Description:
Predictive Maintenance Management in an industry can play a pivotal role in asset management and revenue generation. This work proposes a data-driven-based multi classifier model for implementing predictive maintenance to simultaneously reduce the downtime and idle time of the machines in a manufacturing plant. A case study of the plant comprising of 100 machines has been done to identify the early prediction of failure, its nature, and the attributing cause. Gradient Boosting Tree Classifier and Random Forest Classifier machine learning algorithms have been used to develop the models for fault prediction. A comparative analysis of results obtained using these methods has also been done. Random Forest Classifier outperforms Gradient Boost tree classifier in all evaluation parameters - accuracy, precision and recall.
Description:
Advances in machine intelligence have provided a whole new window of opportunities in medical research. Building a fully automated computer aided diagnostic system for digital mammograms is just one of them. Given some success with semi-automated systems earlier, a fully automated CAD system is just another step forward. A proper combination of a feature selection model and a classifier for those areas of a mammogram marked by radiologists has been very successful. However a fully automated system with only two modules is a time consuming process as the suspicious areas in a mammogram can be quite small when compared to the whole image. Thus an additional clustering process can help in reducing the time complexity of the overall process. In this paper we propose a fast clustering process to identify suspicious areas. Another novelty of this paper is a multi-category feature selection approach. The choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and the required number of samples. In this paper we propose a hybrid canonical based feature extraction technique as a combination of an evolutionary algorithm based classifier with a feed forward MLP model.
Description:
The main objective of this research is to investigate and implement a robust approach with a view to provide the Law Enforcement Agencies (LEAs) with a dedicated forensic tool in future for inspecting confiscated PCs from the suspected paedophile to detect pedophilic images automatically and prevent children viewing pornographic and age-inappropriate images at their home and school and adults at their workplace while they are on the Internet. To achieve this goal, we use a novel face descriptor to differentiate child face from adult face based on categorical age specific contextual cues that are based on new knowledge in terms of features or contexts representatives of child and adult face. Given that the craniofacial cues contain enough structural information on visual cues on human face encoded in the form of high level features we can categorize age into adult and children in tandem with low level features. Finally, we will present a novel stochastic vision model based on Markov Random Fields (MRF) prior, which learned the pornographic contextual constraints from the training pornographic images and eventually introduce knowledge on pornography into our proposed stochastic classifier allowing classification of images into pornographic or benign.