Enhanced maintenance problem recognition techniques and its application to palm oil mills

This paper discusses the application of enhanced maintenance problem recognition techniques. The main contribution of this study is the proposed combined techniques, namely snapshot model, failure mode, effect and criticality analysis (FMECA),Pareto analysis, and decision analysis by using information technology (IT). The snapshot model is part of the maintenance modelling technique while FMECA, Pareto analysis, and decision analysis are part of maintenance reliability techniques.Each of the techniques and the proposed combined techniques is explained. The case study used for this enhanced technique was the palm oil mills maintenance problem. The result and possible further enhancement is also discussed

Enhance maintenance problem recognition techniques and its application to palm oil mills

This paper discusses the application of enhanced maintenance problem recognition techniques. The main contribution of this study is the proposed combined techniques, namely snapshot model, failure mode, effect and criticality analysis (FMECA), Pareto analysis, and decision analysis by using information technology (IT). The snapshot model is part of the maintenance modelling technique while FMECA, Pareto analysis, and decision analysis are part of maintenance reliability techniques. Each of the techniques and the proposed combined techniques is explained. The case study used for this enhanced technique was the palm oil mills maintenance problem. The result and possible further enhancement is also discussed

Named Entity Recognition using Fuzzy C-Means Clustering Method for Malay Textual Data Analysis

The Named Entity Recognition (NER) task is among the important tasks in analysing unstructured textual data as a solution to gain important and valuable information from the text document. This task is very useful in Natural Language Processing (NLP) to analyse various languages with distinctive styles of writing, characteristics and word structures. The social media act as the primary source where most information and unstructured textual data are obtained through these sources. In this paper, unstructured textual data were analysed through NER task focusing on the Malay language. The analysis was implemented to investigate the impact of text features transformation set used for recognising entities from unstructured Malay textual data using fuzzy c-means method. It focuses on using Bernama Malay news as a dataset through several steps for the experiment namely pre-processing, text features transformation, experimental and evaluation steps. As a conclusion, the overall percentage accuracy gave markedly good results based on clustering matching with 98.57%. This accuracy was derived from the precision and recall evaluation of both classes. The precision result for NON_ENTITY class is 98.39% with 100.00% recall, whereas for an ENTITY class, the precision and recall are 100.00% and 88.97%, respectively

A SMS-Based Intelligent Disaster Alert System

A SMS-Based Intelligent Disaster Alert System (IDAS) is an expert system in helping geologist to predict disaster incidences. The disaster includes flood, earthquake, hurricane, drought and tsunami. If disaster is predicted, an alert based on possible disaster area will be sent to the residents via mobile device i.e. Short Messaging System (SMS). The system is developed by utilizing Artificial Intelligence (AI) techniques of Rule-Based, Decision Tree Analysis and Guided Rules Reduction System. A Microsoft Visual Studio.Net and MySQL database are used as the software development environment while SMS technology is based on Global System for Mobile Communications (GSM) connected to IDAS. The case study was done at the area of Melaka Tengah, Melaka. A resident’s information is stored in the database in order to send alert via SMS. Once disaster is predicted, SMS will be sent to their respective mobile phone