
Dietary Treatments ◽  
Fitting Model

Abstract A total of 2,124 barrows and gilts (PIC 1050′DNA 600, initially 48.9 kg) were used in a 32-d study to determine the optimal dietary standardized ileal digestibility (SID) Lys level in a commercial setting. Pigs were randomly allotted to 1 of 5 dietary treatments with 24 to 27 pigs/pen and 16 replications/treatment. Similar number of barrows and gilts were placed in each pen. Diets were fed over 3 phases (48.9 to 58.6, 58.6 to 70.9, and 70.9 to 80.8 kg respectively). Dietary treatments were corn-soybean meal-based and contained 10 (phase 1 and 2) or 5% (phase 3) distillers dried grains with solubles. Diets were formulated to 85, 95, 103, 110, or 120% of the current Pig Improvement Company (PIC, Hendersonville, TN) SID Lys gilt recommendations with phase 1 SID Lys levels of 0.90, 1.01, 1.09, 1.17 and 1.27%, phase 2 levels of 0.79, 0.87, 0.94, 1.03, and 1.10%, and phase 3 levels of 0.71, 0.78, 0.85, 0.92, and 0.99%, respectively. Dose response curves were evaluated using linear (LM), quadratic polynomial (QP), broken-line linear (BLL), and broken-line quadratic (BLQ) models. For each response variable, the best-fitting model was selected using the Bayesian information criterion. Overall (d 0 to 32), increasing SID Lys increased (linear, P< 0.001) BW, ADG, G:F, Lys intake/d, and Lys intake/kg of gain. Modeling margin over feed cost (MOFC), BLL and QP estimated the requirement at 105.8% and 113.7% respectively. In summary, while growth increased linearly up to 120% of the PIC current feeding level, the optimal MOFC was 106% to 114% depending on the model used.

Genetic and Environmental Influences of Dietary Indices in a UK Female Twin Cohort

2020 ◽  
Vol 23 (6) ◽  
pp. 330-337
Author(s):  
Olatz Mompeo ◽  
Rachel Gibson ◽  
Paraskevi Christofidou ◽  
Tim D. Spector ◽  
Cristina Menni ◽  
...  
Keyword(s):  
Healthy Eating ◽  
Menopausal Status ◽  
Healthy Eating Index ◽  
Healthy Diet ◽  
Nutritional Epidemiology ◽  
Best Fitting ◽  
Heritability Estimates ◽  
Fitting Model ◽  
Dietary Indices ◽  
Health Parameters

AbstractA healthy diet is associated with the improvement or maintenance of health parameters, and several indices have been proposed to assess diet quality comprehensively. Twin studies have found that some specific foods, nutrients and food patterns have a heritable component; however, the heritability of overall dietary intake has not yet been estimated. Here, we compute heritability estimates of the nine most common dietary indices utilized in nutritional epidemiology. We analyzed 2590 female twins from TwinsUK (653 monozygotic [MZ] and 642 dizygotic [DZ] pairs) who completed a 131-item food frequency questionnaire (FFQ). Heritability estimates were computed using structural equation models (SEM) adjusting for body mass index (BMI), smoking status, Index of Multiple Deprivation (IMD), physical activity, menopausal status, energy and alcohol intake. The AE model was the best-fitting model for most of the analyzed dietary scores (seven out of nine), with heritability estimates ranging from 10.1% (95% CI [.02, .18]) for the Dietary Reference Values (DRV) to 42.7% (95% CI [.36, .49]) for the Alternative Healthy Eating Index (A-HEI). The ACE model was the best-fitting model for the Healthy Diet Indicator (HDI) and Healthy Eating Index 2010 (HEI-2010) with heritability estimates of 5.4% (95% CI [−.17, .28]) and 25.4% (95% CI [.05, .46]), respectively. Here, we find that all analyzed dietary indices have a heritable component, suggesting that there is a genetic predisposition regulating what you eat. Future studies should explore genes underlying dietary indices to further understand the genetic disposition toward diet-related health parameters.

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