SCOPUS 2005-2009
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Item An adaptive IDS model based on swarm intelligence and support vector machine(2006) Surat Srinoy; S. Srinoy; Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand; email: surat_sri@dusit.ac.thIntrusion detection system looks for unusual or suspicious activity, such as patterns of network traffics that are likely indicators of unauthorized activity. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large, human labeling is tedious, time-consuming, and expensive. In this paper we present support vector machine approach to data clustering. Support vector machine is used to initially create raw clusters and then these clusters are refined using Artificial Fuzzy Ants Clustering (AFAC). AFAC that has been developed as swarm intelligence techniques. The Algorithm uses ant colony optimization principle to find good partitions of the data. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose Artificial Fuzzy Ants Clustering (AFAC) in the second stage for refinement mean of overcoming these complexities are proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999). © 2006 IEEE.Item Intelligent model for automatic text summarization(2009) M.S. Binwahlan; N. Salim; L. SuanmaliThe navigation through hundreds of the documents in order to find the interesting information is a tough job and waste of the time and effort. Automatic text summarization is a technique concerning the creation of a compressed form for single document or multi-documents for tackling such problem. In this study, we introduced an intelligent model for automatic text summarization problem; we tried to exploit different resources advantages in building of our model like advantage of diversity based method which can filter the similar sentences and select the most diverse ones and advantage of the non diversity method used in this study which is the adaptation of intelligent techniques like fuzzy logic and swarm intelligence for building that method which gave it a good ability for picking up the most important sentences in the text. The experimental results showed that our model got the best performance over all methods used in this study. © 2009 Asian Network for Scientific Information.Item Anomaly-based intrusion detection using fuzzy rough clustering(2006) Witcha Chimphlee; Abdul Hanan Abdullah; Mohd Noor Md. Sap; Surat Srinoy; Siriporn Chimphlee; W. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Thailand; email: witcha_chi@dusit.ac.thIt is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods © 2006 IEEE.Item Shelf-life extension of refrigerated soft shell mud crab (Scylla serrata Forskal) by ozone water and storage under air and modified atmosphere packaging(2007) Nathapol Phapinyo; Tawirat Chaowalit; Aiyakarn Tanpipattanakul; Kijja Sooknet; Aekarat Kumthong; Wanchai Worawattanamateekul; Jim P. Smith; N. Phapinyo; Program of Food Science and Technology, Faculty of Science and Technology, Rajabhat Suan Dusit University, Bangkok 10300, Thailand; email: nphapinyo@yahoo.comThe effects of ozone water combined with modified atmosphere packaging on the refrigerated storage quality of soft shell mud crab at 4±0.5¡C were investigated. Soft shell mud crab stored under modified atmosphere packaging and vacuum had lower microbial counts compared with that of mud crab stored in an aerobic package (p<0.05). The odor and overall acceptability were acceptable for the soft shell mud crabs packaged under 80%CO 2+20%N2 and vacuum throughout the storage period of 11 days. Microbial changes in all samples were generally in agreement with sensory panel evaluations. The shelf life of soft shell mud crab soaked in 1.0 ppm for 20 min and packaged under 80%CO2+20%N2 and vacuum was extended to at least 11 days at 4±0.5¡C. The shelf life of soft shell mud crab soaked in water and aerobically packaged was 3 days.Item Swarm Based Text Summarization(2009) Mohammed Salem Binwahlan; Naomie Salim; Ladda Suanmali; M. S. Binwahlan; Faculty of Computer Science and Information Systems, University Teknologi Malaysia, 81310 Skudai, Johor, Malaysia; email: moham2007med@yahoo.comThe scoring mechanism of the text features is the unique way for determining the key ideas in the text to be presented as text summary. The treating of all text features with same level of importance can be considered the main factor causing creating a summary with low quality. In this paper, we introduced a novel text summarization model based on swarm intelligence. The main purpose of the proposed model is for scoring the sentences, emphasizing on dealing with the text features fairly based on their importance. The weights obtained from the training of the model were used to adjust the text features scores, which could play an important role in the selection process of the most important sentences to be included in the final summary. The results show that the human summaries H1 and H2 are 49% similar to each other. The proposed model creates summaries which are 43% similar to the manually generated summaries, while the summaries produced by Ms Word summarizer are 39% similar. © 2009 IEEE.Item Unsupervised learning: A fusion of rough sets and fuzzy ants clustering for anomaly detection system(Institute of Electrical and Electronics Engineers Inc., 2006) Surat Srinoy; Werasak Kurutach; S. Srinoy; Department Computer Science, Suan Dusit Rajabhat University, Thailand; email: surat_sri@dusit.ac.thThe Traditional intrusion detection systems (IDS) look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. However, normal operation often produces traffic that matches likely "attack signature", resulting in false alarms. One main drawback is the inability of detecting new attacks which do not have known signatures. In this paper we propose an intrusion detection method that proposes rough set based feature selection heuristics and using fuzzy ants for clustering data. Rough set has to decrease the amount of data and get rid of redundancy. Fuzzy ants clustering methods allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset. ©2006 IEEE.Item Sentence features fusion for text summarization using fuzzy logic(2009) Ladda Suanmali; Mohammed Salem Binwahlan; Naomie Salim; L. Suanmali; Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, 10300, Thailand; email: ladda_sua@dusit.ac.thThe scoring mechanism of the text features is the unique way for determining the key ideas in the text to be presented as text summary. The efficiency of the technique used for scoring the text sentences could produce good summary. The feature scores are imprecise and uncertain, this marks the differentiation between the important features and unimportant is difficult task. In this paper, we introduce fuzzy logic to deal with this problem. Our approach used important features based on fuzzy logic to extract the sentences. In our experiment, we used 30 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we use 9 important features and calculate their score for each sentence. We propose a method using fuzzy logic for sentence extraction and compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the highest average precision, recall, and F-measure for the summaries were obtained from fuzzy method. © 2009 IEEE.Item Integrating genetic algorithms and fuzzy c-means for anomaly detection(2005) Witcha Chimphlee; Abdul Hanan Abdullah; Mohd Moor Md Sap; Siriporn Chimphlee; Surat Srinoy; W. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Road, Thailand; email: witcha_chi@dusit.ac.thThe goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate. We used data set from 1999 KDD intrusion detection contest. © 2005 IEEE.Item Firmness properties of mangoes(2007) Bundit Jarimopas; Udomsak Kitthawee; B. Jarimopas; Department of Agricultural Engineering, Faculty of Engineering, Kamphaengsaen Kasetsart University, Kamphaenagsaen, Nakohn Pathom, Thailand; email: jarimopas@yahoo.comThe purpose of this research was to determine the firmness of mango fruit at different stages of maturity. Immature, mature, and over mature fruit from two Thai mango cultivars (Nam Dokmai and Chok Anan) were subjected to two firmness measuring techniques: a slow compression technique using the Universal Testing Machine and a high-speed impact sensing technique using the low-mass impact tester. The firmness of a mango as determined by the compression test was expressed by the slope of the force-deformation graph, while firmness values derived from the impact test were defined by the ratio between maximum acceleration and the corresponding time (the firmness index). A very good correlation between the two indicators was obtained. Each firmness indicator of the two cultivars remained relatively unchanged from the immature through to the fully mature stage, although firmness rapidly decreased as the fruit ripened. The Chok Anan cultivar was firmer than the Nam Dokmai type throughout the development period. The impact method was able to determine mango firmness rapidly, accurately, and non-destructively.Item Integration Soft Computing Approach to Network Security(Institute of Electrical and Electronics Engineers Inc., 2007) Surat Srinoy; S. Srinoy; Computer Science Department, Suan Dusit Rajabhat University, Bangkok, 295 Ratchasima Rd, Thailand; email: surat_sri@dusit.ac.thComputer security is defined as the protection of computing system against threats to confidentiality, integrity, and availability. Due to increasing incidents of cyber attacks, building effective intrusion detection systems are essential for protecting information systems security. It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in this area. Lacking a distinctive boundary definition among normal and abnormal datasets, discriminating the normal and abnormal behaviors seems too much complex. This paper proposes an integrating support vector machine and rough set for recognizing intrusion detection in computer network. Empirical results clearly show that support vector machine and rough set approach could play a major role for intrusion detection systems. © 2007 IEEE.Item Media education in Thailand: Contexts and prospects(Springer Netherlands, 2009) John Langer; Nuntiya Doungphummes; J. Langer; School of Communication and the Arts, Victoria University, Melbourne, Australia; email: john.langer@vu.edu.auIn August 2005 Thailand's National Statistical Office released a survey announcing nearly three and a half million Thais to be 'illiterate'. Another fifteen million, it was discovered, had the ability to read but opted not to, for a range of reasons. One of these was a preference for watching television. Of the total who could read, over twenty five percent were found to be 'non-reading literates'. Possibly, the most revealing aspect of the survey was not its results but the intense public reaction in the following weeks: was Thailand turning into a country of passive couch potatoes; what did this say about our nation's cultural sensibilities; where was the potential for growth and change? Letters to daily newspapers flooded in, and column inches were filled with commentary, criticism, alarm, speculation and solutions. © 2009 Springer Netherlands.Item Anomaly detection model based on bio-inspired algorithm and independent component analysis(Institute of Electrical and Electronics Engineers Inc., 2006) Surat Srinoy; Werasak Kurutach; S. Srinoy; Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Ratchasima Road, Thailand; email: surat_sri@dusit.ac.thWith the advent and explosive growth of the global Internet and electronic commerce environments, adaptive/automatic network/service intrusion and anomaly detection in wide area data networks and e-commerce infrastructures is fast gaining critical research and practical importance. In this paper we present independent component analysis (ICA) based feature selection heuristics approach to data clustering. Independent Component Analysis is used to initially create raw clusters and then these clusters are refined using parallel Artificial Immune Recognition System(AIRS). AIRS that has been developed as an immune system techniques. The Algorithm uses artificial immune system(AIS) principle to find good partitions of the data. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose parallel Artificial Immune Recognition System (AIRS)) in the second stage for refinement mean of overcoming these complexities are proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999). © 2006 IEEE.Item E-learning in Thailand: An analysis and case study(International Institute of Informatics and Systemics, IIIS, 2005) Pannee Suanpang; Jarinee SantijanyapornThis paper presents a discussion of E-Leaming in the context of Thailand using as an example a study carried out in a course in Business Statistics at Suan Dusit Rajabhat University (SDU), Thailand. The online course was a pioneering research project at SDU for studying the efficiency and effectiveness of the online learning system. The research conducted over 16 weeks compared online learning with traditional teaching. Aspects of students' learning outcomes have been analyzed, including quantitative features such as their grades and course evaluations, and this analysis is supported by qualitative features such as results of open-ended questionnaires, interviews and diaries. Results of the analysis show that students' outcomes were more favorable in the online groups than in the traditional groups. The large amount of rich qualitative information obtained highlights a range of reasons for this. The results of this study will be beneficial and useful for further research to develop effective and efficient online learning systems in Thailand, and in other countries with similar educational backgrounds to this country.Item An approach to solve computer attacks based on hybrid model(2006) Surat Srinoy; Witcha Chimphlee; Siriporn Chimphlee; Yoothapoom Poopaibool; S. Srinoy; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Ratchasima Road, Thailand; email: surat_sri@dusit.ac.thIt is an important issue for the security of network to detect new intrusion attack. We introduce the idea of the Independent component analysis (ICA) based feature selection heuristics, and present Support Vector Machine (SVM) algorithm for data classification. ICA aims at extracting unknown hidden factors/components from multivariate data using only the assumption that unknown factors are mutually independent. The experimental results on dataset Knowledge Discovery and Data Mining (KDDCup99) show that our method outperforms the existing intrusion detection methods.Item An integrated fuzzy ants and artificial immune recognition system for anomaly detection(2006) Surat Srinoy; Werasak Kurutach; S. Srinoy; Department of Computer Science, Suan Dusit Rajabhat University, Bangkok, Thailand; email: surat_sri@dusit.ac.thA computer system intrusion is seen as any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. The introduction to networks and the internet caused great concern about the protection of sensitive information and have resulted in many computer security research efforts during the past few years. This paper highlights a novel approach for detecting intrusion based on bio-inspired algorithm. The intrusion detection model combines the fuzzy ants clustering algorithm and artificial immune recognition algorithm to maximize detection accuracy and minimize computational complexity. The implemented system has been tested on the training data set from DARPA DATA SET by MIT Lincoln Laboratory on intrusion. The applicability of the proposed method and the enhanced security it provides are discussed. © 2006 ICASE.Item Anomaly intrusion detection systems based on evolutionary computing(2007) Surat Srinoy; Siriporn Chimphlee; Witcha ChimphleeAs malicious intrusions are a growing problem, we need a solution to detect the intrusions accurately. Network administrators are continuously looking for new ways to protect their resources from harm, both internally and externally. Intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. The objective of this paper is to describe a fuzzy c-means and genetic algorithms and discuss its usage to detect intrusion in a computer network. We are using a Genetic Algorithms (GA) to select a subset of input features for clustering with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Experiments were performed with DARPA data sets, which have information on computer networks, during normal behavior and intrusive behavior.Item Proximate analysis and physico-chemical properties of flour from the seeds of the China chestnut, Sterculia monosperma ventenat(2009) S. Noitang; S.A. Sooksai; T. Foophow; A. PetsomThe aim of this study was to evaluate nutritional composition of china chestnut seeds, Sterculia monosperma Vent. and analyze the physico-chemical properties of flour from the seeds. The results obtained on proximate analysis of china chestnut seeds, S. monosperma, revealed that they contained mostly carbohydrate (73.7% dm), followed by fat (12.0% dm), protein (7.8% dm), fiber (5.5% dm) and ash (1.0% dm). They have a relatively high content of potassium (12.3 mg g-1 dm) following by phosphorus (2.30 mg g-1 dm), magnesium (1.87 mg g-1 dm), sulfur (0.88 mg g-1 dm) and calcium (0.14 mg g-1 dm). The fatty acids profile was found to be composed of mainly palmitic (42%) and oleic acids (34%), with general long-chain fatty acids the other significant component by mass (13%). Glutamic acid (17.4%), aspartic acid (12.5%) and arginine (12.5%) were the three major amino acid constituents. The purified seed starch was investigated for its morphological, starch content and physico-chemical properties, such as amylose content, swelling power, solubility and pasting properties. The starch granules were quite round, about 10-15 micron diameter and composed of more than 35% (w/w) of amylose. The pasting properties of flour from the seeds of S. monosperma revealed that gelatinization began at 72.6-73.2¡C and the maximum viscosity in the holding period at 95¡C was 633 BU. Interestingly and potentially of use, was that the viscosity at the cooling period was more than two-fold higher than that in the holding period. © 2009 Asian Network for Scientific Information.Item Fuzzy swarm based text summarization(2009) Mohammed Salem Binwahlan; Naomie Salim; Ladda SuanmaliProblem statement: The aim of automatic text summarization systems is to select the most relevant information from an abundance of text sources. A daily rapid growth of data on the internet makes the achieve events of such aim a big challenge. Approach: In this study, we incorporated fuzzy logic with swarm intelligence; so that risks, uncertainty, ambiguity and imprecise values of choosing the features weights (scores) could be flexibly tolerated. The weights obtained from the swarm experiment were used to adjust the text features scores and then the features scores were used as inputs for the fuzzy inference system to produce the final sentence score. The sentences were ranked in descending order based on their scores and then the top n sentences were selected as final summary. Results: The experiments showed that the incorporation of fuzzy logic with swarm intelligence could play an important role in the selection process of the most important sentences to be included in the final summary. Also the results showed that the proposed method got a good performance outperforming the swarm model and the benchmark methods. Conclusion: Incorporating more than one technique for dealing with the sentence scoring proved to be an effective mechanism. The PSO was employed for producing the text features weights. The purpose of this process was to emphasize on dealing with the text features fairly based on their importance and to differentiate between more and less important features. The fuzzy inference system was employed to determine the final sentence score, on which the decision was made to include the sentence in the summary or not. © 2009 Science Publications.Item Rough fuzzy approach for web usage mining(2006) Siriporn Chimphlee; Naomie Salim; Mohd Salihin Bin Ngadiman; Witcha Chimphlee; Surat Srinoy; S. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Rd, Thailand; email: siriporn_chi@dusit.ac.thWeb usage mining is a new subfield of data mining research. It aims at discovery of trends and regularities in web users' access patterns. In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. A challenge in web classification is how to deal with the high dimensionality of the feature space. This paper applies the concept of rough fuzzy approach for classification in web usage mining tasks after we present Independent Component Analysis (ICA) for feature. Clustering is an important part of web mining that involves finding natural groupings of web resources or web users.Item Using association rules and Markov model for predict next access on Web usage mining(2006) Siriporn Chimphlee; Naomie Salim; Mohd Salihin Bin Ngadiman; Witcha ChimphleePredicting the next request of a user as visits Web pages has gained importance as Web-based activity increases. A large amount of research has been done on trying to predict correctly the pages a user will request. This task requires the development of models that can predicts a user's next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site access prediction based on past visitor behavior and compare it association rules technique. In these approaches, sequences of user requests are collected by the session identification technique, which distinguishes the requests for the same web page in different browses. We report experimental studies using real server log for comparison between methods and show that degree of precision. © 2006 Springer.