Fuzzy multi criteria evaluation for performance of bus companies

A multi criteria decision making in ranking the bus companies using fuzzy rule is proposed. The proposed method uses the application of fuzzy sets and approximate reasoning in deciding the ranking of the performance of several bus companies. The proposed method introduces data normalization using similarity function which dampens extreme values that exist in the data. The use of the model is suitable in evaluating situation that involves subjectivity, vagueness and imprecise information. Experimental results are comparable to several previous methods

Fuzzy subjective evaluation of Asia Pacific airport services

This paper presents a fuzzy decision-making model to determine the ranking of fourteen Asia Pacific airports based on the services provided to passengers. Airport services were represented by six attributes namely comfort, processing time, convenience, courtesy of staff, information visibility and security. Data for the attributes given by travel experts are in the triangular fuzzy number form. Based on fuzzy set and approximate reasoning, the model allows decision makers to make the best choice in accordance with human thinking and reasoning processes.The use of fuzzy rules which are extracted directly from the input data in making evaluation, contributes to a better decision and is less dependent on experts.Experimental results show that the proposed model is comparable to previous studies.The model is suitable for various fuzzy environments

A Framework of Subjective Performance Evaluation Using Fuzzy Technique

This research proposes the framework of subjective performance evaluation using fuzzy technique for ranking the attributes of different types of datasets under a multi-criteria environment. Some previous studies on fuzzy techniques have been attempted in assessment and evaluation methods. The techniques such as fuzzy similarity function, fuzzy synthetic decision and satisfaction function have been adopted in these fuzzy evaluation methods. However, research that discover a scaling measurement which can express the subjectivity element and integrate the organisation‘s objectives and goals into the evaluation processes by utilising the fuzzy rule in the subjective evaluation method seem limited. Hence, this framework uses the application of fuzzy sets, and approximate reasoning to determine the performance evaluation of various characteristics in decision-making. The framework based upon fuzzy sets has initiated the idea of membership set score valued evaluation of each criterion alternative enables to include requirements which are incomplete and imprecise. The approximate reasoning of the method allows decision maker to make the best choice in accordance of human thinking and reasoning processes. The method introduces an approach of normalising data using similarity function which dampens the extreme value that exists in the data. The framework is suitable for dealing with evaluations in situations that involve subjectivity, vagueness and imprecise information, such as the grading system of evaluation which involves subjectivity, vagueness and imprecise information, such as the grading system evaluation which involves many hedges like "good", "bad" and "satisfactory"

The effect of network’s size on the performance of the gateway discovery and selection scheme for MANEMO

In the era of Internet technology, new applications are developed everyday requiring continuous and seamless connections. This urges for access availability solutions to the new scenarios. One of the critical architecture is the Mobile Ad-Hoc Network Mobility (MANEMO). However, the integration of Ad-hoc and NEMO technologies came out with many complications like redundant tunnels and the existence of multiple Exit Routers. This paper presents a scheme to discover and select the optimum gateway to improve the robustness and the performance of the network irrespective of the used routing protocol. The MANEMO Gateway discovery and selection scheme (MGDSS) extends the Tree Discovery Protocol and the Neighborhood Discovery protocol used by NEMO and Ad-Hoc to carry the necessary gateway selection parameters. To compare the effect of network’s size on the performance of the proposed scheme, the standard NEMO BSP and the Multi-homed MANEMO (M-MANEMO) approaches OPNET Modeler 14.5 was used. The results show that the average data packets dropped, the end-to-end delay and the throughput of the proposed MGDSS outperform those for the standard M-MANEMO and standard NEMO BSP. Keywords: Gateway Selection, Mobile Ad Hoc NEMO, MANEMO, Network Mobility, MANE

Modelling of wireless sensor networks for detection land and forest fire hotspot

Indonesia located in South East Asia countries with tropical region, forest fires in Indonesia is one of big issue and disaster because it happens in almost of every year, this is because of some of region consist of peat land that high risk for fire especially in dry season. Riau Province is one of region that regularly incident of forest fire with affected the length and breadth of Indonesia. Propose development of Wireless Sensor Networks (WSNs) for detection of land and forest fire hotspot in Indonesia as well as one of the main consents in this research, case location in Riau province is at one of the regions that high risk forest fire in dry season. WSNs technology used for ground sensor system to collect environmental data. Data training for fire hotspot detection is done in data center to determine and conclude of fire hotspot then potential to become big fire. The deployment of sensors located at several locations that has potential for fire incident, especially as data shown in previous case and forecast location with potential fire happen. Mathematical analysis is used in this case for modelling number of sensors required to deploy and the size of forest area. The design and development of WSNs give high impact and feasibility to overcome current issues of forest fire and fire hotspot detection in Indonesia. The development of this system used WSNs highly applicable for early warning and alert system for fire hotspot detection

Forecasting Rainfall Distribution Based on Deseasonalising Fuzzy Time Series Model

Rainfall prediction is an essential process to reduce loss of lives and properties. However, the accuracy of this prediction has been of many concerns in literature. Therefore, this paper proposed a model of rainfall prediction based on deseasonalising data and fuzzy time series concept. The historical data of rainfall distribution were collected from Drainage and Irrigation Department, Perlis Malaysia between January 2000 and December 2013. These data were analysed in order to determine the seasonal components using fuzzy time series as a medium. The study made use of deseasonalising rainfall data by employing fuzzy time series model in order to forecast the rainfall distribution. The model performance was evaluated by using statistical criteria of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The obtained result was compared with several forecasting models in literature and it was found to be more accurate than others. Hence, this study demonstrates that fuzzy time series model is more suitable for the accurate prediction of rainfall distributions

Deseasonalised forecasting model of rainfall distribution using fuzzy time series

Flood is a frequent occurrence which has a high calamity impact on human lifestyle, environment and economics.Although, there are various methods in the vast literature to predict rainfall distributions so as to prevent flood occurrences, the accuracy of these methods still remain a huge concern.Therefore, this study explores the application of the fuzzy time series method in order to obtain more accurate rainfall distribution predictions.Data for the study were collected from the Drainage and Irrigation Department Perlis (DID) of Malaysia.The data were analysed and validated using the mean square error (MSE) and the root mean squared error (RMSE).The result of the validation was compared with selected results in previous methods.The validation analysis depicts that this method has a higher forecasting accuracy than the previous methods

Interval Based in Fuzzy Sliding Window for Forecasting Crude Palm Oil

Interval is the main component in time series forecasting, hence a Fuzzy Sliding Window Forecasting Method (SWM) suggested in obtaining intervals of forecasting in the Fuzzy Time Series (FTS). Formerly, almost all the intervals were calculated using class frequency. The intervals are then regrouping into the sub-intervals using the provided category. Whereas in this study, the prediction of interval obtained by embedding the idea of SWM into FTS forecasting. The intention of this suggested method is to further improve the success of a time series forecast and indirectly increase forecasting precision. The daily prices of Crude Palm Oil (CPO) data are taken for verification purposes. Hence, the precision of the suggested method is differentiating the existing forecasting method. The outcome of this method is compared to the other methods and it reveals that the suggested method produces precise intervals determination. The discovery of this study can be used as a replacement of existing forecasting method to get an improved prediction interval

A Weighted Subsethood Mamdani Fuzzy Rules Based System Rule Extraction (MFRB-WSBA) for Forecasting Electricity Load Demand - A Framework

Fuzzy rules are very important elements that should be taken consideration seriously when applying any fuzzy system. This paper proposes the framework of Mamdani Fuzzy Rulebased System with Weighted Subsethood-based Algorithm (MFRBS-WSBA) for forecasting electricity load demand. Specifically, this paper proposed two frameworks: MFRBSWSBA and WSBA framework where the WSBA is embedded in MFRBS-WSBA (fourth step in MFRBS-WSBA). The objective of this paper is to show the fourth step in the MFRBS-WSBA framework which applied the new electricity load forecasting rule extraction by WSBA method. We apply the proposed WSBA framework in Malaysia electricity load demand data as a numerical example in this paper. These preliminary results show that the WSBA framework can be one of alternative methods to extract fuzzy rules for forecast electricity load demand where the proposed method provide a simple to interpret the fuzzy rules and also offer a new direction to interpret the fuzzy rules compared to classical fuzzy rule