scholarly journals Statistical Methods in Bidding Decision Support for Construction Companies

2021 ◽  
Vol 11 (13) ◽  
pp. 5973
Author(s):  
Agnieszka Leśniak
Keyword(s):  
Life Cycle ◽  
Discriminant Analysis ◽  
Time Frame ◽  
Classification Model ◽  
Linear Discriminant ◽  
Construction Companies ◽  
Execution Phase ◽  
Subjective Judgement ◽  
Input Variables ◽  
Two Phases

On the border of two phases of a building life cycle (LC), the programming phase (conception and design) and the execution phase, a contractor is selected. A particularly appropriate method of selecting a contractor for the construction market is the tendering system. It is usually based on quality and price criteria. The latter may involve the price (namely, direct costs connected with works realization as well as mark-ups, mainly overhead costs and profit) or cost (based on the life cycle costing (LCC) method of cost efficiency). A contractor’s decision to participate in a tender and to calculate a tender requires an investment of time and company resources. As this decision is often made in a limited time frame and based on the experience and subjective judgement of the contractor, a number of models have been proposed in the literature to support this process. The present paper proposes the use of statistical classification methods. The response obtained from the classification model is a recommendation to participate or not. A database consisting of historical data was used for the analyses. Two models were proposed: the LOG model—using logit regression and the LDA model—using linear discriminant analysis, which obtain better results. In the construction of the LDA model, the equation of the discriminant function was sought by indicating the statistically significant variables. For this purpose, the backward stepwise method was applied, where initially all input variables were introduced, namely, 15 identified bidding factors, and then in subsequent steps, the least statistically significant variables were removed. Finally, six variables (factors) were identified that significantly discriminate between groups: type of works, contractual conditions, project value, need for work, possible participation of subcontractors, and the degree of difficulty of the works. The model proposed in this paper using a discriminant analysis with six input variables achieved good performance. The results obtained prove that it can be used in practice. It should be emphasized, however, that mathematical models cannot replace the decision-maker’s thought process, but they can increase the effectiveness of the bidding decision.

Classification of Diesel Engine Health Using Sparse Linear Discriminant Analysis (SLDA)

2009 ◽  
Author(s):  
Neha Chandrachud ◽  
Ravindra Kakade ◽  
Peter H. Meckl ◽  
Galen B. King ◽  
Kristofer Jennings
Keyword(s):  
Steady State ◽  
Diesel Engine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Optimal Number ◽  
Classification Model ◽  
Misclassification Rate ◽  
Linear Discriminant ◽  
State Data ◽  
Input Variables

With requirements for on-board diagnostics on diesel engines becoming more stringent for the coming model years, diesel engine manufacturers must improve their ability to identify fault conditions that lead to increased exhaust emissions. This paper proposes a statistical classifier model to identify the state of the engine, i.e. healthy or faulty, using an optimal number of sensors based on the data acquired from the engine. The classification model proposed in this paper is based on Sparse Linear Discriminant Analysis. This technique performs Linear Discriminant Analysis with a sparseness criterion imposed such that classification, dimension reduction and feature selection are merged into one step. It was concluded that the analysis technique could produce 0% misclassification rate for the steady-state data acquired from the diesel engine using five input variables. The classifier model was also extended to transient operation of the engine. The misclassification rate in the case of transient data was reduced from 31% to 26% by using the steady-state data trained classifier using thirteen variables.

scholarly journals Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods

2020 ◽  
Vol 10 (19) ◽  
pp. 6724
Author(s):  
Youngwook Seo ◽  
Ahyeong Lee ◽  
Balgeum Kim ◽  
Jongguk Lim
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Partial Least Square ◽  
Hyperspectral Images ◽  
Classification Model ◽  
Support Vector ◽  
Chemometric Methods ◽  
Infrared Wavelength ◽  
Linear Discriminant

(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.

scholarly journals Linear Discriminant Analysis Successfully Predicts Knee Injury Outcome From Biomechanical Variables

2020 ◽  
Vol 48 (10) ◽  
pp. 2447-2455
Author(s):  
Nathan D. Schilaty ◽  
Nathaniel A. Bates ◽  
Sydney Kruisselbrink ◽  
Aaron J. Krych ◽  
Timothy E. Hewett
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Knee Injury ◽  
Factor Model ◽  
Tibial Rotation ◽  
Linear Discriminant ◽  
Mechanical Impact ◽  
Knee Kinetics ◽  
Input Variables ◽  
Injury Outcomes

Background: The most commonly damaged structures of the knee are the anterior cruciate ligament (ACL), medial collateral ligament (MCL), and menisci. Given that these injuries present as either isolated or concomitant, it follows that these events are driven by specific mechanics versus coincidence. This study was designed to investigate the multiplanar mechanisms and determine the important biomechanical and demographic factors that contribute to classification of the injury outcome. Hypothesis: Linear discriminant analysis (LDA) would accurately classify each injury type generated by the mechanical impact simulator based on biomechanical input variables (ie, ligament strain and knee kinetics). Study Design: Controlled laboratory study. Methods: In vivo kinetics and kinematics of 42 healthy, athletic participants were measured to determine stratification of injury risk (ie, low, medium, and high) in 3 degrees of knee forces/moments (knee abduction moment, anterior tibial shear, and internal tibial rotation). These stratified kinetic values were input into a cadaveric impact simulator to assess ligamentous strain and knee kinetics during a simulated landing task. Uniaxial and multiaxial load cells and implanted strain sensors were used to collect mechanical data for analysis. LDA was used to determine the ability to classify injury outcome by demographic and biomechanical input variables. Results: From LDA, a 5-factor model (Entropy R2 = 0.26) demonstrated an area under the receiver operating characteristic curve (AUC) for all 5 injury outcomes (ACL, MCL, ACL+MCL, ACL+MCL+meniscus, ACL+meniscus) of 0.74 or higher, with “good” prediction for 4 of 5 injury classifications. A 10-factor model (Entropy R2 = 0.66) improved the AUC to 0.86 or higher, with “excellent” prediction for 5 injury classifications. The 15-factor model (Entropy R2 = 0.85), produced 94.1% accuracy with the AUC 0.98 or higher for all 5 injury classifications. Conclusion: Use of LDA accurately predicted the outcome of knee injury from kinetic data from cadaveric simulations with the use of a mechanical impact simulator at 25° of knee flexion. Thus, with clinically relevant kinetics, it is possible to determine clinical risk of injury and also the likely presentation of singular or concomitant knee injury. Clinical Relevance: LDA demonstrates that injury outcomes are largely characterized by specific mechanics that can distinguish ACL, MCL, and medial meniscal injury. Furthermore, as the mechanics of injury are better understood, improved interventional prehabilitation can be designed to reduce these injuries.

scholarly journals Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors

2020 ◽  
Vol 4 ◽  
pp. 239784732097125
Author(s):  
Chirag N Patel ◽  
Sivakumar Prasanth Kumar ◽  
Rakesh M Rawal ◽  
Manishkumar B Thaker ◽  
Himanshu A Pandya
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Statistical Significance ◽  
Characteristic Curve ◽  
Hybrid Approach ◽  
Classification Model ◽  
Test Set ◽  
Linear Discriminant ◽  
Specificity And Sensitivity ◽  
Physico Chemical

Background: Bioinformatics and statistical analysis have been employed to develop a classification model to distinguish toxic and non-toxic molecules. Aims: The primary objective of this study is to enumerate the cut-off values of various physico-chemical (ligand-centric) and target interaction (receptor-centric) descriptors which forms the basis for classifying cardiotoxic and non-toxic molecules. We also sought correlation of molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) parameters, Lipinski rules, physico-chemical parameters, etc. of human cardiotoxicity drugs. Methods: A training and test set of 91 compounds were applied to linear discriminant analysis (LDA) using 2D and 3D descriptors as discriminating variables representing various molecular modeling parameters to identify which function of descriptor type is responsible for cardiotoxicity. Internal validation was performed using the leave-one-out cross-validation methodology ensuing in good results, assuring the stability of the discriminant function (DF). Results: The values of the statistical parameters Fisher Discriminant Analysis (FDA) and Wilk’s λ for the DF showed reliable statistical significance, as long as the success rate in the prediction for both the training and the test set attained more than 93% accuracy, 87.50% sensitivity and 94.74% specificity. Conclusion: The predictive model was built using a hybrid approach using organ-specific targets for docking and ADMET properties for the FDA (Food and Drug Administration) approved and withdrawn drugs. Classifiers were developed by linear discriminant analysis and the cut-off was enumerated by receiver operating characteristic curve (ROC) analysis to achieve reliable specificity and sensitivity.

scholarly journals Detecting BSR-infected Oil Palm Seedlings using Thermal Imaging Technique

2021 ◽  
Vol 34 ◽  
pp. 73-80
Author(s):  
Siti N. Á. M. Johari ◽  
Siti Khairunniza Bejo ◽  
Ghaibulna Abdol Lajis ◽  
Leona D. J. DaimDai ◽  
Neoh B. Keat ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Oil Palm ◽  
Thermal Imaging ◽  
Classification Model ◽  
Support Vector ◽  
Pixel Intensity ◽  
Linear Discriminant ◽  
Palm Trees ◽  
Water Transportation ◽  
Infected Seedling

Basal Stem Rot (BSR) is the most destructive disease instigated by a white wood rotting fungus called Ganoderma boninense, which cause great economic setback in oil palm productivity. It attacks the basal stem of oil palm trees, causing them to slowly rot. It also affects the xylem tissues that eventually interrupt water transportation to the upper part of the oil palm, turning the leaves at the frond become yellow. This problem should be prevented during nursery stage by separating between healthy and BSR-infected seedling. Therefore, this study focuses on the potential use of thermal imaging for detecting BSR in oil palm at seedling. Thermal images of oil palm seedling from healthy and BSR-infected were captured and processed to extract several thermal properties of the seedling, i.e., maximum, minimum, mean, and standard deviation of pixel intensity value. These values were then undergone statistical analysis to identify its significant different in differentiating healthy and BSR-infected seedling. Several classification models were tested including Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM) and k-Nearest Neighbour (kNN). Principal Component Analysis (PCA) was used to reduce the dimensionality of the dataset. The results demonstrated that the highest accuracy achieved at 80.0 % using SVM (fine gaussian) classification model with PC1 and PC3 as the input parameter. This summarizes the potential of thermal imaging in detecting BSR-infected oil palm trees at seedling stage.

Tabu Search for Variable Selection in Classification

2011 ◽  
pp. 1909-1915
Author(s):  
Silvia Casado Yusta ◽  
Joaquín Pacheco Bonrostro
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Tabu Search ◽  
Variable Selection ◽  
Classification Problem ◽  
Linear Functions ◽  
New Method ◽  
Linear Discriminant ◽  
Regression Methods ◽  
Input Variables

Variable selection plays an important role in classification. Before beginning the design of a classification method, when many variables are involved, only those variables that are really required should be selected. There can be many reasons for selecting only a subset of the variables instead of the whole set of candidate variables (Reunanen, 2003): (1) It is cheaper to measure only a reduced set of variables, (2) Prediction accuracy may be improved through the exclusion of redundant and irrelevant variables, (3) The predictor to be built is usually simpler and potentially faster when fewer input variables are used and (4) Knowing which variables are relevant can give insight into the nature of the prediction problem and allows a better understanding of the final classification model. The importance of variables selection before using classification methods is also pointed out in recent works such as Cai et al.(2007) and Rao and Lakshminarayanan (2007). The aim in the classification problem is to classify instances that are characterized by attributes or variables. Based on a set of examples (whose class is known) a set of rules is designed and generalised to classify the set of instances with the greatest precision possible. There are several methodologies for dealing with this problem: Classic Discriminant Analysis, Logistic Regression, Neural Networks, Decision Trees, Instance- Based Learning, etc. Linear Discriminant Analysis and Logistic Regression methods search for linear functions and then use them for classification purposes. They continue to be interesting methodologies. In this work an “ad hoc” new method for variable selection in classification, specifically in discriminant analysis and logistic regression, is analysed. This new method is based on the metaheuristic strategy tabu search and yields better results than the classic methods (stepwise, backward and forward) used by statistical packages such as SPSS or BMDP, as it’s shown below. This method is performed for 2 classes.

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