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Text Categorization is the process of automatically assigning predefined categories to free text documents. Feature weighting, which calculates feature (term) values in documents, is an important preprocessing technique in text categorization. In this paper, we purpose Thai Document Categorization Framework focusing on the comparison of various term weighting schemes, including Boolean, tf, tf-idf,...
Microarray data contains thousands of genes which are used to evaluate expression level. However, most of them are not associated with cancer diseases and leads to the curse of dimensionality. The challenge based on microarray data is feature selection which searches for subsets of informative genes. At the moment, these techniques focus on filter and wrapper approaches to discover subsets of genes...
Traffic congestion is the cause of pollution and economic loss. The Real time traffic state report can alleviate this problem by assisting drivers for route planning and choosing unblocked roads. More traffic information could lead to more accurate route planning and greater awareness of traffic situations and road conditions for drivers. However, investment into sensor infrastructure for small minor...
Fuzzy ontology is based on the concept that each index object is related to every other object in the ontology, with a degree of membership assigned to that relationship based on fuzzy set theory. This paper proposes use cases based on the related process of the terrorism event extraction using fuzzy ontology, especially the terrorism fuzzy ontology construction methodology. The related use cases...
In this paper, we present a traffic flow model, a local routing strategy based on an estimated waiting time to improve transportation efficiency on Barabasi and Albert (BA) network model. Instead of global shortest path routing strategies, our method is cooperated between static and dynamic local information: degree of node and number of packets in node's queue, as an estimated waiting time, respectively...
The aim of this paper is to study and compare several machine learning methods for implementing a Thai terrorism event extraction system. The main function of the system is to extract information related to terrorism events found in Thai news articles. The terrorism events can then be classified and presented to intelligence officers who can further analyze and predict terrorism events. This paper...
Real time traffic congestion degree is useful information in assisting decision making of drivers. It can also be a factor for calculating other traffic information. The congestion degree can be usually calculated on the basis of sensors installed along roads. It is possible that the sensory data can be lost due to potentially unreliable communication or faulty sensors, leading to lost of important...
This paper presents a data clustering algorithm based on the natural behaviors of social insects in multiple colonies and multiple food sources concept; agents from each colony take a food back to their colony aimed to group the food. The proposed algorithm is a distributed data clustering algorithm based on multiple swarm-like agent colonies. Its advantages are a distributed data clustering and heterogeneous...
In this paper we propose a new classifier called an incremental learning algorithm based on support vector machine with Mahalanobis distance (ISVMM). Prediction of the incoming data type by supervised learning of support vector machine (SVM), reducing the step of calculation and complexity of the algorithm by finding a support set, error set and remaining set, providing of hard and soft decisions,...
Estimating the degree of traffic congestion at run time is crucially important for intelligent transportation systems (ITS) especially when selecting travel routes. One of the challenges when using a mobile sensor (e.g. mobile phone, GPS) as source of traffic data stems from its mobility. This paper proposes a novel approach to fusing mobile data and an algorithm for traffic congestion estimation...
This study was to create a forecasting model for evaluate freshmen's ability to succeed with using the longest rules from CARs technique as called a particular full-scaled class association rules (PFSCARs). The purposed of this study was to create a classifier tool to evaluate freshmen's ability. This study used demographic data of students in Information Technology program Chandrakasem Rajabhat University...
This paper is aimed to present a genetic algorithm focusing on the sexual selection used the Pareto based approach for solving multi-objective optimization problems. It uses a concept of sexual selection with different types of gender and mutation rates based on the sex to produce offspring. Its performance was evaluated by the well-known benchmark functions as well as also tested with a networking...
This paper proposes a decision tree-based model for automatic assignments of IT service desk outsourcing in banking business. The model endeavors to match the most appropriate resolver group with the type of the incident ticket on behalf of the IT service desk function. Recently, service desk technologies have not addressed the problem of performance in resolving incidents dropped due to overwhelming...
Global prediction techniques such as support vector machines show accurate prediction for time series data; however, such models tend to delay the predicted output. Fuzzy systems have benefits in local optimum, thus producing significant results within training sets. Unfortunately, the existing techniques sometimes give undesired effects of surface oscillation at predicted outputs. This paper presents...
Microarrays are useful biological resource to study living forms at the molecule level. Microarrays usually have only few samples but high dimensionality with many missing values. The consequent downstream analysis becomes less efficiency. This paper proposes a methodology to impute missing values in microarray data. The proposed methodology is a combination of KNN-based feature selection and KNN-based...
The main component of observation data includes both trend and seasonal effects. The represented equations of forecasting models like ARIMA seem to have too many explained parameters when we need more accuracy in time series prediction. To apply these elaborate and beautifully crafted techniques we require an advanced level of knowledge and sophistication only available from specialists. However,...
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