Softmax Function Based Neutrosophic Aggregation Operators and Application in Multi-Attribute Decision Making Problem

Softmax function is a well-known generalization of the logistic function. It has been extensively used in various probabilistic classification methods such as softmax regression, linear discriminant analysis, naive bayes classifiers, and artificial neural networks. Inspired by the advantages of softmax function, we focused on this paper is to develop the softmax function based single valued neutrosophic aggregation operators. Then we have established some essential properties of aggregation operators based on softmax function with neutrosophic set. Additionally, we have defined a multi-attribute decision making method based on the proposed aggregation operators. Using this MADM method we exercised a realistic MADM problem with neutrosophic informations. Finally, we checked the validity and reliability of the proposed methods considering by one numerical illustration.

Application of Data Mining Techniques for the Investigation of Factors Affecting Transportation Enterprises

<p> Inland transportation, due to the importance of this business and competition between active organizations in this field, applying new technologies in management and making better decisions can be beneficial. We have presented three data mining techniques of clustering, association rules, and classification to investigate the factors affecting the cost and time of road and rail transportation. Using methods based on the K-means algorithm with comparing four clusterings, we have proposed Naive Bayes (probabilistic) classification to determine the total accuracy of transportation percent to 97.91%. Finally, classification tree algorithms such as Bayesian theory and random forest have been used, and the results and output rules have been compared. This article is comprehensive and new to use various effective parameters inland transportation. We will confirm its efficiency by using the criterion(5v)(which we will explain in its place) and then the results in the field. A larger one, called land transit, could be generalized between the two countries. In the end, we have discussed more in methodology and results</p>

Application of Data Mining Techniques for the Investigation of Factors Affecting Transportation Enterprises

<p> Inland transportation, due to the importance of this business and competition between active organizations in this field, applying new technologies in management and making better decisions can be beneficial. We have presented three data mining techniques of clustering, association rules, and classification to investigate the factors affecting the cost and time of road and rail transportation. Using methods based on the K-means algorithm with comparing four clusterings, we have proposed Naive Bayes (probabilistic) classification to determine the total accuracy of transportation percent to 97.91%. Finally, classification tree algorithms such as Bayesian theory and random forest have been used, and the results and output rules have been compared. This article is comprehensive and new to use various effective parameters inland transportation. We will confirm its efficiency by using the criterion(5v)(which we will explain in its place) and then the results in the field. A larger one, called land transit, could be generalized between the two countries. In the end, we have discussed more in methodology and results</p>

Road Emissions in London: Insights from Geographically Detailed Classification and Regression Modelling

Greenhouse gases and air pollutant emissions originating from road transport continues to rise in the UK, indicating a significant contribution to climate change and negative impacts on human health and ecosystems. However, emissions are usually estimated at aggregated levels, and on many occasions roads of minor importance are not taken into account, normally due to lack of traffic counts. This paper presents a methodology enabling estimation of air pollutants and CO2 for each street segment in the Greater London area. This is achieved by applying a hybrid probabilistic classification–regression approach on a set of variables believed to affect traffic volumes and utilizing emission factors. The output reveals pollution hot spots and the effects of open spaces in a spatially rich dataset. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels.