Telecommunications package recommendation algorithm based on Deep forest

In view of the wide variety of telecom packages and the difficulty of adapting to the needs of users, this paper introduces a recommendation model for telecom packages based on deep forests. This paper first analyzes the telecom package data, and then optimizes the deep forest according to its characteristics such as discrete, continuous attribute interleaving and high coupling characteristics, including the use of decision trees to discretize continuous features and design continuous window sliding mechanism. These methods can improve the ability of deep forest combination high coupling features. Finally, the model optimization measures were verified by detail experiments. The experimental results show that the optimized deep forest can be applied to the telecom package recommendation field. Compared with other shallow models and unoptimized deep forest models, the deep forest model has increased the F1 score by 5%; after adjusting the deep forest hyper parameters, the F1 score can be increased by 2%.

Falling through the cracks: Modeling the formation of social category boundaries

Social categorizations divide people into “us” and “them”, often along continuous attributes such as political ideology or skin color. This division results in both positive consequences, such as a sense of community, and negative ones, such as group conflict. Further, individuals in the middle of the spectrum can fall through the cracks of this categorization process and are seen as out-group by individuals on either side of the spectrum, becoming inbetweeners. Here, we propose a quantitative, dynamical-system model that studies the joint influence of cognitive and social processes. We model where two social groups draw the boundaries between “us” and ‘them” on a continuous attribute. Our model predicts that both groups tend to draw a more restrictive boundary than the middle of the spectrum. As a result, each group sees the individuals in the middle of the attribute space as an out-group. We test this prediction using U.S. political survey data on how political independents are perceived by registered party members as well as existing experiments on the perception of racially ambiguous faces, and find support.

Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Discretization based on rough sets is used to divide the space formed by continuous attribute values with as few breakpoint sets as possible, while maintaining the original indistinguishable relationship of the decision system, so as to accurately classify and identify related information. In this study, a discretization algorithm for incomplete economic information in rough set based on big data is proposed. First, the algorithm for filling-in incomplete economic information based on deep learning is used to supplement the incomplete economic information. Then, based on breakpoint discrimination, the algorithm for discretization in the rough set is used to implement the discretization based on rough set for supplementary economic information. The performance of this algorithm was tested using multiple sets of data and compared with other algorithms. Experimental results show that this algorithm is effective for discretization based on a rough set of incomplete economic information. When the number of incomplete economic information rough candidate breakpoints increases, it still has a higher computational efficiency and can effectively improve the integrity of incomplete economic information, and finally the application performance is superior.

Six Sigma: Main Metrics and R Based Software for Training Purposes and Practical Industrial Quality Control

<p><strong>Purpose:</strong> To clarify the different types of data likely to occur in any service or industrial process, the main applicable statistics for each type of data and the Six Sigma metrics that allow characterising and benchmarking organisational processes.</p><p><strong>Methodology/Approach:</strong> A short reference to the statistical process control is carried out, from Shewhart’s works to Motorola’s achievements, followed by a short discussion of the use of Six Sigma tools as a part of today’s total quality approaches, and by a discussion of the continuous, attribute and counting data worlds and their main applications in process analysis. Because many quality professionals may have difficulties dealing with engineering perspectives, a review of main classic and Six Sigma process metrics is done with examples. Complementing discussions, four functions written in the R language are presented, which can deal with real organisational data, or can be used for training purposes.</p><p><strong>Findings:</strong> The functions developed provide useful graphical displays and calculate all necessary metrics, having the ability to let the user provide theoretical values for training activities. Real and simulated case studies help understanding data worlds and respective Six Sigma metrics.</p><p><strong>Research Limitation/implication:</strong> This paper reports an intentionally simple theoretical perspective of Six Sigma metrics and friendly software which is available to all interested professionals on request to the authors.</p><strong>Originality/Value of paper:</strong> The paper presents clear definitions of main data types and metrics and is supported by a set of four new functions that can be used by any researcher with a minimum knowledge of the R software.

No Longer Discrete: Modeling the Dynamics of Social Networks and Continuous Behavior

The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale. Continuous variables first need to be discretized to fit into such a modeling framework. This article presents an extension of the stochastic actor-oriented model that does away with this restriction by using a stochastic differential equation to model the evolution of a continuous attribute. We propose a measure for explained variance and give an interpretation of parameter sizes. The proposed method is illustrated by a study of the relationship between friendship, alcohol consumption, and self-esteem among adolescents.

Conditions on the Energy Market Diversification from Adaptive Dynamics

We study a mathematical model based on ordinary differential equations to describe the dynamic interaction in the market of two types of energy called standard and innovative. The model consists of an adaptation of the generalized Lotka-Volterra system in which the parameters are assumed to depend on a quantitative and continuous attribute characteristic of energy generation. Using the analysis of the model the fitness function for the innovative energy is determined, from which conditions of invasion can be established in a market dominated by the conventional power. The canonical equation of the adaptive dynamics is studied to know the long-term behavior of the characteristic attribute and its impact on the market. Then we establish conditions under which evolutionary ramifications occur, that is to say, the requirements of coexistence and divergence of the characteristic attributes, whose occurrence leads to the origin of diversity in the energy market.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.