Multi-stage consistency optimization algorithm for decision making with incomplete probabilistic linguistic preference relation

For many companies, green product development has become a key strategic consideration due to regulatory requirements and market trends. In this paper, the life cycle assessment technique is used to develop an innovative multi-criteria group decision-making approach that incorporates power aggregation operators and a TOPSIS-based QUALIFLEX method in order to solve green product design selection problems using neutrosophic linguistic information.

/pdf/simplified-neutrosophic-linguistic-multi-criteria-group-25dldewj2q.pdf

Simplified Neutrosophic Linguistic Multi-criteria Group Decision-Making Approach to Green Product Development

A dynamic hybrid trust network-based dual-path feedback consensus model for multi-attribute group decision-making in intuitionistic fuzzy environment

In this paper, the concept of generalized neutrosophic pre-closed sets and generalized neutrosophic pre-open sets are introduced. We also study relations and various properties between the other existing neutrosophic open and closed sets. In addition, we discuss some applications of generalized neutrosophic pre-closed sets, namely neutrosophic pT_{1/2} space and neutrosophic gpT_{1/2} space. The concepts of generalized neutrosophic connected spaces, generalized neutrosophic compact spaces and generalized neutrosophic extremally disconnected spaces are established. Some interesting properties are investigated in addition to giving some examples.

/pdf/on-generalized-closed-sets-and-generalized-pre-closed-sets-mpia9qwaw0.pdf

On Generalized Closed Sets and Generalized Pre-Closed Sets in Neutrosophic Topological Spaces

The aim of the paper is to solve the group decision making problems which contain inconsistent probabilistic linguistic preference relations (PLPRs) and unknown expert weights When the PLPRs are inconsistent, there are contradictories in the preference relations expressed by the experts The evaluation value with contradictory information will bring out an incorrect consequence in decision making Hence, this paper develops a novel consistency measure method to gauge the consistency level of PLPRs Moreover, a nonlinear optimization model is newly constructed to optimize the inconsistent PLPRs The proposed methods overcome the limitations in the existing methods and ameliorate the interpretation and complexities of inconsistency PLPRs revise strategies Additionally, a weighting method using fuzzy cooperative games with PLPRs is put forward to derive the weight vector of experts It helps to balance the deviations between the individual PLPRs and the group PLRP At last, a numerical example illustration for physician selection is put forward to demonstrate the effectiveness of the proposed model and its practical applicability The comparative analysis gives deep insights into the rationality of the proposed model

A group decision making with probability linguistic preference relations based on nonlinear optimization model and fuzzy cooperative games

Wart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.

/pdf/applying-particle-swarm-optimization-based-decision-tree-18f6n6keu6.pdf

Applying particle swarm optimization-based decision tree classifier for wart treatment selection

Malaria is one of the three major diseases with the highest mortality worldwide and can turn fatal if not taken seriously. The key to surviving this disease is its early diagnosis. However, manual diagnosis is time consuming and tedious due to the large amount of image data. Generally, computer-aided diagnosis can effectively improve doctors’ perception and accuracy. This paper presents a medical diagnosis method powered by convolutional neural network (CNN) to extract features from images and improve early detection of malaria. The image sharpening and histogram equalization method are used aiming at enlarging the difference between parasitized regions and other area. Dropout technology is employed in every convolutional layer to reduce overfitting in the network, which is proved to be effective. The proposed CNN model achieves a significant performance with the best classification accuracy of 99.98%. Moreover, this paper compares the proposed model with the pretrained CNNs and other traditional algorithms. The results indicate the proposed model can achieve state-of-the-art performance from multiple metrics. In general, the novelty of this work is the reduction of the CNN structure to only five layers, thereby greatly reducing the running time and the number of parameters, which is demonstrated in the experiments. Furthermore, the proposed model can assist clinicians to accurately diagnose the malaria disease.

Bo Li

Papers

A group decision making with probability linguistic preference relations based on nonlinear optimization model and fuzzy cooperative games

Applying particle swarm optimization-based decision tree classifier for wart treatment selection

A novel method based on convolutional neural network for malaria diagnosis