Problem statement: Over the last two decades, Fault Diagnosis (FD) has a major importance to enhance the quality of manufacturing and to lessen the cost of product testing. Actually, quick and correct FD system helps to keep away from product quality problems and facilitates precautionary maintenance. FD may be considered as a pattern recognition problem. It has been gaining more and more attention to develop methods for improving the accuracy and efficiency of pattern recognition. Many computational tools and algorithms that have been recently developed could be used. Approach: This study evaluates the performances of three of the popular and effective data mining models to diagnose seven commonly occurring faults of the steel plate namely; Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps and Other_Faults. The models include C5.0 decision tree (C5.0 DT) with boosting, Multi Perception Neural Network (MLPNN) with pruning and Logistic Regression (LR) with step forward. The steel plates fault dataset investigated in this study is taken from the University of California at Irvine (UCI) machine learning repository. Results: Given a training set of such patterns, the individual model learned how to differentiate a new case in the domain. The diagnosis performances of the proposed models are presented using statistical accuracy, specificity and sensitivity. The diagnostic accuracy of the C5.0 decision tree with boosting algorithm has achieved a remarkable performance with 97.25 and 98.09% accuracy on training and test subset. C5.0 has outperformed the other two models. Conclusion: Experimental results showed that data mining algorithms in general and decision trees in particular have the great impact of on the problem of steel plates fault diagnosis.

Problem statement:In earlier days, each and every individual system has particular IDS to the particular system and due to this particular technique there are many drawbacks and much more drawbacks in the system side networks. "All processes used in discovery of unauthorized uses of network or computer devices" Detection of unusual and abnormal activity/events in real-time. Detects break-ins or attacks through various data sources from logs/audit/surveillance and network traffic. Approach: The Intrusion Detection System (IDS) has an objective to identify individuals that try to use a system in a way not authorized or those that have authorization to use but they abuse of their privileges. This study proposing the Dynamic Distributed Intrusion Detection System (DDIDS) to improve the system Processing and system Networking. Results: An implementation result of the network plays a very important role in order to connect each and every system through a network. For that reason it is said with the experiment that the enhanced intrusion detection system based on Agent gain highly developed detecting performance with fault tolerance. Conclusion: The main aim of this study is to design and develop the dynamic distributed intrusion detection system that would be accurate, low in false alarms, not easily cheated by small variations in pattern, adaptive and be of real time and also increase the system efficiency and increase the system network efficiency.