A multi-criteria approach to the evaluation of Malaysian government portal

National portal is one of the main gateways to e-government entry for providing e-services to citizen. The purpose of this paper is to evaluate a set of factors that influence on Malaysian government portal using Multi-Criteria method. These factors were derived from a previous studies and literature related to the subject matter by Malaysian government to enhance rank of government portals and websites. In addition , obtained factors were considered from all Malaysian government portals and in this research based on them the rank of those were evaluated. In this study we also consider those factors to rank them and show which factors is more important for improving Malaysian government portal based on citizen desires. With regard to the citizen's perception of government portal, a questionnaire was structured to gather their responses for raking the factors using TOPSIS method. Therefore, an MCDM supported study for ranking criteria in government portals. The outcome of this paper assists to Malaysian e-government that considers importance of effective factors for government portal in giving better e-services

Comparative Study of Artificial Neural Network and ARIMA Models in Predicting Exchange Rate

Abstract: Capital market as an organized market has an effective role in mobilizing financial resources due to have growth and economic development of countries and many countries now in the finance firms is responsible for the required credits. In the stock market, shareholders are always seeking the highest efficiency, so the stock price prediction is important for them. Since the stock market is a nonlinear system under conditions of political, economic and psychological, it is difficult to predict the correct stock price. Thus, in the present study artificial intelligence and ARIMA method has been used to predict stock prices. Multilayer Perceptron neural network and radial basis functions are two methods used in this research. Evaluation methods, selection methods and exponential smoothing methods are compared to random walk. The results showed that AI-based methods used in predicting stock performance are more accurate. Between two methods used in artificial intelligence, a method based on radial basis functions is capable to estimate stock prices in the future with higher accuracy

Presenting a Meta-Heuristic Algorithm to Detect Regulatory Elements in the Genome of Breast Cancer Patients

Background & Objective: Nowadays, in medical sciences, the amount of data on symptoms of people affected with various illnesses on one hand, and finding assistive techniques for the diagnosis of those diseases on the other, has been widespread. Consequently, the analysis and consideration of all factors involved in a disease are often challenging. Thus, a mechanized system to help discover the rules, identify patterns, and predict future events is absolutely needed. In this research, we intend to use a multi-objective algorithm to provide a method capable of detecting, extract sequences of variable-length from the genome, and count the interactions among them. In fact, these regulatory elements could play a significant role in the incidence and exacerbation of cancer. Material & Methods: In this research, a proposed method for the detection of regulatory elements in the genome of a breast cancer patient has been used. The proposed method is implemented in MATLAB software. Also, to measure the performance and effectiveness of the suggested method, the proposed algorithm is implemented on HiC dataset, regarding patients with breast cancer in two blood cells GM12878 and CD34+ introduced by Mifsud et al. Results: The results of implementing the proposed method are compared with the HiCUP method. The results show that the MSARE method has a better performance in detecting regulatory elements compared to the HiCUP method. Conclusion: Experimental studies have shown that the two promoters BLC6 and HOTTIP discovered by the proposed method have had a significant effect on the incidence and severity of breast cancer in both blood cells GM12878 and CD34+

Application of K-nearest neighbour predictor for classifying trust of B2C customers

K-nearest neighbor (k-NN) classification is one of the most fundamental classification methods and should be one of the first choices for a classification study when there is little or no prior knowledge about the distribution of the data. In addition, nearest neighbor analysis is a method for classifying cases based on their similarity to other cases. In this paper using k-NN method some factors that affect on customer trust in online transactions, were classified. Raw data gathered from customers when they were buying as customer in B2C websites. One questionnaire was developed and data was gathered from online customers. After organizing data, k-NN method was applied and desired results were obtained. Results showed that in which positions customer can trust to B2C websites and which factors are more significant. Accordingly, in this paper k-NN enable us to predict role of factors on trust level in five levels

Collaborative Filtering Recommender Systems

Abstract: Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decisionmaking processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms

Application of ANFIS system in prediction of machining parameters

Since cutting conditions have an influence on reducing the production cost and time of machining process and also the quality of a final product the prediction of output machining parameters such as surface roughness and tool life criteria for different cutting speed, feed rate, depth of cut and tool geometry is one of vital modules in process planning of metal parts. In this study with use of experimental results on machining of ST-37 and subsequently, ANFIS system, importance of each parameter was studied. These parameters were considered as input in order to predict the surface finish and tool life criteria, two conflicting objectives, as the process performance. In this paper ANFIS system was applied to predict output parameters of machining. Results show that amount of input influence on the outputs parameters. By using ANFIS input parameters entered to ANFIS system then all training data was trained with 300 epochs. After training the value of error which is 1.039e-006 was calculated