Judo mixed team event match outcome and the Judo World Ranking List

The Judo World Ranking List has been used to assess its predictive power regarding athletic performance. The aim of this study was to check if the ranking difference among athletes in the individual rankings could be used as a predictor for the outcome of mixed teams judo competition. We sought to verify the association between the variable “rank difference” and the probability of the higher-ranked athlete winning against that lower-ranked. This heterogeneity between athletes is a major factor in increasing or decreasing the team’s chances of winning. The higher ranked athlete’s defeat seems to have a significant negative effect on his/her teammates in subsequent bouts. These findings imply that future studies on mixed teams judo competitions should consider individual ranking differences among athletes as a predictor of performance.

Application of Calculation of Ranking Funding Officer at the Bank(Aplikasi Perhitungan Rangking Funding Officer Pada Bank)

Bank is a business entity that collects funds from the public in the form of deposits and distributes them to the public in the form of credit and or other forms. To raise funds, bank officers are known as funding officers who have the main task of collecting funds in the form of savings, current accounts and deposits. Generally banks already have monitoring tools, dashboards and reports on the achievement of funds, but these tools do not yet present a ranking for funding officers. One of the things needed to support the improvement of funding officer performance is the making of individual ranking, for that purpose an application for ranking calculation is made using the PHP programming language whose data is sourced from existing monitoring tools, it is hoped that this application can help the relevant part to accelerate presentation of individual ranking of funding officers.

Model-Based Learning from Preference Data

Preference data occur when assessors express comparative opinions about a set of items, by rating, ranking, pair comparing, liking, or clicking. The purpose of preference learning is to ( a) infer on the shared consensus preference of a group of users, sometimes called rank aggregation, or ( b) estimate for each user her individual ranking of the items, when the user indicates only incomplete preferences; the latter is an important part of recommender systems. We provide an overview of probabilistic approaches to preference learning, including the Mallows, Plackett–Luce, and Bradley–Terry models and collaborative filtering, and some of their variations. We illustrate, compare, and discuss the use of these methods by means of an experiment in which assessors rank potatoes, and with a simulation. The purpose of this article is not to recommend the use of one best method but to present a palette of different possibilities for different questions and different types of data.

Application of case-based reasoning and multi-criteria decision-making methods for material selection in petrochemistry

This paper describes a decision support approach for selecting construction materials in the field of petrochemistry. The correct selection of construction materials is the basis for the provision of durability, long lifetime, and the safety of the designed and upgraded equipment. The proposed hybrid approach is based on the joint application of case-based reasoning and multi-criteria decision-making methods, particularly the aggregation and ranking alternatives nearby the multi-attribute ideal situation (ARAMIS) and aggregation of individual ranking/ complex of aggregation of individual ranking (AIR/CAIR) methods are used. In turn, ARAMIS enables the processing of individual preferences, which are represented in the form of numeric and verbal estimates, and AIR/CAIR is used for processing rankings (strictly or partially ordered sets of alternatives). The primary advantage of this approach is that it considers the experience of previous successful solutions of the materials selection problem, demonstrates the validity of the obtained results using the mathematical theory of multi-criteria decision making and explains the decision making process. The approach is implemented in the form of the expert system. The case model, algorithms, and an illustrative example of application of the proposed approach are also presented.