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Purpose Due to several unique characteristics, such as high tensile strength, low extensibility, high frictional resistance, biodegradability, eco-friendliness and cheapness, Jute ranks second just after cotton with respect to its worldwide consumption and production. To overcome the difficulties of the existing Jute grading procedure, this paper aims to focus on the application of decision-making trial and evaluation laboratory (DEMATEL) and multi-attributive border approximation area comparison (MABAC) methods for evaluation of 10 Tossa Jute fiber lots based on strength, defects, root content, color, fineness and bulk density properties. Design/methodology/approach The DEMATEL method divides all the six physical properties of Jute fiber into cause and effect groups. The most influencing property is also identified. On the other hand, the considered Jute fiber lots are ranked using MABAC method along with the identification of the strengths and weaknesses of each of them. Findings This combined approach would provide a more scientific and realistic way of Jute grading and evaluation based on various properties of the considered Jute fiber lots. The positions of the superior and the inferior Jute lots perfectly match with those as identified by the earlier researchers. Originality/value It is concluded that the adopted combined decision-making tool can be effectively applied for grading and evaluation of other natural fibers with diverse heterogeneous physical properties.

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A prior knowledge regarding the effectiveness of each of the medicines prescribed by a physician would be quite helpful to a patient for rapid recovery from a particular disease. In this paper, an attempt is put forward to develop the related association rules for understanding the roles of different types of medicines prescribed for treatment of dental diseases, especially tooth pain (odontalgia/dentalgia) and swelling of tooth (pericoronitis). 75 patient cases from a dentist are analyzed to determine the average number of different types of medicines prescribed, average number of medicines and average cost of treatment, and to mine the corresponding association rules. It is observed from 1-item dataset that antibiotic#1 is the most preferred medicine, followed by antiseptic. Similarly, the 2-item dataset shows that the most preferred combination on medicines is {antibiotic#1, antiseptic}, followed by {antibiotic#1, anti-reflux}. Among all the association rules developed, the rule (If antibiotic#1 and antibiotic#2 and antiseptic, then anti-reflux) appears with the maximum strength.