Attempting to obtain a classifier or a model from datasets could be a cumbersome task, specifically when using datasets of high dimensionality. The larger the amount of features the higher the complexity of the problem and the longer the time that is expended in generating the outcome (the classifier or the model). Feature selection has been proved as a good technique for choosing features that best describe the system under certain criteria or measure. There are several different approaches for feature selection, but to our knowledge, there are not many different approaches when feature selection is involved with imprecise data and genetic fuzzy systems. In this paper, a feature selection method based on the fuzzy mutual information is proposed. The outlined method is valid for classifying problems when expertise partitioning is given, and it represents the base of future work including the use of imprecise data.
- Inderscience Publishers
- A feature selection method using a fuzzy mutual information ...
Comparing RGB-based vegetation indices with NDVI for agricultural drone imagery
AbstractAgribotix conducted a post hoc test of two vegetation indices, VARI and TGI, that used only the three visible-band signals from an unmodified CMOS camera. The results were compared to NDVI, which is generally considered to be a reliable measure of field health and the underlying RGB image. While the results are somewhat encouraging in limited cases, it is clear that neither VARI nor TGI are applicable as general-purposes measures of field health.1. IntroductionA vegetation index is used to combine or...
Flood modeling and mitigation measures in an Urban Environment –Victoria, Australia - Case Study
AbstractAccurate prediction of flood extents in urban catchments is extremely difficult without having a 1d/2d integrated modeling approach. The integrated flood modeling is becoming popular and a new benchmark for urban flood risk management studies.City of Glen Eira in Victoria, Australia is a developed metropolitan municipality. The council is responsible for the management of 535km of drains servicing roads, properties and parks. Most of the drains were built before 1960, at a time when there were less...
Ferrous Wear Metal Measurement Made Easy with the FerroCheck
Historically, one of the most cost-effective indicators of machinery health has been the analysis of ferrous debris in oil. Such analysis utilizes the ferromagnetic properties of generated debris to perform the analysis. Since nearly all wear debris contains ferrous material (in particular iron), an abnormal increase in the amount of debris can be directly correlated to abnormal machinery conditions in the vast majority of cases. One type of ferrous debris analyzer is the magnetometer. The device works by sensing...
Multi-parameter based coagulant dosing control
The required coagulant dosage is strongly related to the quality of raw water or wastewater. Online sensors for most quality parameters are now readily available to treatment facilities, yet remain rarely used in treatment process control. This paper presents the evaluation of an advanced coagulant dosing control system based on online measurements in full-scale processes. The popular multivariate analytical method, partial least square regression, was used to build up the relationship between the coagulant dose...
LTM-2 Probes Provide Consistent Sump Level Measurement Resulting in Better Cyclone Performance
Mill’s have shown a significant interest in using LTM-2 probes for measuring secondary mill sump pulp levels due to their accuracy, fast response and not being affected by froth-foam or subject to erroneous signal bouncebacks. In fact over the last year the bulk of Zeroday’s probe sales have been in ferrous and base metal mill pebble and ball milling sump applications. Uniformly the experience has been that where LTM-2 probes have been installed, much more consistent level control has been obtained...