A classification model on tumor cancer disease based mutual information and firefly algorithm

Cancer is a globally recognized cause of death. A proper cancer analysis demands the classification of several types of tumor. Investigations into microarray gene expressions seem to be a successful platform for revising genetic diseases. Although the standard machine learning (ML) approaches have been efficient in the realization of significant genes and in the classification of new types of cancer cases, their medical and logical application has faced several drawbacks such as DNA microarray data analysis limitation, which includes an incredible number of features and the relatively small size of an instance. To achieve a reasonable and efficient DNA microarray dataset information, there is a need to extend the level of interpretability and forecast approach while maintaining a great level of precision. In this work, a novel way of cancer classification based on based gene expression profiles is presented. This method is a combination of both Firefly algorithm and Mutual Information Method. First, the features are used to select the features before using the Firefly algorithm for feature reduction. Finally, the Support Vector Machine is used to classify cancer into types. The performance of the proposed system was evaluated by using it to classify datasets from colon cancer; the results of the evaluation were compared with some recent approaches

Conformance Checking for Manufacturing Processes using Control-flow Perspective and Time Perspective

Department of Management EngineeringRecently, the amount of manufacturing data being collected has been increasing dramatically due to growing interests of convergence of manufacturing and IT. As such, it is possible to analyze the recorded manufacturing data for various purposes. One of the most important goals of manufacturing data analysis is to understand the current situation of manufacturing processes based on comparing actual and plan data. In order to execute such analysis, conformance checking, which is to check for deviations between models and logs, can be applied. However, existing conformance checking research mostly focuses on the control-flow perspective. Thus, it is hard to apply existing conformance checking methods in the manufacturing industry since other factors such as resources, machines, groups, deadlines, and processing time needs to be determined and considered as well. Therefore, this paper proposes a comprehensive conformance checking method using the control-flow perspective and time perspective and validates the proposed method by applying actual data extracted from a manufacturing company in Korea.ope