SMS Management System for Direct Sales and Network Marketing

SMS management system in direct sales and network marketing is a concept of integrating information system with mobile phone as well as using short message service (SMS) as a medium of communication in the business process of direct sales and network marketing sector. Direct sales and network marketing sector is a business phenomenon which expanding rapidly within these few years and it will keep on expanding. To deal with the large members and distributors joining the company, the management of these companies started to seek for new direction in upgrading the relationship management between the company and the distributors. This is important when the low cost and time saving SMS is introduce to these direct selling companies. With the intention of enhancing the connection between distributors is an opportunity to integrate SMS system in the management system in this industry. In this paper, we have analyzed how the SMS will play an important role in the business process by allowing the end user and the company will benefit from its simple and cost saving

An architecture for a focused trend parallel web crawler with the application of clickstream analysis

The tremendous growth of the Web poses many challenges for all-purpose single-process crawlers including the presence of some irrelevant answers among search results and the coverage and scaling issues regarding the enormous dimension of the World Wide Web. Hence, more enhanced and convincing algorithms are on demand to yield more precise and relevant search results in an appropriate amount of time. Since employing link based Web page importance metrics within a multi-processes crawler bears a considerable communication overhead on the overall system and cannot produce the precise answer set, employing these metrics in search engines is not an absolute solution to identify the best search answer set by the overall search system. Thus considering the employment of a link independent Web page importance metric is required to govern the priority rule within the queue of fetched URLs. The aim of this paper is to propose a modest weighted architecture for a focused structured parallel Web crawler which employs a link independent clickstream based Web page importance metric. The experiments of this metric over the restricted boundary Web zone of our crowded UTM University Web site shows the efficiency of the proposed metric

Multi-agent reinforcement learning for route guidance system

Nowadays, multi-agent systems are used to create applications in a variety of areas, including economics, management, transportation, telecommunications, etc. Importantly, in many domains, the reinforcement learning agents try to learn a task by directly interacting with its environment. The main challenge in route guidance system is to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing travel times and ensuring efficient use of available road network capacity. This paper proposes a multi-agent reinforcement learning algorithm to find the best and shortest path between the origin and destination nodes. The shortest path such as the lowest cost is calculated using multi-agent reinforcement learning model and it will be suggested to the vehicle drivers in a route guidance system. The proposed algorithm has been evaluated based on Dijkstra's algorithm to find the optimal solution using Kuala Lumpur (KL) road network map. A number of route cases have been used to evaluate the proposed approach based on the road network problems. Finally, the experiment results demonstrate that the proposed approach is feasible and efficient

Web news classification using neural networks based on PCA

In this paper, we propose a news web page classification method (WPCM). The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). The fixed number of regular words from each class will be used as a feature vectors with the reduced features from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM provides acceptable classification accuracy with the sports news datasets

The dominant of Bloggers in Malaysian politics through social networks

Every country in this world has own political issues. In Malaysia for example, political issues played an important role that can influence other factors such as social and economy. As we all know, political factor can give positive and negative effect to a situation in Malaysia. The frequent usage of computer nowadays by Malaysian people helps in spreading information and news about political situation in Malaysia through cyberspace. In this paper, we use web mining system with Artificial Immune System (AIS) to regain a small group of relevant websites and webpages on political issues in Malaysia. To analyze the relationship between website and webpages, the concept of social networks will be used. Result from the web mining system with AIS will be used to understand the impact of social network to the political situation in Malaysia

Financial time series representation using multiresolution important point retrieval method

Financial time series analysis usually conducts by determining the series important points. These important points which are the peaks and the dips indicate the affecting of some important factors or events which are available both internal factors and external factors. The peak and the dip points of the series may appear frequently in multiresolution over time. However, to manipulate financial time series, researchers usually decrease this complexity of time series in their techniques. Consequently, transfonning the time series into another easily understanding representation is usually considered as an appropriate approach. In this paper, we propose a multiresolution important point retrieval method for financial time series representation. The idea of the method is based on finding the most important points in multiresolution. These retrieved important points are recorded in each resolution. The collected important points are used to construct the TS-binary search tree. From the TS-binary search tree, the application of time series segmentation is conducted. The experimental results show that the TS-binary search tree representation for financial time series exhibits different performance in different number of cutting points, however, in the empirical results, the number of cutting points which are larger than 12 points show the better results

The determinants of internet financial disclosure: The perspective of Malaysian listed companies

This paper investigates whether Internet Financial Disclosure (IFD) can be explained by the elements of the company’s characteristics and dominant personalities in board committees.Ten variables have been tested using data collected from 194 Malaysian listed companies’ websites, namely, internationality, leverage, foreign shareholders, information technology (IT) experts, firm’s age, number of shareholders, listing status, dominant personalities in the audit committee, chairman of audit and nomination committees, and dominant personalities in the audit and nomination committees.It is found that IT experts, firm’s age, number of shareholders and listing status are significantly affected by the level of IFD. However dominant personalities in the audit and nomination committees are negatively related to the level of IFD in Malaysia.The study provides some evidence to support the signalling theory and the cost and benefit hypothesis in relation to Internet disclosure

Modeling the correlations of crude oil properties based on sensitivity based linear learning method

This paper presented a new prediction model of pressure–volume–temperature (PVT) properties of crudeoil systems using sensitivity based linear learning method (SBLLM). PVT properties are very important in the reservoir engineering computations. The accurate determination of these properties, such as bubble-point pressure and oil formation volume factor, is important in the primary and subsequent development of an oil field. Earlier developed models are confronted with several limitations especially their instability and inconsistency during predictions. In this paper, a sensitivitybasedlinearlearningmethod (SBLLM) prediction model for PVT properties is presented using three distinct databases while comparing forecasting performance, using several kinds of evaluation criteria and quality measures, with neural network and the three common empirical correlations. In the formulation used, sensitivity analysis coupled with a linear training algorithm for each of the two layers is employed which ensures that the learning curve stabilizes soon and behaves homogenously throughout the entire process operation. In this way, the model will be able to adequately model PVT properties faster with high stability and consistency. Empirical results from simulations demonstrated that the proposed SBLLM model produced good generalization performance, with high stability and consistency, which are requisites of good prediction models in reservoir characterization and modeling

Dominant personalities in board committees, company characteristics, and internet environmental disclosure by Malaysian listed companies

This paper investigates the determinants of internet environmental disclosure (IED) amongst Malaysian listed companies. Ten variables have been tested using data collected from 170 Malaysian listed company websites, namely, dominant personalities in the audit committee, chairman of audit and nomination committees, dominant personalities in the audit and nomination committees, internationality, leverage, foreign shareholders, level of technology, firm age, number of shareholders, and listing status. It was found that internationality, foreign shareholders, level of technology, firm age, number of shareholders, and listing status are significantly affected by the level of IED. However, dominant personalities in the audit committee, chairman of audit and nomination committees, dominant personalities in the audit and nomination committees, and leverage did not show a significant relationship with the level of IED in Malaysia. The study provided some evidence to support signaling theory, shareholder accountability theory, and cost and benefit hypothesis in relation to internet disclosure

A model transformation framework to increase OCL usability

The usability of a modeling language has a direct relationship with several factors of models constructed with the modeling language, such as time required and accuracy. Object Constraint Language (OCL) is the most prevalent language to document system constraints that are annotated in the Unified Modeling Language (UML). OCL is reputed as a modeling language with difficult syntax, and prior knowledge of OCL is needed to use the language. These obstacles result in the low usability of OCL. Therefore, the current research proposes a model to automatically transform system constraints formed in English sentences to OCL specifications. The proposed model is based on the Model-Driven Architecture (MDA) approach. The Linear Temporal Logic (LTL) properties of the proposed model are verified by the Maude model checker. To validate the proposed model and compare it with the existing work, the En2OCL (English2OCL) application is developed. This application is tested by three evaluation metrics: precision, recall, and f-measure. The En2OCL application is further compared with the NL2OCLviaSBVR application, which is the existing work on OCL generation from English sentences. The comparison shows a considerable improvement in precision, recall, and f-measure.Web of Science281261