The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
Recent developments in artificial intelligence and machine learning are of great importance in supporting, identifying, and classifying Lung diseases, whether using medical images or using gene expression. That is why many researchers have worked to detect lung diseases using various methods of machine learning. This paper presents a survey of 20 papers using different methods. To screen for lung disease. And the goal From this paper present a classification of the latest lung diseases based on machine learning. Classification consists of a number of features common to the surveys are: Types of data used, types of lung diseases, types of machine learning algorithms used, and this classification is of great importance and can be used by many researchers to plan their contributions and research activities in many fields. It is also important in terms of improving the efficiency and accuracy of machine learning in examining and classifying lung diseases with the least possible error.
In this wok, a novel approach based on ordinary Petri net is used to generate private key . The reachability marking of petri net is used as encryption/decryption key to provide more complex key . The same ordinary Petri Nets models are used for the sender(encryption) and the receiver(decryption).The plaintext has been permutated using look-up table ,and XOR-ed with key to generate cipher text
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