AbstractAs the growing number of cases is draining the limited court resources in China, how to scientifically measure the reasonable saturated workload of judges has become an urgent issue. This issue is the prerequisite of other important topics such as determination of judges’ quotas, measurement of the actual workload of a trial team, performance evaluation of judges, and resource allocation within courts. Data-driven measurement of the actual workload of China’s judges depends on various factors such as local economic development, public transportation, case-load in the past, and staffing of assistant positions. Therefore, traditional approaches that depend only on a single element, such as cause of action, do not work well. We proposed a modelling framework based on big-data and machine-learning technology to more accurately measure the actual workload of judges. This framework extracts the core elements of judicial cases, assigns target workload to the cases based on feedback from judges and analyzing case samples to create a standard training dataset, and trains machine-learning models using the data. A preliminary case-weight calculation model is built using the framework. Besides, the model is continuously evaluated and improved by comparing its output with the actual demand in a court through methods such as sampling, questionnaires, and expert evaluation.
Machine learning based nominal root stress calculation model for gears with a progressive curved path of contact
