Azizul Azhar Ramli

A combination of genetic algorithm‐based fuzzy C‐means with a convex hull‐based regression for real‐time fuzzy switching regression analysis: application to industrial intelligent data analysis

Processing an increasing volume of data, especially in industrial and manufacturing domains, calls for advanced tools of data analysis. Knowledge discovery is a process of analyzing data from different perspectives and summarizing the results into some useful and transparent findings. To address such challenges, a thorough extension and generalization of well-known techniques such as regression analysis becomes essential and highly advantageous. In this paper, we extend the concept of regression models so that they can handle hybrid data coming from various sources which quite often exhibit diverse levels of data quality. The major objective of this study is to develop a sound vehicle of a hybrid data analysis, which helps in reducing the computing time, especially in cases of real-time data processing. We propose an efficient real-time fuzzy switching regression analysis based on a genetic algorithm-based fuzzy C-means associated with a convex hull-based fuzzy regression approach. The method enables us to deal with situations when one has to deal with heterogeneous data which were derived from various database sources (distributed databases). In the proposed design, we emphasize a pivotal role of the convex hull approach, which is essential to alleviate the limitations of linear programming when being used in modeling of real-time systems.

Real-time fuzzy switching regression analysis: a convex hull approach

Regression models are well known and widely used as one of the important models in system modeling. In this paper, we extend the concept of regression models in order to handle hybrid data coming from various sources of data quite often exhibiting diverse levels of quality. The major objective of this study is to develop a convex hull method being regarded as a potential vehicle, which helps reduce the computing time, especially in real-time data analysis as well as an overall computational complexity. We propose an efficient real-time fuzzy switching regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. The method addresses situations when we have to deal with heterogeneous data. In the proposed design setting, we emphasize a pivotal role of convex hull approach which is crucial when alleviating limitations of a linear programming manifesting in system modeling.

Indoor Navigation Using A* Algorithm

This paper introduced an indoor navigation application that helps junior students in Faculty of Computer Science and Information Technology (FSKTM) to find their classroom location. This project implements the A* (pronounced A Star) path finding algorithm to calculate the shortest path for users. Users can choose to view the floor plan of the building or start navigation. Users can choose their starting point from the list and set their destination to start navigation. The application is then calculate the shortest path for users by implement the A* algorithm. The route path will show on the floor plan after calculation done. Thus, users will find this project is easy to use and time saving.

E-Code Checker Application

Nowadays, many areas in computer sciences use ontology such as knowledge engineering, software reuse, digital libraries, web on the heterogeneous information processing, semantic web, and information retrieval. The area of halal industry is the fastest growing global business across the world. The halal food industry is thus crucial for Muslims all over the world as it serves to ensure them that the food items they consume daily are syariah compliant. However, ontology has still not been used widely in the halal industry. Today, Muslim community still have problem to verify halal status for halal products in the market especially in foods consisting of E number. In this paper, ontology will apply at E numbers as a method to solve problems of various halal sources. There are various chemical ontology and databases found to help this ontology construction. The E numbers in this chemical ontology are codes for chemicals that can be used as food additives. With this E numbers ontology, Muslim community could identify and verify the halal status effectively for halal products in the market.

A Practical Weather Forecasting for Air Traffic Control System Using Fuzzy Hierarchical Technique

Due to rapid changes of global climate, weather forecasting has becomes one of the significant research fields. Modern airports maintain high security flight operations through precise knowledge of weather forecasting. The objectives of this research focused on two major parts; the weather forecasting model of an airport system and the fuzzy hierarchical technique used. In general, this research emphasizes on the building blocks of a weather forecasting application that could support Terminal Aerodrome Forecast by utilizing Mamdani model. The developed application considers variables, groups of weather elements, combination of weather elements in a group, web data sources and structured knowledge to provide a profound forecast.

New perspectives and applications of real-time fuzzy regression

Fuzzy regression is one of important methods for data analysis. Fuzzy regression extends the concept of classical regression which has been constructed in the statistical framework. We show that a convex hull method can provide a powerful tool to reduce the computing time, especially for real-time data analysis. The main objective of this study is to propose an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. The reconstruction of convex hull edges depends on incoming vertices while a recomputing procedure can be implemented in real-time. An air pollution data is analyzed by applying the proposed approach. An important role of convex hull is emphasized in particular when dealing with the limitations of linear programming.

Modelling End-User of Electronic-Government Service: The Role of Information quality, System Quality and Trust

Many governments around the world increasingly use internet technologies such as electronic government to provide public services. These services range from providing the most basic informational website to deploying sophisticated tools for managing interactions between government agencies and beyond government. Electronic government (e-government) aims to provide a more accurate, easily accessible, cost-effective and time saving for the community. In this study, we develop a new model of e-government adoption service by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) through the incorporation of some variables such as System Quality, Information Quality and Trust. The model is then tested using a large-scale, multi-site survey research of 237 Indonesian citizens. This model will be validated by using Structural Equation Modeling (SEM). The result indicates that System Quality, Information Quality and Trust variables proven to effect user behavior. This study extends the current understanding on the influence of System Quality, Information Quality and Trust factors to researchers, practitioners, and policy makers.

Second Order Back Propagation Neural Network (SOBPNN) Algorithm for Medical Data Classification

Gradient based methods are one of the most widely used error minimization methods used to train back propagation neural networks (BPNN). Some second order learning methods deal with a quadratic approximation of the error function determined from the calculation of the Hessian matrix, and achieves improved convergence rates in many cases. This paper introduces an improved second order back propagation which calculates efficiently the Hessian matrix by adaptively modifying the search direction. This paper suggests a simple modification to the initial search direction, i.e. the gradient of error with respect to weights, can substantially improve the training efficiency. The efficiency of the proposed SOBPNN is verified by means of simulations on five medical data classification. The results show that the SOBPNN significantly improves the learning performance of BPNN.

Real Time Model of Fuzzy Random Regression Based on a Convex Hull Approach

In this study, we present a new idea dealing with the analysis of fuzzy random variables (FRVs) being treated as samples of data. The proposed concept can be used to model various real-life situations where uncertainty is not only present in the form of randomness but also comes in the form of imprecision described in terms of fuzzy sets. We propose a hybrid approach, which combines a convex hull approach (called Beneath-Beyond algorithm) with a fuzzy random regression analysis. Falling under the umbrella of intelligent data analysis (IDA) tool, this approach is suitable for real-time implementation of data analysis. For a fuzzy random data set, we include simulation results and highlight two main advantages, namely a decrease of required analysis time and a reduction of computational complexity. This emphasizes that the proposed IDA approach becomes an efficient way for real-time data analysis.

A Novel Idea of Real-Time Fuzzy Switching Regression Analysis: A Nuclear Power Plants Case Study

In this paper, the concept of regression models is extended to handle hybrid data from various sources that quite often exhibit diverse levels of data quality specifically in nuclear power plants. The major objective of this study is to develop a convex hull method as a potential vehicle which reduces the computing time, especially in the case of real-time data analysis as well as minimizes the computational complexity. We propose an efficient real-time fuzzy switching regression analysis based on a convex hull approach, in which a beneath-beyond algorithm is used in building a convex hull when alleviating limitations of a linear programming in system modeling. Additionally, the method addresses situations when we have to deal with heterogeneous data.