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This paper presents a model and methodology for estimating the residual time of a plant item. This plant item can be an engine or any complex technical system monitored by a regularly spaced oil analysis programme. Typically in the oil samples taken, two groups of observed variables are available, namely, metal concentrations and variables related to the condition of the lubricant and contaminants. We term the former as internal variables and the latter as external variables. External variables are those that may cause the change of the underlying condition of the plant item and therefore the residual time, while internal variables are those variables that only reflect the residual time but cannot change it. We modelled both variables in an oil-based monitoring case, but the principle can be generalized to other monitoring situations. The main techniques used are stochastic filtering for residual time prediction and the maximum likelihood method for parameters estimation. The model established was fitted to the real data of marine diesel engines monitored by an oil analysis programme and the results are presented.

The aim of this study is to propose the discriminant analysis (DA)

Quality prediction model has been developed in various industries to realize the faultless manufacturing. However, most of quality prediction model is developed in single-stage manufacturing. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing effectively. This study is intended to propose combination of multiple PCA+ID3 algorithm to develop quality prediction model in MMS. This technique is applied to a semiconductor manufacturing dataset using the cascade prediction approach. The result shows that the combination of multiple PCA+ID3 is manage to produce the more accurate prediction model in term of classifying both positive and negative classes. General TermsData Mining, Prediction Model.

Regardless of how much effort we put for the success of software projects, many software projects have very high failure rate. Risk is not always avoidable, but it is controllable on software development projects. The aim of this paper is to present new mining technique that uses the fuzzy regression analysis modelling techniques to manage the risks in a software development project and to reduce risk with software process improvement. Top ten software risk factors in planning phase and thirty risk management techniques were presented to respondents. The results showed that all risks in software projects were important in software project manager perspective, whereas all risk management techniques are used most of time, and often. However, these mining tests were performed using fuzzy multiple regression analysis techniques to compare the risk management techniques to each of the software risk factors to determine if they are effective in mitigating the occurrence of each software risk factor. Also ten top software risk factors ( planning phase) were mitigated by using risk management techniques. The risk management techniques were mitigated on software risk factors in Table 15. The study has been conducted on a group of software project managers. Successful software project risk management will greatly improve the probability of project success.

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One of the significant threats that faces the web nowadays is the DNS tunneling which is an attack that exploit the domain name protocol in order to bypass security gateways. This would lead to lose critical information which is a disastrous situation for many organizations. Recently, researchers have pay more attention in the machine learning techniques regarding the process of DNS tunneling. Machine learning is significantly impacted by the utilized features. However, the lack of benchmarking standard dataset for DNS tunneling, researchers have captured the features of DNS tunneling using different techniques. This paper aims to present a review on the features used for the DNS tunneling.