We define the Interval Neutrosophic Hesitant Fuzzy Choquet Integral (INHFCI) operator as a useful tool for multicriteria decision making (MCDM). The INHFCI operator generalizes both the interval neutrosophic hesitant fuzzy ordered weighted averaging operator and the interval neutrosophic hesitant f uzzy weighted averaging operator. A modified version of the score function to make comparison among Interval Neutrosophic Hesitant Fuzzy elements is proposed. We develop an approach for multicriteria decision making based on the interval neutrosophic hesitant fuzzy choquet integral operator that applies to our proposed score function. Finally the model is illustrated with the help of an example.
We propose an allocation rule that takes into account the importance of players and their links and characterizes it for a fixed network. Unlike previous rules, our characterization does not require component additivity. Next, we extend it to flexible networks a la Jackson (2005). Finally, we provide a comparison with other fixed (network Myerson and Position value) and flexible network (player and link based) allocation rules through a number of examples.
Aggregation functions are mostly used in decision-making situations that require information fusion in a meaningful manner. The main purpose of aggregation is to turn a group of input data into a single and comprehensive one. However, in real decision-making and system evaluation problems, the decision maker may exhibit only some amount of certainty in her decision inputs. In this study, we show how to aggregate these certainty degrees assigned to a group of inputs in an intuitive and reasonable manner. One of the interesting aspects of the problem is that the value aggregation is independent of their certainty degrees while the certainty aggregation essentially depends on both the input values and the value aggregation function. The construction of the aggregation function gives rise to a fuzzy measure that satisfies some very interesting properties. The technique presented here has wide range of applications.
Preferences-involved evaluation and decision making are the main research subjects in Yager’s decision theory. When the involved bipolar preferences are concerned with interval information, some induced weights allocation and aggregation methods should be reanalyzed and redesigned. This work considers the multi-criteria evaluation situation in which originally only the interval-valued absolute importance of each criterion is available. Firstly, based on interval-valued importance, upper bounds, lower bounds, and the mean points of each, we used the basic unit monotonic function-based bipolar preference weights allocation method four times to generate weight vectors. A comprehensive weighting mechanism is proposed after considering the normalization of the given absolute importance information. The bipolar optimism–pessimism preference-based weights allocation will also be applied according to the magnitudes of entries of any given interval input vector. A similar comprehensive weighting mechanism is still performed. With the obtained weight vector for criteria, we adopt the weighted ordered weighted averaging allocation on a convex poset to organically consider both two types of interval-inducing information and propose a further comprehensive weights allocation mechanism. The detailed comprehensive evaluation procedures with a numerical example for education are presented to show that the proposed models are feasible and useful in interval, multi-criteria, and bipolar preferences-involved decisional environments.
Automatic Text Summarization techniques in Assamese language is still in an immature stage compared to other Indian languages. In this study, we have reviewed some of the studies in other Indian languages for having a better understanding of the problems. Furthermore, we have also put in our efforts to understand keeping in view the opinion of Assamese linguists as how we could overcome the shortcomings of Assamese Text Summarization and provide an effective solution that could serve as fundamental concepts in this particular field. An extractive method for text summarization is proposed in this paper to evaluate the sentences of the input document based on the merging of semantic and statistical characteristics. It is applied considering the importance of sentence and diversity. Moreover, we have applied Synsets from Assamese WordNet to calculate the word frequency. Besides identifying and weighing the salience sentences, the concept of vector centrality and cosine similarity is used and the result is represented in graph representation. A threshold value to indicate the important sentences is applied. The effectiveness of our proposed method is demonstrated through a set of experiments conducted using ROUGE measure and the evaluation is showed in terms of Recall.
In this paper, we develop an algorithm for forming fuzzy coalitions by accumulating resources from a finite set of rational agents and allocating profits accordingly under the framework of a fuzzy cooperative game. The underlying assumption is that this process is dynamic in nature and is influenced by the players' satisfactions over both resource accumulation and profit allocations. Our model is based on situations, where possibly one or more players compromise on their resource assignment and payoff allocations in order to make a binding agreement with the others.
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