Identifying Lung Cancer Using CT Scan Images Based On Artificial Intelligence

Lung cancer appears to be the common reason behind the death of human beings at some stage on the planet. Early detection of lung cancers can growth the possibility of survival amongst human beings. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected in time. Although computerized tomography (CT) is frequently more efficient than X-ray. However, the problem regarded to merge way to time constraints in detecting this lung cancer concerning the numerous diagnosing strategies used. Hence, a lung cancer detection system that usage of image processing is hired to categorize lung cancer in CT images. In image processing procedures, procedures like image pre-processing, segmentation, and have extraction are mentioned intimately. This paper is pointing to set off the extra precise comes approximately through making use of distinctive improve and department procedures. In this proposal paper, the proposed method is built in some filter and segmentation that pre-process the data and classify the trained data. After the classification and trained WONN-MLB method is used to reduce the time complexity of finding result. Therefore, our research goal is to get the maximum result of lung cancer detection

Combining heterogeneous classifiers via granular prototypes.

In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e., Decision Template and Two Stages Ensemble System, AdaBoost, Random Forest, L2-loss Linear Support Vector Machine, and Decision Tree

Designing multiple classifier combinations a survey

Classification accuracy can be improved through multiple classifier approach. It has been proven that multiple classifier combinations can successfully obtain better classification accuracy than using a single classifier. There are two main problems in designing a multiple classifier combination which are determining the classifier ensemble and combiner construction. This paper reviews approaches in constructing the classifier ensemble and combiner. For each approach, methods have been reviewed and their advantages and disadvantages have been highlighted. A random strategy and majority voting are the most commonly used to construct the ensemble and combiner, respectively. The results presented in this review are expected to be a road map in designing multiple classifier combinations

Artificial Intelligence and Industry 4.0

Cílem této práce je poskytnout přehled aplikací metod umělé inteligence v kontextu průmyslu 4.0. První kapitola je věnována definici konceptu průmyslu 4.0, předchozímu vývoji průmyslu a zařazení vědního oboru umělé inteligence do tohoto konceptu. Druhá kapitola je zaměřena na rešerši aplikací metod umělé inteligence v oblasti obrábění, výrobního průmyslu, automatizace a energetiky. Závěr práce je věnován zhodnocení metod, jejich výhod a úskalí z pohledu jednotlivých praktických aplikací a zmiňuje možné směry budoucího vývoje.The aim of this work is to provide an overview of the application of artificial intelligence methods in the context of Industry 4.0. The first chapter defines the concept of industry 4.0, previous development of the industry and inclusion of the scientific field of artificial intelligence in this concept. The second chapter is focused on the applications of artificial intelligence methods in the field of machining, manufacturing industry, automation and energetics. The work concludes with evaluation of methods, their advantages and disadvantages from the point of view of individual practical applications and mentions possible directions of future development.