Somchai Prakancharoen

A combined semantic social network analysis framework to integrate social media data

The technology of web 2.0 especially social networking has changed peoples behavior, attitudes, interactions, and relationships. User activities on social networking sites have created both explicit and implicit relationship. The relations can be distinguished for the sites with symmetric and asymmetric relationship types. In this paper, we propose a framework for semantic social network analysis for recommending user member groups on social media sites that combines explicit and implicit relationship. The framework utilizes semantic technology, i.e. ontology and RDF, to integrate the resulted data from different sites. Moreover, a presentation of a ontology design for user and activities referring to social network sites. An example of the integrated user profile is demonstrated using FOAF vocabulary.

Combining Algorithms for Recommendation System on Twitter

Twitter has rapidly increased in popularity over the past few years. So, we have focused on Twitter as it has a large scale of data which is increasingly difficult to search through. In this paper, we propose recommendations for content on Twitter. We explored four dimensions in designing such as: topic relevance of content sources, the content candidate set for users, social voting and Meta data mapping. We implemented 24 algorithms for analysis of 12,000 records for three domains as follows: entertainment, stock exchange and smart phone in the design space. The best performing algorithm improved the percentage of correct matching interesting content to 23.86%.

Efficient Handwritten Curve Approximation by a Bezier Curve Using Chebyshev Polynomials

This paper presents two methodologies for approximating a handwritten curve by a Bézier curve using Chebyshev Polynomials. First, two different featured point selection techniques, are introduced in algorithms 1 and 2. Algorithm 1 uses frequency while algorithm 2 uses the analysis of inflection point. Then, using Chebyshev Polynomials for approximation curve, however a new curve must be adjusted by algorithm 3 because the end points of curve are swing.

Sys-log database manipulation security protocol

Sys-log message is the importance data which could present information about internet source-destination connection of all IP address nodes in each network server. These sys-log messages must be securely kept in database in case of being referenced, such as legal requesting. The objective of this research was to develop a security protocol of system log message database manipulation. Message was split to two non oblivious halves then sent them to be appended in two difference databases. These databases were managed by two database administrators (DBA#1, DBA#2). They could not knew the meaning of received message and both messages were meaningless when normally put two of them together. This protocol was developed by two security protocol; Message splitting and Blind signature protocol. Security techniques such as RSA, Keyed hashing function-MAC were used as basic processing in protocol scenario. Nevertheless, each DBA could verify authentication of the sender (system administrator) by senders digital signature and integrity of log message by keyed hashing function. This designed protocol can help network system administrator and DBA easily manipulate sys-log database and also increase securely ensure manipulation of sys-log database.

Transformation Polynomial Secret Sharing

This research objective is to introduce how the secret sharing algorithm was used to increase the security of your data before distributing to the database. The first arbitrated polynomial function was transformed to be another one in order to enhance more security. Plaintext was reconstructed by reversing two stages interpolation. This algorithm improves more security than ordinary share secret sharing algorithm.

The homomorphic other property of Paillier cryptosystem

In this paper, one example of Pailliers encryption schemes and homomorphic encryption was illustrated. In mathematical details, Subtraction, Multiply, Division binary operation of binary based integer number operands was presented. In particular, the secrecy of encryption and decryption will be shown. Both operands were still encrypted even through an other operation was processing.

A Comparison of Accuracy Based on Data Imputation between Unconstrained Structural Equation Modeling and Weighted Least Squares Regression

This research proposed a comparison of accuracy based on data imputation between unconstrained structural equation modeling (Uncon-SEM) and weighted least squares (WLS) regression. This model is developed by University of California, Irvine (UCI) and measured using the mean magnitude of relative error (MMRE). Experimental data set is created using the waveform generator that contained 21 indicators (1,200 samples) and divided into two groups (1,000 for training and 200 for testing groups). In fact, training group was analyzed by three main factors (F1, F2, and F3) for creating the models. The result of the experiment show MMRE of Uncon-SEM method based on the testing group is 34.29% (accuracy is 65.71%). In contrast, WLS method produces MMRE for testing group is 55.54% (accuracy is 44.46%). So, Uncon-SEM is high accuracy and MMRE than WLS method that is 21.25%.

Accuracy Comparison of Data Imputation Estimation Methods between the Unconstrained Structural Equation Modeling and K-Nearest Neighbors

This study aimed to the accuracy comparison of data imputation estimation methods between the unconstrained structural equation modeling (Uncon-SEM) and k-nearest neighbors (K-NN). The measurement accuracy of the model based on the mean magnitude of relative error (MMRE). The model is developed by using the online database from University of California, Irvine (UCI) which is a data set on waveform generators. Indicators 21 (1,200 sets) methods were as follows: 1) Data set was divided into two groups (experimental group of 1,000 sets and test group of 200 sets); 2) The experimental group was analyzed by three main factors (F1, F2, F3); 3) Uncon-SEM method: It created a SEM with three main factors, then the remaining factors to be created new the relationships with the unconstrained approach and created new SEM. The test data was substituted in the equation to find the MMRE which was 34.29% (accuracy was 65.71%); 4) K-NN method: It selected the main factor was the relationship of the missing data (F2). Measure the Euclidean distance between the test group and experimental group and selected 5 (K=5) of data sets were nearest to the missing data for the estimate by mean. The MMRE which was 57.00% (accuracy was 43.00%). Thus, comparing estimates of missing data showed that using the Uncon-SEM method were more accuracy, and MMRE declined about 22.71% than K-NN method.

Web Based Application Maintenance Cost Estimation Modeling Using Bayesian SEM

The objectives of this research were to find out the Structural equation modeling coefficient and other parameter estimation under unknown prior distribution and compare this new model’s coefficient accuracy with the former model on “Web based application maintenance cost estimation multi group modeling” [16]. This new coefficient and parameter were estimated with Bayesian analysis instead of Maximum likelihood estimation (ML). The input model used in Bayesian analysis was started from the ML-model result [16]. The new values were replaced into the former model then MMRE was detected from 30 (testing) completed software maintenance projects while 192 projects were used for SEM model training. The result of cross validation was about 44.08% for Bayesian analysis refined SEM model while the ML-SEM model was 47.58%.