Lifetime Distribution With a Limited Failure in Condition Based Maintenance: A Case Study

Today, the value of condition monitoring information recognised by most of plant personnel. The main idea is that, the observed information can act as maintenance indicators, which could be used to describe the key relationship between equipment condition and a maintenance decision. The deteriorating of equipment condition can be modelled using indicator such as cumulative wear and residual time, (Hussin 2007). If the condition or the state of the equipment can be predicted, maintenance actions which include manpower, equipment and tools and spare parts can be planned and scheduled, (Duffua, 1997)

Integrated Features by Administering the Support Vector Machine (SVM) of Translational Initiations Sites in Alternative Polymorphic Contex

Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM) is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM) is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts

Modelling and Evaluating Software Project Risks with Quantitative Analysis Techniques in Planning Software Development

Risk is not always avoidable, but it is controllable. The aim of this paper is to present new techniques which use the stepwise regression analysis tomodel and evaluate the risks in planning software development and reducing risk with software process improvement. Top ten software risk factors in planning software development phase and thirty control factors were presented to respondents. This study incorporates risk management approach and planning software development to mitigate software project failure. Performed techniques used stepwise regression analysis models to compare the controls to each of the risk planning software development factors, in order to determine and evaluate if they are effective in mitigating the occurrence of each risk planning factor and, finally, to select the optimal model. Also, top ten risk planning software development factors were mitigated by using control factors. The study has been conducted on a group of software project managers. Successful project risk management will greatly improve the probability of project success.</p

Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization

Nowadays, online social media is online discourse where people contribute to create content, share it, bookmark it, and network at an impressive rate. The faster message and ease of use in social media today is Twitter. The messages on Twitter include reviews and opinions on certain topics such as movie, book, product, politic, and so on. Based on this condition, this research attempts to use the messages of twitter to review a movie by using opinion mining or sentiment analysis. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the movie is good or not based on message opinion. Support Vector Machine (SVM) is supervised learning methods that analyze data and recognize the patterns that are used for classification. This research concerns on binary classification which is classified into two classes. Those classes are positive and negative. The positive class shows good message opinion; otherwise the negative class shows the bad message opinion of certain movies. This justification is based on the accuracy level of SVM with the validation process uses 10-Fold cross validation and confusion matrix. The hybrid Partical Swarm Optimization (PSO) is used to improve the election of best parameter in order to solve the dual optimization problem. The result shows the improvement of accuracy level from 71.87% to 77%

Cascade Quality Prediction Method Using Multiple PCA+ID3 for Multi-Stage Manufacturing System

AbstractQuality prediction model, as the key to realize the real-time online quality monitoring process, has been developed using various data mining techniques. However, most of quality prediction models are developed in single-stage manufacturing system, where the relationship between manufacturing operation and quality variables is straightforward. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing system due to the complex variable relationships. This study is intended to propose a data mining method to develop quality prediction model which is able to deal with the complex variable relationships in multi-stage manufacturing system. This method, named Cascade Quality Prediction Method (CQPM), is developed by considering the complex variables relationships in multi-stage manufacturing system. CQPM employs the combination of multiple Principal Component Analysis and Iterative Dichotomiser 3 algorithm. A case study in semiconductor manufacturing shows that the prediction model that has been developed using CQPM is performed better in predicting both positive and negative classes compared to others

Managing Software Project Risks (Design Phase) with Proposed Fuzzy Regression Analysis Techniques With Fuzzy Concepts

Abstract - This Regardless how much effort we put for the success of sofnvare projects, many sofnvare projects have very high failure rate. Risk is not always avoidable, but it is controllable. The aim of this paper is to present the new mining technique that uses the fuzzy multiple regression analysis techniques with fuzzy concepts to managing the risks in a software project and reducing risk with sofnvare process improvement. Top ten sofnvare risk factors in design phase and thirty risk management techniques were presented to respondents. Tire results show that alf risks in sofnvare projects were important in sofnvare project manager perspective. whereas all risk management techniques are used most of time. and often. However, these mining tests were perfonned using fuzzy multiple regression analysis techniques to compare tire risk management techniques to each of the software risk factors to determine if they are effective in mitigating the occurrence of each sofnvare risk factor by usjng statistical package for the Social Science (SPSS) for Manipulating and analyzing tire data set, MAT LAB 7.12.0 (R20 11 a), wolfram mathematic 9. 0,. Also ten top software risk factors were mitigated by using risk management techniques except Risk 3 "Developing the Wrong User Interface". We referred the risk management techniques were mitigated on sofnvare risk factors in Table XV. The study has been conducted on a group of software project managers. Successful project risk management will greatly improve th

Managing Software Project Risks (Analysis Phase) with Proposed Fuzzy Regression Analysis Modelling Techniques with Fuzzy Concepts

The aim of this paper is to propose new mining techniques by which we can study the impact of different risk management techniques and different software risk factors on software analysis development projects. The new mining technique uses the fuzzy multiple regression analysis techniques with fuzzy concepts to manage the software risks in a software project and mitigating risk with software process improvement. Top ten software risk factors in analysis phase and thirty risk management techniques were presented to respondents. The results show that all software risks in software projects were very important from software project manager perspective, whereas all risk management techniques are used most of the time, and often. However, these mining tests were performed using fuzzy multiple regression analysis techniques to compare the risk management techniques with each of the software risk factors to determine if they are effective in reducing the occurrence of each software risk factor. The study has been conducted on a group of software project managers. Successful software project risk management will greatly improve the probability of software project success

Practical and user friendly tool of analytic hierarchy process for decision making

This paper discusses on the use of analytic hierarchy process (AHP) aiming at improving and enhancing the decision making process.Currently, the decision provided by user is referring to their opinion and experience.If there is a supported tool, normally mean for expert users or researchers.By using a practical and user friendly AHP tool, many users are benefited from the tool. There are three basic features of AHP called criteria, sub-criteria and alternative.These features consist of a combination of users experience and mathematical approach. The method aim to give users a decision making process according to the given problem. The results will suggest users on what is the best decision should be made.In order to test its applicability, a real world case study at Palm Oil Mill (POM) plant is used.A satisfactory result has confirmed the practicality and user friendliness of the tool