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In this paper, we propose an integrated model to solve the multi-criteria decision making problem.Firstly, AHP method is applied to calculate the weight value of each attribute on all the alternatives; secondly, TOPSIS method is used to get the preference value based on the weights calculated in the first step; finally, AFS concept description algorithm is employed to give the semantic descriptions of performance characteristics for all the alternatives. Compared with other models, this model can not only consider the comprehensive preference value, but also consider the specific performance characteristic of the alternative, which makes the decision more scientific. The supplier selection problem is applied to explain the application ability of this model.

Role positioning of women is an important research topic, which seriously affects women's life path. This paper constructs three-way decision models based on intuitive multi-attribute decision making method (MADM) to help women choose roles. First, by analysing the factors that affect the role positioning of modern women, the decision-making evaluation system is constructed. Second, decision attribute values for every female are given by intuitionistic fuzzy sets (IFS). Third, the conditional possibilities of becoming a professional female is computed combined with the attribute weights. Finally, each female's role is defined by comparing conditional probabilities and decision initial values computed by loss functions.

This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, “BehavDT” context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.

Fuzzy set theory, rough set theory and soft set theory are all generic mathematical tools for dealing with uncertainties. There has been some progress concerning practical applications of these theories, especially, the use of these theories in decision making problems. In the present article, we review some decision making methods based on (fuzzy) soft sets, rough soft sets and soft rough sets. In particular, we provide several novel algorithms in decision making problems by combining these kinds of hybrid models. It may be served as a foundation for developing more complicated soft set models in decision making.

Assessing the progress of lectures ensures the involvement of lectures in the exercise of science dissemination by research, education, and community service. That job affects the performance of lecturers at universities throughout the country. The performance of lecturers is evaluated regularly because of the vast number of evaluation attributes. Manual assessment can take time and cannot be carried out. In the evaluation of lecturer’s outcomes, a decision support model is needed to facilitate decision making. This study applies the preference ranking organization method for enrichment assessment (PROMETHEE) as a decision model in Multi-Criteria Decision Making (MCDM) and Geometric Analysis for Interactive Aid (GAIA) as a geometric analysis in determining lecturer performance. This analysis resulted in the maximal value of an alternative of 2 with Phi 0.2333, followed by 1 and 3 alternatives. These results suggest that alternative 2 is the best way to assess the performance of lecturers as the main indicator. Alternative 3 is the least recommendable alternative because of its lowest precision on the basis of visualization results.

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment 1 . The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject’s upcoming decision 2 . Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind 3 . Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making. In macaque motor cortex, moment-to-moment fluctuations in neurally derived decision variables are tightly linked to decision state and predict behavioural choices with better accuracy than condition-averaged decision variables or the visual stimulus alone, and can be used to distinguish between different models of decision making.

Security model is the main means to protect the information security of automobile network. Among many relevant security models, stochastic forest model is a strong classifier model and can better prevent overfitting than decision tree model. It has good characteristics in resisting flood attacks and other aspects, but it has poor ability to resist Sybil attacks. Therefore, an identity authentication system is added on the basis of the original random forest model, which can resist Sybil attacks while supporting the work in the Internet of vehicles environment. Experimental results show that the model can achieve the described effect.

Aiming at the problem of too coarse matching of machine learning decision tree models in the field of data mining and low prediction accuracy, the corresponding improved optimization strategies are proposed. First, the field matching degree of data is further improved by discretizing continuous attributes in multiple intervals. Then, the method makes the selection of business attributes more reasonable in the downward splitting process of the model by compensating the weight of feature attributes by business sensitivity indicators. Finally, the data classification rule transformation is used to further improve the data prediction accuracy of the model. The experimental results show that the introduction of the tree model generated by the business sensitivity index is more concise. In addition, the business pertinence and data classification capabilities are stronger. The results show that the transformed and upgraded data classification rules can effectively improve the accuracy of data prediction compared with the traditional optimization algorithm.

The paper aims to present a technique for optimizing industrial management processes by mathematical modeling using linear inequality systems. An issue facing an industrial enterprise regarding production costs will be presented and solved, after which the results will be interpreted to formulate the optimal solution. The main phases of solving a problem with the help of linear programming are presented as well as the detailed analysis of the obtained results. The reduction of costs due to the processes presented in the analysis is presented alternatively with the existing costs and the paper details how to save 336 euros per day, as well as an increase in profit by 1935 euros. At the same time, the paper aims to present a software product to support economic decision modeling with which to perform calculations and present the optimal decision.

This study analyzed the prognostic significance of clinico-pathologic factors, including the number of metastatic lymph nodes (LNs) and lymph node ratio (LNR), in patients with papillary thyroid carcinoma (PTC), and attempted to construct a disease recurrence prediction model using machine learning techniques. We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with PTC between 2003 and 2009. We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Among those factors mentioned above, LNR and contralateral LN metastasis were used as important features in all machine learning prediction models. We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. LNR and contralateral LN metastasis were used as important features for constructing a robust machine learning prediction model. In the future, we have a plan to perform large-scale multicenter clinical studies to improve the performance of our prediction models and verify their clinical effectiveness.