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Background: Experiment-driven development with the help of real usage data helps to build software products and services that are of high value to their users. As more software companies use experimentation in their development practises, ethical concerns are increasingly important. Objective: There is a need for understanding the ethical issues companies must take into account when practising experimentation...
Emergency decision itself is a scientific and complicated process, clarifying scarcity factors' influence on individual decision-making risk behaviors can help decision-making makers make a scientific decision. From scarcity factor in emergency, this study analyzes the decision-making risk behaviors, then puts forward the research hypotheses, Based on the questionnaire data, the research hypotheses...
Failure modes and effects analysis (FMEA) is a powerful and proactive quality tool for defining, detecting, and identifying potential failure modes and their effects. However, conventional FMEA process is sometimes difficult to implement due to workload required and subjectivity of the evaluations performed. Hence, automation of this tool can be useful for some application domains to objectively evaluate...
In this paper we present an approach for solving the problem of data association in multi-target tracking with evidence theory. The solution adopted for the representation of the available information in this method is based on a mapping between targets and observations. Furthermore we derive several basic belief mass functions to build different possible mapping. Using this mapping we make decision...
Discovering patterns from big data attracts a lot of attention due to its importance in discovering accurate patterns and features that are used in predictions of decision making. The challenges in big data analytics are the high dimensionality and complexity in data representation. Granular computing and feature selection are among the challenge to deal with big data analytics that is used for Decision...
Existing clustering algorithms need to specify the number of clusters and to select initial points using human input, which lead to inferior clustering and optimisation outputs. Here, an improved grey decision-making model based on the thought of affinity propagation algorithm and grey correlation analysis is proposed to solve these problems. According to the panel data class and the inter-class candidate...
For the multi-index decision problem with uncertain information, this paper introduces the definition of interval distance of three-parameter interval grey number, proposes the relative degree of grey incidence based on interval distance of three-parameter interval grey number, constructs the grey incidence decision-making model with three-parameter interval grey number, measures the relative degree...
Spatial outlier detection in wireless sensor network (WSN) can detect the objects whose non-spatial attributes are significantly different from their spatial neighbors, so as to ensure the reliability and accuracy of sensor data before decision-making process. The main drawback of existing spatial outlier detection algorithms is high user-dependency, which is not suitable for dynamic WSN data. This...
Products arranged in the same zone usually have some spatial correlation, function relation and the impact of environmental stress, which usually causes coupling effect with other products in the same zone. This kind of coupling effect affects products' quality, and may influence the veracity and comprehensiveness of the quality analysis. To solve this issue, starting from changing the traditional...
In spite of the success of many commercial cloud service e-marketplaces, the search results from these platforms are usually presented as an unordered list of icons representing the services that best fit users' keyword-based queries. The drawback of such presentation mechanisms is that users are not able to immediately discriminate among the cloud services for easy decision making. A number of cloud...
This paper uses the data envelopment analysis method (DEA) to compare the efficiency of state-owned commercial banks, joint-stock commercial banks and urban commercial banks with 16 listed commercial banks in 2016, and points out the problems of commercial banks in China, and then put forward some suggestions.
Reasoning is commonly understood as a means to increase knowledge and take better decision. That is why the role of arguments has a positive impact in decisions making, as well as in crucial issues discussion. Particularly in selecting one or several alternatives, or to justify an already taken decision. However, much indication illustrates that reasoning often points to epistemic biasness and underprivileged...
The approaches for developing the mechanisms of effective decision making in sea container traffic management based on traffic volumes forecasting by the means of artificial neural networks is investigated. It was found out that the efficiency of the container transport depends on the ratio of import and export of goods which is non-linear. The mechanism of enhancing the effectiveness of the decisions,...
In this paper, we propose a variety of correlation coefficient measures for hesitant fuzzy sets (HFSs) and investigate their properties. Then, we define the concepts of correlation relation matrix, composition matrix and equivalent correlation relation matrix in the frame of HFSs. Furthermore, we propose a clustering method for HFSs. The method utilizes the correlation coefficient of HFSs to construct...
Supplier evaluation in tourism supply chain (TSC) is a typical multi-criteria group decision problem. An evaluation approach that makes use of 2-tupple linguistic model, quality function deployment (QFD), and fuzzy information aggregation is proposed. The establishment process of assessment criteria based on fuzzy linguistic term sets has been discussed, and house of quality (HOQ) based on 2-tuple...
Discrimination decisions are at the forefront of human cognition. For this reason, many different types of models aim to predict how they are made. In this research, we compared the discrimination capabilities of a Recurrent Associative Memory (RAM) with the predictions of an accumulator model to show that, although the discrimination processes of both model classes differs, both make similar predictions...
There are six main categories of breast cancer be existent. In this paper, we have taken the Type 1 carcinoma cancer to support the decision making. For this, a novel machine learning based cost optimization is applied to make an efficient decision from the samples. Moreover, we have applied our methodology on the real datasets to predict cancer with appropriate parameters using Pearson correlation...
Aiming at the problem of expert weighting in group decision making, an expert weighting method based on D-S evidence theory is studied. Three ways such as the distance, grey correlation and the combination of these two methods are used to measure the deviation degree of the experts' opinions on the scheme, construct mass function based on this, and uses the D-S synthesis rule to carry on the information...
This paper describes a research project conducted to correlate emotion to film rating classification using EEG signals measured by Emotiv device. Certain films contain inappropriate content that call for the need to classify their ratings. Every Television of Film Review Board rates a certain film based on their general criteria. Since emotion plays a huge role in decision making, this paper conducts...
This paper considers a network of agents generating correlated data according to a known kernel. The correlation structure might undergo a change at an unknown time instant, where the post-change kernel is not fully known. Moreover, due to the data processing and communication costs, only a subset of agents can be observed at any time instant. The objective is to detect the change-point with minimum...
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