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

Molecular Cloning, Expression, And Characterization Of Glutathione-Stransferase As A Novel Target In Antimalarial Drug Design And Discovery

Glutation-S-transferase (GSTs) adalah sekumpulan enzim detoksifikasi. Plasmodium falciparum mempunyai isoform tunggal GST (PfGST) yang terlibat dalam bagi detoksifikasi heme. The Glutathione-S-transferases (GSTs) are group of detoxification enzymes. Plasmodium falciparum has a single isoform of GST (PfGST) that involves in heme detoxification. While other GSTs isoforms from human (hGSTP1) and mouse (mGSTM1) are involved in apoptotic stress kinase pathway and mediate cancer cell resistance to chemotherapy

Development of STEP-NC based machining system for machining process information flow

To realize the STEP-NC based machining system, it is necessary to perform machining feature extraction, generating machine-specific information, and creating a relationship between STEP-NC entities. A process planning system of a STEP-NC information flow that starts with constructing a machining feature from a CAD model will be developed. In this paper, a further in depth study of the implementation and adaptation of STEP-NC in manufacturing is studied. This study will help to understand how the data from CAD/CAM can be converted into STEP-NC codes and the machining process will be based on the STEP-NC codes generated

Good governance and the rule of law

Good governance is basically governing in the right and just ways.Good governance relate,s to good administration at both public and private sectors.Corporate governance is synonymous and the common usage in the private sector Common characteristics of good governance include transparency, accountability, participatory and rule of law.Rule of law is the focus of this paper The principle in itself is problematic because of multifarious interpretation Nonetheless, the consensus has been that rule of law is essential in any government and breach of its principles may lead to arbitrariness and breach of fundamental rights.The paper will expound the roles of rule of law in ensuring good governance and how abuse of power and corruption have undermined rule of law seriously and subsequently affect good governanc

Hybrid expert system of rough set and neural network

The combination of neural network and expert system can accelerate the process of inference, and then rapidly produce associated facts and consequences. However, neural network still has some problems such as providing explanation facilities, managing the architecture of network and accelerating the training time. Thus to address these issues we develop a new method for pre-processing based on rough set and merge it with neural network and expert system. The resulting system is a hybrid expert system model called a Hybrid Rough Neural Expert System (HRNES)

Fouling prediction using neural network model for membrane bioreactor system

Membrane bioreactor (MBR) technology is a new method for water and wastewater treatment due to its ability to produce better and high-quality effluent that meets water quality regulations. MBR also is an advanced way to displace the conventional activated sludge (CAS) process. Even this membrane gives better performances compared to CAS, it does have few drawbacks such as high maintenance cost and fouling problem. In order to overcome this problem, an optimal MBR plant operation needs to be developed. This can be achieved through an accurate model that can predict the fouling behaviour which could optimise the membrane operation. This paper presents the application of artificial neural network technique to predict the filtration of membrane bioreactor system. The Radial Basis Function Neural Network (RBFNN) is applied to model the developed submerged MBR filtration system. RBFNN model is expected to give good prediction model of filtration system for estimating the fouling that formed during filtration process