In the last couple of decades, data analytics-based pattern classification methods for disease detection have gained much traction in healthcare research and applications. The current study builds linear programming (...
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In the last couple of decades, data analytics-based pattern classification methods for disease detection have gained much traction in healthcare research and applications. The current study builds linear programming (LP) models for detecting disease incidence. We propose sequential steps of a convex programming algorithm to construct decision boundary functions to classify patterns in disease detection data. We compare the performance of our LP-based classifier with others (neural network, decision tree, k-nearest-neighbor, logistic regression, naive-Bayes, and support-vector-machine) on four datasets: two different ones for breast cancer, and one each for diabetes and diabetic retinopathy. Statistical tests reveal that the LP classifier did significantly better than the other methods in five out of eight false-positive and false-negative test cases. There is not a statistically significant difference in performance in the remaining three tests between the LP classifier and the best alternative method. Most importantly, the LP classifier has significantly superior performance in both diabetes detection and diabetic retinopathy data. The success of the proposed LP classifier results from avoiding "modeling noise" and "memorization of training data." We recommend that our proposed LP classifier be among the set of classifiers for use in disease detection analytics.
This letter proposes a machine learning-based linear programming model that quickly establishes the nonparametric prediction intervals of wind power by integrating extreme learning machine and quantile regression. The...
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This letter proposes a machine learning-based linear programming model that quickly establishes the nonparametric prediction intervals of wind power by integrating extreme learning machine and quantile regression. The proportions of quantiles can be adaptively determined via sensitivity analysis. The proposed method has been proven to be significantly efficient and reliable, with a high application potential in power systems.
Repetitiveness in project's activities has gained an important role in the construction industry. Multiple linear scheduling methods have been proposed in order to fully take advantage of the spatial and temporal ...
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Repetitiveness in project's activities has gained an important role in the construction industry. Multiple linear scheduling methods have been proposed in order to fully take advantage of the spatial and temporal information these type of project can provide to practitioners. Besides the advances in the optimization models in these fields, to the extent of the authors knowledge, there is still pending a complete and flexible mathematical linear programming formulation that allow practitioners to easily and jointly solve the Resource allocation, Resource-Constrained Project Scheduling and Time-Cost Tradeoff problem, taking into account as many scheduling properties, benefits and challenges that linear scheduling of repetitive activities imply. This paper shows a complete guide and computational experimentation, of a novel mathematical model that can be easily used by practitioners to optimize construction schedules considering to the largest extent the time and space conditions repetitive projects offer. Particularly, it contributes to the repetitive activities scheduling body of knowledge by successfully implementing a robust linear programing optimization model in a real construction project, while considering as much linear scheduling characteristics as possible. It proves that relationships in the sub-activity level, continuity conditions, multiple modes of execution, controlled acceleration routines and execution mode shifts, and multiple crews can be easily and jointly integrated to a linear optimization model by adding simple linear restrictions to the model.
The solution of problems arising in fabrication of optical systems often requires the optimization of a process, design, test, or evaluation related to those systems. There are different optimization techniques that c...
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The solution of problems arising in fabrication of optical systems often requires the optimization of a process, design, test, or evaluation related to those systems. There are different optimization techniques that can be used to achieve this goal. In some problems, the parameters to be found have a linear dependence and may be subject to certain restrictions. We present the use of linear programming (LP) to solve optical devices' fabrication problems. We first describe the LP concept, the standard form of an LP, and show how to give this form to an arbitrary problem. From there, fundamentals and steps of the simplex method and its use in finding an optimal solution are described. Next, we present its application to three different problems in the optical field, highlighting the advantages of modeling through LP: finding the shape of a surface, creating the optimal design of a polishing tool, and developing the analytical profile of a polishing tool. The objective is to exemplify the process of modeling an engineering problem in mathematical terms as an optimization problem of the LP type. We show the usefulness and easiness of implementing and potential for solving optimization problems through LP. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).
Low amylose rice, intermediate amylose rice, and seeded banana were taken for formulation of an extruded product using linear programming (LP) model. The model was designed to develop nutritious low-cost extrudate pro...
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Low amylose rice, intermediate amylose rice, and seeded banana were taken for formulation of an extruded product using linear programming (LP) model. The model was designed to develop nutritious low-cost extrudate products. The optimization for development of extrudates was performed by using central composite rotatable design (CCRD). The formulated extruded product developed at the optimized condition was found to have calorific value of 175 kcal/100 g and protein content of 6.35% with adequate amount of micro-nutrients like magnesium (15.3 mg/100 g) and potassium (33.2 mg/100 g), respectively. Cytotoxicity assay with various levels of the extrudate showed negligible toxicity against human embryonic kidney (HEK293) cells. Practical Application Ready-to-eat (RTE) foods are becoming widely popular due to change in lifestyle. However, RTE products are basically cereal-based and found to have low nutritional value. Thus, there is a need to increase protein and minerals in cereal-based extruded cereals.
In this paper, we are interested in the performance of Karmarkar's projective algorithm for linear programming. We propose a new displacement step to accelerate and improve the convergence of this algorithm. This ...
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In this paper, we are interested in the performance of Karmarkar's projective algorithm for linear programming. We propose a new displacement step to accelerate and improve the convergence of this algorithm. This purpose is confirmed by numerical experimentations showing the efficiency and the robustness of the obtained algorithm over Schrijver's one for small problem dimensions. (C) 2018 Elsevier B.V. All rights reserved.
This paper introduces a linear programming formulation for a ternary-integration Workforce Scheduling and Routing Problem that incorporates scheduling of tasks, assigning of workers to the tasks according to their ski...
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For a pair of dual inconsistent linear programming problems, the existence and uniqueness of a correction vector that is optimal in the norm is proved. It is shown that the correction problem is reduced to the problem...
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For a pair of dual inconsistent linear programming problems, the existence and uniqueness of a correction vector that is optimal in the norm is proved. It is shown that the correction problem is reduced to the problem of finding a saddle point of a regularized Lagrange function. A modified extragradient method is proposed for solving the latter problem, and its convergence is proved.
In this paper, we consider the expansion of linear inequality and some related theorems in the fuzzy case, and propose an optimization criterion of fuzzy linear problems. Firstly, the fuzzy version of Tucker's lem...
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Background: Lactating women need to consume a high-quality diet to replete nutrient stores depleted during pregnancy and to ensure sufficient nutrition for breastfeeding. However, several studies reported suboptimal d...
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Background: Lactating women need to consume a high-quality diet to replete nutrient stores depleted during pregnancy and to ensure sufficient nutrition for breastfeeding. However, several studies reported suboptimal dietary quality and nutrient intake of lactating mothers in China. The objectives of this study was to apply dietary modeling method to develop individualized optimal diets, which meet the nutrient requirements for lactating women in urban China. Methods: Data were collected from a sample of 576 lactating women from 0 to 240 days postpartum during the Maternal Infant Nutrition Growth study conducted between 2011 and 2012 in three cities including Beijing, Guangzhou, and Suzhou. Dietary intake data were collected with an interviewer-administered 24-h survey. linear programming was applied to develop dietary plans that meet recommendations for lactation women in the China Dietary Reference Intakes 2013 and the Chinese Dietary Guideline 2016, while with least deviation from the observed dietary intake. Results: Through dietary modeling, individual optimal diets were developed for 576 lactating women. The optimal diets met all the food and nutrient intake constraints set in the linear programming models. The large difference between observed and optimized diets suggests that the nutrient needs of lactating mothers in China may only be met after substantial dietary changes. In addition, the analysis showed that it was difficult to meet the recommended intake for six nutrients: vitamin A, vitamin B1, vitamin B6, calcium, selenium, and dietary fiber. Moreover, four clusters in the optimized diets were identified by K-means cluster analysis. The four clusters confirmed that the optimal diets developed by linear programming could characterize the variety in dietary habits by geographical regions and duration of lactation. Conclusion: linear programming could help translate nutrient recommendations into personal diet advices for a sample of urban lactating mothers
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