Rough Set-Game Theory Information Mining Model Considering Opponents’ Information

In multi-strategy games, the increase in the number of strategies makes it difficult to make a solution. To maintain the competition advantage and obtain maximal profits, one side of the game hopes to predict the opponent’s behavior. Building a model to predict an opponent’s behavior is helpful. In this paper, we propose a rough set-game theory model (RS-GT) considering uncertain information and the opponent’s decision rules. The uncertainty of strategies is obtained based on the rough set method, and an accurate solution is obtained based on game theory from the rough set-game theory model. The players obtain their competitors’ decision rules to predict the opponents’ behavior by mining the information from repeated games in the past. The players determine their strategy to obtain maximum profits by predicting the opponent’s actions, i.e., adopting a first-mover or second-mover strategy to build a favorable situation. The result suggests that the rough set-game theory model helps enterprises avoid unnecessary losses and allows them to obtain greater profits.

The Application of Thermal Decomposition for Determination of Carbonate Acid-Neutralising Capacity for Improved Acid Mine Drainage Prediction

This study developed an industry-applicable, thermal decomposition methodology for quantification of carbonate mineral acid neutralisation capacity (ANCtherm-carb) for waste rock, tailings, and other mined materials. Standard titration-based methods for ANC can be compromised due to contributions from silicate minerals, ion exchange, Fe-rich carbonates, and other transition metal carbonates. C emission (CO2 and CO) was measured using IR in a N2 atmosphere. Cneut (wt%) was calculated using the C emission at 800 or 1000 °C minus the C emission at 400, 450 or 500 °C and the weight of sample prior to decomposition (Equation (2) of this manuscript). This value was then input into Equation (3) of this manuscript to calculate ANCtherm-carb. Good correlation of ANCtherm-carb for single-mineral carbonates with ANCcalc, calculated from bulk assay concentrations for Mg, K, Na, Ca, and Mn, was achieved. Thereafter, 18 waste rock samples were examined, resulting in the correlation of ANCtherm-carb versus non-standard ANCtitrate-carb (titration methodology adapted to focus on carbonate neutralisation only) with R2 = 0.96. This correlation is valid for samples containing both non-neutralising carbonates (siderite) and sources of neutralisation arising from non-carbonates (Mg-clay) within this waste rock system. Typically, mining operations use total C measurements for assessment of carbonate neutralisation potential in the block and mining model. This method provides an effective means to cheaply analyse for carbonate neutralisation potential with assignment of potentially acid-forming and non-acid-forming blocks to waste rock cells, etc.

Fathers’ Predominance in Transport Arrangements for Family Tourism: E-Chaid-Based Profiling in East Asia

In decisions about transportation for family vacations, the distribution of the decision-making role between fathers and other family members is subject to characteristics of the society, the travel and the household. Therefore, the purpose of this study is to present a data-mining model that identifies the relative importance of those determining characteristics in predicting the probability of the father’s predominance in transportation decisions for family vacations. By investigating cases across four East Asian societies and using exhaustive chi-square automatic identification detector analysis, it was found that the primary source of the family’s income was the strongest predictor of the father-determined likelihood of decisions about vacation transportation. The results also suggested that the decision tree method is appropriate for targeting the father-predominant market of transportation in cross-societal contexts.

Are Machines-learning Methods More Efficient than Humans in Triaging Literature for Systematic Reviews?

Systematic literature reviews provide rigorous assessments of clinical, cost-effectiveness, and humanistic data. Accordingly, there is a growing trend worldwide among healthcare agencies and decision-makers to require them in order to make informed decisions. Because these reviews are labor-intensive and time consuming, we applied advanced analytic methods (AAM) to determine if machine learning methods could classify abstracts as well as humans. Literature searches were run for metastatic non-small cell lung cancer treatments (mNSCLC) and metastatic castration-resistant prostate cancer (mCRPC) . Records were reviewed by humans and two AAMs. AAM-1 involved a pre-trained data-mining model specialized in biomedical literature, and AAM-2 was based on support vector machine algorithms. The AAMs assigned an accept/reject status, with reasons for exclusion. Automatic results were compared to those of humans. For mNSCLC, 5820 records were processed by humans and 440 (8%) records were accepted and the remaining items rejected. AAM-1 correctly accepted 6% of records and correctly excluded 79%. AAM-2 correctly accepted 6% of records and correctly excluded 82%. The review was completed by AAM-1 or AAM-2 in 52 hours, compared to 196 hours for humans. Work saved was estimated to be 76% and 79% by AAM-1 and AAM-2, respectively. For mCRPC, 2434 records were processed by humans and 26% of these were accepted and 74% rejected. AAM-1 correctly accepted 23% of records and rejected 62%. AAM-2 correctly accepted 20% of records and rejected 66%. The review was completed by AAM-1, AAM-2, and humans in 25, 25 and 85 hours, respectively. Work saved was estimated to be 61% and 68% by AAM-1 and AAM-2, respectively. AAMs can markedly reduce the time required for searching and triaging records during a systematic review. Methods similar to AAMs should be assessed in future research for how consistent their performances are in SLRs of economic, epidemiological and humanistic evidence.

Construction and Simulation of a Multiattribute Training Data Mining Model for Basketball Players Based on Big Data

This paper provides an in-depth analysis and research on the construction and simulation of a big data model for multiattribute training of basketball players. To get a more accurate and three-dimensional information, the training can use a multitraining target robot, i.e., to detect feedback on multiple indicators at the same time and correct the player’s errors in time; the other is an auxiliary robot, which can actively correct technical movements and train the player to form muscle memory, compared with general training. The analysis results show that by either constructing a human model or designing an active assistive robot, the player’s technical movements can be regulated accordingly, protecting the player’s body laterally and improving the player’s ability. An assisted training system with an accurate model of physiological indicators is constructed based on the data of the player throughout the season. The Warriors, who have applied this system, not only have the best record in recent years but also have the lowest injury rate in the league, indicating that this method has indeed reduced the injury rate of players.