Image Encryption Using Lagrange-Least Squares Interpolation
Authors :- Mohammed A. Shreef and Haider K. Hoomod
Keywords :- Image Encryption, Lagrange, Least Squares.
Published Online :- 19 September 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]Today, information security is becoming one of the most important issues in social network era. The fast development of network technology leads to facilitate many aspects of life, but it also gives attackers or unauthorized users an opportunity to violate the privacy of people. Encryption is a common technique that exists to protect information security, thereby deters attackers. Actually, digital images are widely used in storage and communication applications. Therefore, the protection of image data from unauthorized access has attracted much attention recently. This paper adopts a new image cryptosystem, XLLS, which consists of two main parts: encryption/decryption algorithm and ciphered key. The encryption algorithm is composed of two main stages: the diffusion stage and the substitution stage. In the diffusion stage, the pixels values are modified so that a slight change in one pixel is spread out to all pixels in the image. This stage completely depends in its construction on ‘XOR’ operation. For the substitution stage, it mainly composes of two encryption processes: Lagrange Process (LP) and Least Squares Process (LSP). This stage aims at changing the value of each pixel in the diffused image by using the principles of Lagrange interpolation and least squares method. For the decryption algorithm, it is simply the reverse of the encryption algorithm. On the other hand, the proposed cryptosystem introduces two different approaches of initial key. The users have option to choose any one of them to encrypt the plain-image. In the first approach, the proposed cryptosystem uses a key whose length is of 192 bits (24 bytes) in hexadecimal system as its input, and then expands it by using AES-192 key expansion algorithm. Conversely, in the second approach, the proposed cryptosystem uses an image as a key to cipher the plain-image, and then processes and expands the key-image by using the CBI key expansion algorithm.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/09/Image-Encryption-Using-Lagrange-Least-Squares-Interpolation.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] M, J. & S, M. (March 2012). A Survey on Various Encryption Techniques. International Journal of Soft Computing and Engineering (IJSCE), Vol. 2, Issue No. 1, pp. 429-432.
[2] El-Ashry, I. (2010). Digital Image Encryption. MS.c Thesis, Electronics and Electrical Communications Engineering Dept., Faculty of Electronic Engineering, Menofia University.
[3] Hung, K. (September 2007). A Study on Efficient Chaotic Image Encryption Schemes. MS.c Thesis, Electronic Engineering Dept., City University of Hong Kong.
[4] Jolfaei, A. & Mirghadri A. (September 2010). Survey: Image Encryption Using Salsa20. IJCSI International Journal of Computer Science Issues, Vol. 7, Issue No. 5, pp. 213-220.
[5] Mcgregor, J. (June 2005). Architectural Techniques for Enabling Secure Cryptographic Processing. PhD Thesis, Electrical Engineer Dept., Princeton University, New Jersey, United States.
[6] Ali, M. (2008). Scrambling and Encrypting Using Cipher Parameters Hopping, MS.c Thesis, Control and Computers Engineering Dept., University of Baghdad, Iraq.
[7] Younes, M. (2009). An Approach to Enhance Image Encryption Using Block Based Transformation Algorithm. PhD Thesis, the School of Computer Science, University Sains Malaysia.
[8] Chapra, S. & Canale, R. (2009). Numerical Methods for Engineers. Sixth Edition, McGraw-Hill College.
[9] Kiusalaas, J. (2005). Numerical Methods in Engineering with Python. First Edition, Cambridge University Press.
[10] Akif, O. (2012). Image Encryption Technique Using Lagrange Interpolation. Ibn Al-Haitham Journal for Pure and Applied Science, Vol. 25, No. 1.
[11] Abbasfard, M. (2009). Digital Image Watermarking Robustness: A Comparative Study. MS.c Thesis, Electrical Engineering Dept., Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, South Holland.
[12] Ahmad, J. & Ahmed, F. (August 2008). Efficiency Analysis and Security Evaluation of Image Encryption Schemes. International Journal of Video & Image Processing and Network Security, Vol. 12, No. 4, pp. 18-31.
[13] Congxu, Z. ( 1 January 2012). A Novel Image Encryption Scheme Based on Improved Hyperchaotic Sequences. Optics Communications, Vol. 285, Issue No. 1, pp. 29–37.
[14] Elashry, I., Farag Allah, O., Abbas, A., El-Rabaie, S. and Abd El-Samie, F.( July-September 2009). Homomorphic Image Encryption. Journal of Electronic Imaging, Vol. 18, No. 3, pp. 1-14.
[/accordionitem][/cq_vc_accordion]

Data Mining Approach to Detect Heart Dieses
Authors :- Vikas Chaurasia and Saurabh Pal
Keywords :- Bagging algorithm, Data Mining, Heart disease Diagnosis, J48 Decision Tree, Naïve Bayes.
Published Online :- 07 November 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]Globally, heart diseases are the number one cause of death. About 80% of deaths occurred in low- and middle income countries. If current trends are allowed to continue, by 2030 an estimated 23.6 million people will die from cardiovascular disease (mainly from heart attacks and strokes).
The healthcare industry gathers enormous amounts of heart disease data which, unfortunately, are not “mined” to discover hidden information for effective decision making. The reduction of blood and oxygen supply to the heart leads to heart disease. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques which will be useful for medical practitioners to take effective decision. The objective of this research work is to predict more accurately the presence of heart disease with reduced number of attributes. Originally, thirteen attributes were involved in predicting the heart disease. Thirteen attributes are reduced to 11 attributes. Three classifiers like Naive Bayes, J48 Decision Tree and Bagging algorithm are used to predict the diagnosis of patients with the same accuracy as obtained before the reduction of number of attributes. In our studies 10-fold cross validation method was used to measure the unbiased estimate of these prediction models.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2012/07/Data-Mining-Approach-to-Detect-Heart-Dieses.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] World Health Organization. 2007 7-Febuary 2011]; Available from: http://www.who.int/mediacentre/factsheets/fs310.pdf.
[2] Preventing Chronic Disease: A Vital Investment. World Health Organization Global Report. 2005
[3] Global Burden of Disease. 2004 update (2008). World Health Organization.
[4] Coronary Heart Diseases in India. Mark D Huffman. Center for Chronic Disease Control. http://sancd.org/uploads/pdf/factsheet_CHD.pdf
[5] Fayadd, U., Piatesky -Shapiro, G., and Smyth, P. 1996. From Data Mining To Knowledge Discovery in Databases, AAAI Press / The MIT Press, Massachusetts Institute Of Technology. ISBN 0–26256097–6 Fayap.
[6] Liao, S.-C. and I.-N. Lee, Appropriate medical data categorization for data mining classification techniques. MED. INFORM., 2002. Vol. 27, no. 1, 59–67, .
[7] Sitar-Taut, V.A., et al., Using machine learning algorithms in cardiovascular disease risk evaluation. Journal of Applied Computer Science & Mathematics, 2009.
[8] Srinivas, K., B.K. Rani, and A. Govrdhan, Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks. International Journal on Computer Science and Engineering (IJCSE), 2010. Vol. 02, No. 02: p. 250-255.
[9] L. Breiman, “Bagging predictors”, Machine Learning, 26, 1996, 123-140.
[10] My Chau Tu, Dongil Shin, Dongkyoo Shin ,“Effective Diagnosis of Heart Disease through Bagging Approach”, 2nd International Conference on Biomedical Engineering and Informatics,2009.
[11] Rajkumar, A. and G.S. Reena, Diagnosis Of Heart Disease Using Datamining Algorithm. Global Journal of Computer Science and Technology, 2010. Vol. 10 (Issue 10).
[12] Cheung, N., Machine learning techniques for medical analysis. School of Information Technology and Electrical Engineering, B.Sc. Thesis, University of Queenland., 2001.
[13] Ratanamahatana , C.A. and D. Gunopulos, Scaling up the Naive Bayesian Classifier: Using Decision Trees for Feature Selection. Proc. Workshop Data Cleaning and Preprocessing (DCAP ’02), at IEEE Int’l Conf. Data Mining (ICDM ’02), 2002.
[14] Ramana, B.V., M.S.P. Babu, and N.B. Venkateswarlu, A critical evaluation of bayesian classifier for liver diagnosis using bagging and boosting methods. International Journal of Engineering Science and Technology, 2011. Vol. 3 No. 4.
[15] My Chau Tu, Dongil Shin, Dongkyoo Shin, “A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms” Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009.
[16] Tsirogiannis, G.L, Frossyniotis, D, Stoitsis, J, Golemati, S, Stafylopatis, A Nikita,K.S,”Classification of Medical Data with a Robust Multi-Level Combination scheme”, IEEE international joint Conference on
Neural Networks.
[17] Kaewchinporn .C, Vongsuchoto. N, Srisawat. A ” A Combination of Decision Tree Learning and Clustering for Data Classification”, 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE).
[18] Pan Wen, “Application of decision tree to identify a abnormal high frequency electrocardiograph”, China National Knowledge Infrastructure Journal, 2000.
[19] Jinyan LiHuiqing Liu, See-Kiong Ng and Limsoon Wong,” Discovery of significant rules for classifying cancer diagnosis data”, Bioinformatics 19(Suppl. 2)Oxford University Press 2003.
[20] Dong-Sheng Cao, Qing-Song Xu ,Yi-Zeng Liang, Xian Chen, “Automatic feature subset selection for decision tree-based ensemble methods in the prediction of bioactivity”, Chemometrics and Intelligent Laboratory Systems.
[21] Liu Ya-Qin, Wang Cheng, Zhang Lu,” Decision Tree Based Predictive Models for Breast Cancer Survivability on Imbalanced Data” , 3rd International Conference on Bioinformatics and Biomedical Engineering , 2009.
[22] Tan AC, Gilbert D. “Ensemble machine learning on gene expression data for cancer classification”, Appl Bioinformatics. 2003;2(3 Suppl):S75-83.
[23] Andreeva, P., Data Modelling and Specific Rule Generation via Data Mining Techniques. International Conference on Computer Systems and Technologies – CompSysTech, 2006.
[24] Sitar-Taut, V.A., et al., Using machine learning algorithms in cardiovascular disease risk evaluation. Journal of Applied Computer Science & Mathematics, 2009.
[25] Wu, X., et al., Top 10 algorithms in data mining analysis. Knowl. Inf. Syst., 2007.
[26] S. K. Yadev & Pal., S. 2012. Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification, World of Computer Science and Information Technology (WCSIT), 2(2), 51-56.
[27] S. K. Yadav, B. K. Bharadwaj & Pal, S. 2011. Data Mining Applications: A comparative study for predicting students’ performance, International journal of Innovative Technology and Creative Engineering (IJITCE), 1(12).
[28] L. Breiman, “Bagging predictors”, Machine Learning, 26, 1996, 123-140.
[29] Kappa at http://www.dmi.columbia.edu/homepages/chuangj/ kappa.
[/accordionitem][/cq_vc_accordion]

Smart Grid Ping – A Customized Ping Tool for a Heterogeneous and Hybrid Smart Grid Communication Network
Authors :- Do Nguyet Quang, Ong Hang See and Ong Xing Jui
Keywords :- Network performance, ping, smart grid, testing tool.
Published Online :- 23 November 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]Heterogeneous and hybrid smart grid communication network is a network that comprises of different communication mediums and technologies. Performance evaluation is one of the main concerns in smart grid communication system. In any smart grid communication implementation, to determine the performance factor of the network, a testing of an end-to-end process flow is required. Therefore, an effective testing tool plays a crucial role in evaluating the performance of smart grid communications. Ping is currently one of the most common network testing tools. In this paper, a customized ping utility, called Smart Grid Ping, is introduced. This utility provides random ping intervals with user selectable distribution, allowing network administrators to test the reachability and availability of various applications in smart grid communication system. [/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2012/07/Smart-Grid-Ping-%E2%80%93-A-Customized-Ping-Tool-for-a-Heterogeneous-and-Hybrid-Smart-Grid-Communication-Network1.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Ken, M. (2010). Hybrid communication networks? The key to meeting smart grid requirements. Retrieved 3 January 2012, from http://www.elp.com/index/display/articledisplay/3852722092/articles/utility-automation-engineering-td/volume-15/issue-10/features/hybridcommunication-
networks-the-key-to-meeting-smart-grid-requirements.html
[2] Jayant, D., Arthur, L., & Mark, M. Smart Choices for the Smart Grid. Retrieved 12 September 2011, from enterprise.alcatel-lucent.com/private/images/public/si/pdf_smartChoice.pdf
[3] Zaballos, A., Vallejo, A., & Selga, J. M. (2011). Heterogeneous Communication Architecture for the Smart Grid. IEEE Network, 25(5), 30-37.
[4] Sauter, T., & Lobashov, M. (2010). End-to-End Communication Architecture for Smart Grid. IEEE Transactions on Industrial Electronics, 58(4), 1218-28.
[5] Zaballos, A., Vallejo, A., & Selga, J. M. (2011). Heterogeneous Communication Architecture for the Smart Grid. IEEE Network, 25(5), 30-37.
[6] Wenye, W, Yi, X., & Mohit, K. (2011). A Survey on the Communication Architectures in Smart Grid. Computer Networks: The International Journal of Computer and Telecommunications Networking, 55(15), 3604-29.
[7] Lichtensteiger, B., Bjelajac, B., Muller, C., & Wietfeild, C. (2010). RF Mesh Systems for Smart Metering: System Architecture and Performance. 2010 First IEEE International Conference on Smart Grid Communications (pp. 379-384). Gaithersburg, Montgomery, Maryland.
[8] Wikipedia: The Free Encyclopedia. Ping (networking utility). Retrieved 17 March 2012, from http://en.wikipedia.org/wiki/Ping_(networking_utility)
[9] Kush, N., Clark, A. J., & Foo, E. (2010). Smart Grid Test Bed Design and Implementation (Master research project, Queensland University of Technology). Retrieved from http://eprints.qut.edu.au/39098/1/39098.pdf
[10] Laverty, D. M., Morrow, D. J., Best, R., & Crossley, P. A. (2010). Telecommunications for Smart Grid: Backhaul solutions for the Distribution Network. 2010 IEEE Power and Energy Society General Meeting (pp. 1-6). Minneapolis, Hennepin, United States.
[11] Pinomaa, A., Ahola, J., & Kosonen, A. (2011). Power-line Communication-based Network Architecture for LVDC Distribution System. 2011 IEEE International Symposium on Power Line Communications and Its Applications (pp. 358-363). Udine.
[12] Wenpeng, L., Sharp, D., & Lancashire, S. (2010). Smart Grid Communication Network Capacity Planning for Power Utilities. 2010 IEEE PES Transmission and Distribution Conference and Exposition (pp. 1-4). New Orleans, Louisiana, United States.
[13] Cuvelier, P. K., & Sommereyns, P. (2009). Proof of concept smart metering. 20th International Conference on Electricity Distribution (pp. 8-11). Prague, Czech Republic.
[14] Quang, D. N., See, O. H., Nga, D. V., Chee, L. L., Xuen, C. Y., & Shashiteran, A. L. K. (2013). Performance Testing Framework in a Heterogeneous and Hybrid Smart Grid Communication Network. Research Journal of Applied Sciences, Engineering and Technology, 6(23), 4506-18.
[/accordionitem][/cq_vc_accordion]

Data Missing Solution Using Rough Set Theory and Swarm Intelligence
Authors :- Ahmed Tariq Sadiq, Mehdi Gzar Duaimi and Samir Adil Shaker
Keywords :- Bees’ Algorithm, Incomplete Databases, Null Values Problem, Rough Set.
Published Online :- 24 June 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]This paper presents a hybrid approach for solving null values problem; it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estimation through working with rough set theory. The results obtained from most code sets show that bees algorithm better than ID3 in decreasing the number of extracted rules without affecting the accuracy and increasing the accuracy ratio of null values estimation, especially when the number of null values is increasing.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/06/Data-Missing-Solution-Using-Rough-Set-Theory-and-Swarm-Intelligence.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Farhangfar, A., Kurgan, L., & Pedrycz,W. (2004). “Experimental analysis of methods for imputation of missing values in databases”. Proc. SPIE 5421, Intelligent ComputingIntelligent Computing: Theory
and Applications II, Orlando, FL.
[2] Marwala,T. (2009). “Computational iIntelligence for missing Data imputation, estimation, and management: knowledge optimization techniques”, Information Science Reference, Hershey, New York.
[3] Aghazadeh, F., & Meybodi, M. (2011). “Learning bees algorithm for optimization”, International Conference on Information and Intelligent Computing, IPCSIT vol. 18, IACSIT Press, Singapore.
[4] Sadiq, A. T., Chawishly, S. A., & Sulaka, N. J. (2012). ”Solving null values problem using intelligent methods”, Journal of Advanced Computer Science and Technology Research, 2(2), 91-103.
[5] Chiu, H., Wei, T., & Lee, H. (2009). ”A novel approach for missing data processing based on compounded PSO clustering”, Journal of WSEAS Transactions on Information Science and Applications, 6(4), 589-600.
[6] Gómez, Y., Bello, R., Puris, A., & García, M. (2008). “Two step swarm intelligence to solve the feature selection problem”, Journal of Universal Computer Science, 14(15), 2582-2596.
[7] Chen, S., & Hsiao, H. (2005). “A new method to estimate null values in relational database systems based On automatic clustering techniques”, International Journal of Information Sciences, 169(1), Elsevier, 47–69.
[8] Chen, S., & Huang, C. (2003). “Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms”, Journal of IEEE Transactions on Fuzzy Systems, 11(4), 495 – 506.
[9] Chen, S., & Yeh, M. (1997). “Generating fuzzy rules from relational database systems for estimating null values”, International Journal of Cybernetics and Systems, 28, Taylor & Francis, 695-723.
[10]Komorowski, J., Polkowski, L., & Skowron, A. (2006). “Rough sets: A tutorial”, Rough Fuzzy Hybridization – A New Tend in Decision Making , (pp 3-98), S. K. Pal, A. Skowron, Eds., springer.
[11]Pawlak, Z. (2004). “Rough sets”, Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44 100 Gliwice, Poland.
[12]Jurka, P., & Zbořil, F. (2005). “Using rough sets in data mining”, Doctoral Degree Programme, Dept. of Intelligent Systems, FIT, BUT.
[13]Pawlak, Z. (1992). “Rough sets theoretical aspects of reasoning about data”. Dordrecht Kluwer Academic Publishers, Norwell, MA, USA.
[14]Bazan, J., Son, N., Skowron, A., & Szczuka, M. (2003). “A view on rough set concept approximations”, international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC), Springer-Verlag Berlin Heidelberg, (pp. 181-188).
[15]Tripathy, H. K., Tripathy, B. K., & Das, P. K. (2008). “An intelligent approach of rough set in knowledge discovery databases ”, International Journal of Electrical and Electronics Engineering , 2(5), 334-337.
[16]Pham, D.T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). “The bees algorithm–A novel tool for complex optimization problems”, In Proceedings of the Intelligent Production Machines and Systems (IPROMS) Conference, (pp. 454-461).
[17]Pham, D.T., & Koc, E. (2010). ”Design of a two-dimensional recursive filter using the bees algorithm”, International Journal of Automation and Computing, 7(3), 399-402.
[18]Berry, M. W., & Browne, M. (2006). ”Lecture notes in data mining”, World Scientific Publishing Co. Pte. Ltd.
[19]Colin. A. (1996). “Building decision trees with the ID3 algorithm”, Journal of Dr. Dobb’s, URL:www.ddj.com/architect/184409907.
[20]Luger, G. F. (2002). “Artificial intelligent, structures and strategies for complex problem solving ”, Pearson Education, (4th ed.).
[/accordionitem][/cq_vc_accordion]

Outlier Detection in Stream Data by Machine Learning and Feature Selection Methods
Authors :- Hossein Moradi Koupaie, Suhaimi Ibrahim and Javad Hosseinkhani
Keywords :- Outlier Detection, Stream Data, Framework, Support Vector Machine.
Published Online :- 24 June 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]In recent years, intrusion detection has emerged as an important technique for network security. Machine learning techniques have been applied to the field of intrusion detection. They can learn normal and anomalous patterns from training data and via Feature selection improving classification by searching for the subset of features which best classifies the training data to detect attacks on computer system. The quality of features directly affects the performance of classification. Many feature selection methods introduced to remove redundant and irrelevant features, because raw features may reduce accuracy or robustness of classification. Outlier detection in stream data is an important and active research issue in anomaly detection. Most of the existing outlier detection algorithms has less accurate because use some clustering method. Some data are so essential and secretary. Therefore, it needs to mine carefully even if spend cost. This paper presents a framework to detect outlier in stream data by machine learning method. Moreover, it is considered if data was high dimensional. This method is more accurate from other preferred models, because machine learning method is more accurate of other methods..8.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/06/Outlier-Detection-in-Stream-Data-by-Machine-Learning-and-Feature-Selection-Methods.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1][1] Jin, W., Tung, K.H. and Han,J., (2001). Mining top-n local outliers in large databases.In Proc.2001 ACMSIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’01), pp. 293–298,San Fransisco, CA, Aug. 2001.
[2] Babu, S. and Widom,J., (2001). Continuous queries over data streams.SIGMOD Record, 30:109–120..
[3] Charu C. Aggarwal, Philip S. Yu, (2001). Outlier detection for high dimensional data, Proc. of the 2001 ACM SIGMOD int. conf. on Management of data, p.37-46, May 21-24, 2001, Santa Barbara, California, United States
[4] Babcock, B., Babu, S. , Datar, M. , Motwani, R., & Widom,J. (2002). Models and issues in data stream systems. In Proc. 2002 ACM Symp. Principles of Database Systems (PODS’02), pages 1–16, Madison, WI, June 2002.
[5] Gibbons, P.B., & Matias, Y.(1998). New sampling-based summary statistics for improving approximate query answers. In Proc. 1998 ACM-SIGMOD Int. Conf.ManagementofData (SIGMOD’98), pages 331–342, Seattle,WA, June 1998.
[6] Knorr, E., & Ng, R, (1997). A unified notion of outliers: Properties and computation. In Proc. 1997 Int. Conf. Knowledge Discovery and Data Mining (KDD’97), pp. 219–222, Newport Beach, CA, Aug. 1997.
[7] Chandola, V., Banerjee, A., & Kumar, V., (2009). Anomaly detection: A survey. ACM Computing Surveys, 41:1–58.
[8] Chandrasekaran, S., & Franklin, M., (2002). Streaming queries over streaming data.In Proc.2002 Int. Conf. Very Large Data Bases (VLDB’02), pages 203–214, Hong Kong, China, Aug. 2002.
[9] Babcock, B., Babu, S., Datar, M., Motwani, R. & Widom, J. (2002). Models and issues in data stream systems. In Proc. 2002 ACM Symp. Principles of Database Systems (PODS’02), pages 1–16, Madison, WI, June 2002.
[10] Muthukrishnan, S. (2003). Data streams: algorithms and applications. In Proc. 2003 Annual ACMSIAM Symp. Discrete Algorithms (SODA’03), pages 413–413, Baltimore, MD,Jan. 2003.
[11] R. Kohavi, G.H. John. (1997). Wrappers for feature subset selection, Artificial Intelligence 97 (1–2), 273–324.
[12] R.S. Kulkarni, G. Lugosi, V.S. Santosh. (1998). Learning pattern classification—a survey, IEEE Transaction on Information Theory 44 (6), 2178–2206.
[13] R.S. Kulkarni, M. Vidyasagar. (1997). Learning decision rules for pattern classification under a family of probability measures, IEEE Transactions on Information Theory 43 (1) 154–166.
[14] Liang, Z., Lia, Y. (2009). Incremental support vector machine learning in the primal and applications. Neurocomputing 72(10-12), 2249–2258.
[15] Zheng, J., Yu, H., Shen, F., Zhao, J. (2010). An Online Incremental Learning Support Vector Machine for Large-scale Data. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010. LNCS, vol. 6353, pp.
76–81. Springer, Heidelberg.
[16] Liu, X., Zhang, G., Zhan, Y., Zhu, E. (2008). An Incremental Feature Learning Algorithm Based on Least Square Support Vector Machine. In: Preparata, F.P., Wu, X., Yin, J. (eds.) FAW 2008. LNCS, vol. 5059, pp. 330–338. Springer, Heidelberg.
[17] Vapnik, V. (1999). The nature of statistical learning theory. Springer, New York.
[18] Ruping, S. (2002): Incremental learning with support vector machines. Technical Report TR-18, Universitat Dortmund, SFB475.
[19] Nguyen, H., Franke, K., & Petrovic, S. (2010, February). Improving effectiveness of intrusion detection by correlation feature selection. InAvailability, Reliability, and Security, 2010. ARES’10 International Conference on (pp. 17-24). IEEE.
[20] G. Gu, P. Fogla, D. Dagon, W. Lee, and B. Skoric. (2006). Towards an information-theoretic framework for analyzing intrusion detection systems. In Proceedings of the 11th European Symposium on Research in Computer Security (ESORICS’06), September.
[21] I. Guyon, S. Gunn, M. Nikravesh and L.A. Zadeh. (2005). Feature Extraction: Foundations and Applications. Series Studies in Fuzziness and Soft Computing, Springer.
[22] H. Liu, H. Motoda. (2008). Computational Methods of Feature Selection. Chapman & Hall/CRC.
[/accordionitem][/cq_vc_accordion]

Outlier Detection in Stream Data by Clustering Method
Authors :- Hossein Moradi Koupaie, Suhaimi Ibrahim and Javad Hosseinkhani
Keywords :- Outlier Detection, Stream Data, Clustering Method, Efficient Algorithm.
Published Online :- 24 June 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]The fundamental and active research problem in a lot of fields is outlier detection. It is involved many applications. A lot of these methods based on distance measure. But for stream data these methods are not efficient. Most of the previous work on outlier detection declares online outlier and these have less accuracy and it may be lead to a wrong decision. moreover the exiting work on outlier detection in data stream declare a point as an outlier/inlier as soon as it arrive due to limited memory resources as compared to the huge data stream, to declare an outlier as it arrive often can lead us to a wrong decision, because of dynamic nature of the incoming data. The aim of this study is to present an algorithm to detect outlier in stream data by clustering method that concentrate to find real outlier in period of time. It is considered some outlier that has received in previous time and find out real outlier in stream data. The accuracy of this method is more than other methods.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/06/Outlier-Detection-in-Stream-Data-by-Clustering-Method.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Elahi, M. L., Xinjie ; Nisar, Wasif ; Khan, Imran Ali2 ; Qiao, Ying ; Wang, Hongan (2008). DB-Outlier detection algorithm using divide and conquer approach over dynamic DataStream. International Conference on Computer Science and Software Engineering, CSSE 2008. Wuhan, Hubei, China, IEEE
Computer Society, 445 Hoes Lane – P.O.Box 1331, Piscataway, NJ 08855-1331, United States. 4: 438-443.
[2] Elahi, M. K., Li ;Nisar, Wasif2 ; Xinjie, Lv1 ; Hongan, Wang (2009). Detection of local outlier over dynamic data streams using efficient partitioning method. 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009. Los Angeles, CA, United states, IEEE Computer Society, 445
Hoes Lane – P.O.Box 1331, Piscataway, NJ 08855-1331, United States. 4: 78-81.
[3] Ren, J. W., Qunhui ; Zhang, Jia ; Hu, Changzhen (2009). Efficient outlier detection algorithm for heterogeneous data streams. 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009.Tianjin, China, IEEE Computer Society, 445 Hoes Lane – P.O.Box 1331, Piscataway, NJ
08855-1331, United States. 5: 259-264
[4] Zhang, J. G., Qigang ; Wang, Hai (2008). SPOT: A system for detecting projected outliers from highdimensional data streams. 2008 IEEE 24th International Conference on Data Engineering, ICDE’08. Cancun, Mexico, Inst. of Elec. and Elec. Eng. Computer Society, 445 Hoes Lane – P.O.Box 1331,
Piscataway, NJ 08855-1331, United States: 1628-1631.
[5] Bakar, Z. A., Mohemad, R., Ahmad, A., & Deris, M. M.(2006). A comparative study for outlier detection techniques in data mining. In Proc. 2006 IEEE Conf. Cybernetics and Intelligent Systems, pp. 1– 6, Bangkok, Thailand.
[6] Babcock, B., Babu, S. , Datar, M. , Motwani, R., & Widom,J. (2002). Models and issues in data stream systems. In Proc. 2002 ACM Symp. Principles of Database Systems (PODS’02), pages 1–16, Madison, WI, June 2002.
[7] Babu, S. and Widom,J., (2001). Continuous queries over data streams.SIGMOD Record, 30:109–120.
[8] Charu C. Aggarwal, Philip S. Yu, (2001). Outlier detection for high dimensional data, Proc. of the 2001 ACM SIGMOD int. conf. on Management of data, p.37-46, May 21-24, 2001, Santa Barbara, California, United States.
[9] Babcock, B., Babu, S., Datar, M., Motwani, R. & Widom, J. (2002). Models and issues in data stream systems. In Proc. 2002 ACM Symp. Principles of Database Systems (PODS’02), pages 1–16, Madison, WI, June 2002.
[10] Muthukrishnan, S. (2003). Data streams: algorithms and applications. In Proc. 2003 Annual ACMSIAM Symp. Discrete Algorithms (SODA’03), pages 413–413, Baltimore, MD,Jan. 2003.
[11] Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97(1), 273-324.
[12] R.S. Kulkarni, G. Lugosi, V.S. (1998). Santosh, Learning pattern classification—a survey, IEEE Transaction on Information Theory 44 (6), 2178–2206.
[13] R.S. Kulkarni, M. Vidyasagar. (1997) . Learning decision rules for pattern classification under a family of probability measures, IEEE Transactions on Information Theory 43 (1) 154–166.
[14] Liang, Z., Lia, Y. (2009). Incremental support vector machine learning in the primal and applications. Neurocomputing 72(10-12), 2249–2258.
[15] Zheng, J., Yu, H., Shen, F., Zhao, J. (2010). An Online Incremental Learning Support Vector Machine for Large-scale Data. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010. LNCS, vol. 6353, pp.
76–81. Springer, Heidelberg.
[16] Liu, X., Zhang, G., Zhan, Y., Zhu, E. (2008). An Incremental Feature Learning Algorithm Based on Least Square Support Vector Machine. In: Preparata, F.P., Wu, X., Yin, J. (eds.) FAW 2008. LNCS, vol. 5059, pp. 330–338. Springer, Heidelberg.
[17] Vapnik, V. (1999). The nature of statistical learning theory. Springer, New York.
[18] Ruping, S. (2002). Incremental learning with support vector machines. Technical Report TR-18, Universitat Dortmund, SFB475.
[19] Elahi, Manzoor, et al. (2008). “Efficient clustering-based outlier detection algorithm for dynamic data stream.” Fuzzy Systems and Knowledge Discovery, 2008. FSKD’08. Fifth International Conference on. Vol. 5. IEEE.
[20] Thakran, Yogita, and Durga Toshniwal. (2012). “Unsupervised outlier detection in streaming data using weighted clustering.” Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on. IEEE.
[21] Angiulli, F. and Fassetti, F. (2007). Detecting distance-based outliers in streams of data. In Proc. of the Sixteenth ACM Conf. on information and Knowledge Management (Lisbon, Portugal, November 2007). CIKM ’07.
[22] Pokrajac, D. Lazarevic, A.Latecki, L.J. (2007). Incremental Local Outlier Detection for Data Streams Computational Intelligence and Data Mining 07. CIDM.
[23] Knorr, E. M., Ng, R. T. (1998). Algorithms for Mining Distance-Based Outliers in Large Datasets, Proc. 24th VLDB.
[24] M.M. Breunig, H.P.Kriegel, R.T. Ng and J.Sander. (2000). LOF: Identifying Density-Based Local Outliers ACM SIGMOD.
[25] Wen Jin and Anthony K. H. Tung and Jiawei Han. (2001). Mining top-n local outliers in large databases. Pages 293-298.
[26] Ramaswamy S., Rastogi R., Kyuseok S. ( 2000). Efficient Algorithms for Mining Outliers from Large Data Sets, Proc. ACM SIDMOD Int. Conf. on Management of Data.
[27] Fabrizio Angiulli & Clara Pizzuti. (2002). Fast Outlier Detection in High Dimensional Spaces, Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, p.15- 26, August 19-23.
[28] F. Angiulli, S. Basta, and C. Pizzuti. (2006). Distance-based detection and prediction of outliers. IEEE Transaction on Knowledge and Data Engineering, 18(2):145(160, February 2006).
[29] M. F. Jiang, S. S. Tseng, C. M. Su. (2001). Two-phase clustering process for outliers detection. Pattern Recognition Letters, 22(6/7): 691-700.
[30] Charu C. Aggarwal, Philip S. Yu.( 2001). Outlier detection for high dimensional data, Proc. of the 2001 ACM SIGMOD int. conf. on Management of data, p.37-46, May 21-24 , Santa Barbara, California, United States.
[31] E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo. (2002) A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data. In Data Mining for Security Applications.
[32] A. Arning, R. Agrawal, P. Raghavan. (1996). A linear method for deviation detection in large databases. In: Proc of KDD’96, 164 169.
[33] S. Harkins, H. He, G. J.Willams, R. A. Baster. (2002). Outlier detection using replicator neural networks. In: Proc of DaWaK’02, 170-180.
[/accordionitem][/cq_vc_accordion]

Hash Function of Finalist SHA-3: Analysis Study
Authors :- Imad Fakhri Al-shaikhli, Mohammad A. Alahmad and Khanssaa Munthir
Keywords :- Complexity, FSMFHF, Performance, Security.
Published Online :- 19 April 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]The National Institute of Standard and Technology (NIST) has suggested different principles for hash functions to avoid the blunders and to choice the ideal quality of hash function, which to be a measurement for the future of hash function generations. Therefore, the goal of the NIST contenders in SHA-3 between the hash functions is to be chosen as the winner in the end of 2012, and the beginning of 2013. Thus, for this reason the paper addresses the comparative and analysis study of the finalist SHA-3 candidates in: complexity of security, design and structure, as well as performance and cost, to measure the robustness of the algorithms in this area, through the Fundamentals Security Measurement Factors of Hash Function (FSMFHF) of Secure Hash Algorithm (SHA). Therefore, main idea from this comparison and analysis study between the finalist of SHA-3 candidates such as (BLAKE, Grostl, JH, Keccak, and Skein) is to investigate the tight security in the suitable designs of lightweight such as JH and Keccak for the future security of hash function. Moreover, they are investigating the high trade-off in (speed/memory) that implemented in Virtex-7 2000T of FPGAs family hardware. Whereas, excluded the rest of hash functions in this finalist, which are not investigated all the measurements of hash function as mentioned above.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/04/Hash-Function-of-Finalist-SHA-3-Analysis-Study.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Andreeva E. Mennink B. Preneel B. & Skrobot M. (2012), Security Analysis and Comparison of the SHA-3 Finalists BLAKE, Grostl, JH, Keccak, and Skein.fromKatholiekeUniversiteit Leuven.
[2] Belgium. E. B. Kavun& T. Yalcin (2012), On the Suitability of SHA-3 Finalists for Lightweight Applications. from Horst Görtz Institute, Ruhr University, Chair of Embedded Security, Germany.
[3] Ewan F. Christian F. and Michael G.(2008), The Twister Hash Function Family. Publishing Article, Retrieved in October 28, 2008,
[4] Elbirt J. (2009), Understanding and Applying Cryptography and Data Security. Book ISBN 978-1-4200-6160-4 (alk. paper).
[5] Imad Fakhri Alshaikhli, Mohammad A. Ahmad, Hanady Mohammad Ahmad (2012). “Protection of the Texts Using Base64 and MD5.” JACSTR Conference Vol 2, No 1 (2012)(1): 12.
[6] Imad Fakhri Al Shaikhli, A. M. Z., Rusydi H. Makarim, and Al-Sakib Khan Pathan (2012). “Protection of Integrity and Ownership of PDF Documents Using Invisible Signature.” UKSim 14th International Conference on Computer Modelling and Simulation: 533–537.
[7] Homsirikamol E. Rogawski M. & K. Gaj (2010), Comparing Hardware Performance of Fourteen Round Two SHA-3 Candidates Using FPGAs. Retrieved December 21, 2010, from George Mason University.
[8] Namin A. H. & Hasan M. A. (2010), Implementation of the Compression Function for Selected SHA-3 Candidates on FPGA. Retrieved Feb. 25th, 2010, Conference Publications
[9] Regenscheid A. Perlner R. Chang S. Kelsey J. Nandi M. & Paul S. (2009), Status Report on the First Round of the SHA-3 Cryptographic Hash Algorithm Competition. Retrieved September 2009, from National Institute of Standards and Technology, U.S. Department of Commerce. NIST Interagency Report 7764
[10] Rechberger C. (2010), Second-Preimage Analysis of Reduced SHA-1, from Katholieke Universiteit Leuven, Department of Electrical Engineering. Publishing paper.
[11] Silva J. E. (2003), An Overview of Cryptographic Hash Function and Their Uses. from the SANS Institute Reading Room site. Retrieved January 15, 2003.
[12] Schorr (2010), Performance Analysis of a Scalable Hardware FPGA Skein Implementation. Retrieved February 2010, thesis of Master Degree from Kate Gleason College of Engineering Department of Computer Engineering Rochester, New York.
[/accordionitem][/cq_vc_accordion]

Criminal Communities Mining on the Web
Authors :- Javad Hosseinkhani, Suriayati Chuprat, Hamed Taherdoost, Morteza Harati Cool and Sadegh Emami Korani
Keywords :- Crime Web Mining, Concurrent Crawler, Terrorist Network, Social Network, Forensics Analysis.
Published Online :- 19 April 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]Criminal web data always offer novel and useful knowledge and information for Law administration. The used digital data in legal assessments are involved parts of information about the accused’ social networks. Thus, evaluation of these parts of information is challenging. Therefore, an investigator has to pull out the appropriate information from the text manually, these information are on the website. An investigator also makes the relationship between various parts of information and classify them in to a structured database so the category is ready to use in different criminal network evaluation tools for analysis. On the other hand, these manual processes are not adequate in the case that it has many errors. Moreover, as the quality of resulted evaluation depends on the investigator’s proficiency, the reliability is not stable. On another word, the more proficient operator, the better result achieved. The purpose of this paper is suggesting a framework by using concurrent crawler to show the process of exploring the criminal accused of legal data evaluation which insures the reliability gap.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/04/Criminal-Communities-Mining-on-the-Web.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] U.M. Fayyad and R. Uthurusamy, “Evolving Data Mining into Solutions for Insights,” Comm. ACM, Aug. 2002, pp. 28-31.
[2] W. Chang et al., “An International Perspective on Fighting Cybercrime,” Proc. 1st NSF/NIJ Symp. Intelligence and Security Informatics, LNCS 2665, Springer-Verlag, 2003, pp. 379-384.
[3] Kaur, P. G., Raghu ; Singh, Ravinder ; Singh, Mandeep (2012). Research on the application of web mining technique based on XML for unstructured web data using LINQ. 2011 7th International Conference on MEMS, NANO and Smart Systems, ICMENS 2011. Kuala Lumpur, Malaysia, Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670, Germany. 403-408: 1062-1067.
[4] Xu, J.J., Chen, H.: CrimeNet Explorer: A framework for criminal network knowledge discovery. ACM Transactions on Information Systems 23(2), 201–226 (2005)
[5] Peng Tao, “Research on Topical Crawling Technique for Topic- Specific Search Engine,” Doctor degree thesis of Jilin University, 2007.
[6] Jiang Peng and Song Ji-hua, “A Method of Text Classifier for Focused Crawler,” JOURNAL OF CHINESE INFORMATION PROCESSING, vol. 26, pp. 92-96 Nov. 2010.
[7] Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M. Crime data mining: a general framework and some examples. Computer 2004;37(4):50–6.
[8] Yang CC, Ng TD. Terrorism and crime related weblog social network: link, content analysis and information visualization. In: IEEE international conference on intelligence and security informatics (ISI);2007. p. 55–8.
[9] Hope T, Nishimura T, Takeda H. An integrated method for social network extraction. In: Proc. Of the 15th international conference on world wide web (WWW); 2006. p. 845–6.
[10] Jin W, Srihari RK, Ho HH. A text mining model for hypothesis generation. In: Proc. Of the 19th IEEE international conference on tools with artificial intelligence ICTAI; 2007. p. 156–62.
[11] Zhou D, Manavoglu R, Li J, Giles CL, Zha H. Probabilistic models for discovering e-communities. In: Proc. of the 15th international conference on world wide web (WWW); 2006. p. 173–82.
[12] Jin Y, Matsuo Y, Ishizuka M. Ranking companies on the web using social network mining. In: Ting IH,Wu HJ, editors.Web mining applications in e-commerce and e-services. Studies in computational intelligence, vol. 172. Berlin/Heidelberg: Springer; 2009. p. 137–52.
[13] Srinivasan P. Text mining: generating hypotheses from medline.Journal of the American Society for Information Science and Technology 2004; 55:396–413.
[14] Skillicorn DB, Vats N. Novel information discovery for intelligence and counterterrorism. Decision Support Systems 2007;43(4): 1375–82.
[15] Al-Zaidy, R. F., Benjamin C.M.; Youssef, Amr M ; Fortin, Francis (2012). “Mining criminal networks from unstructured text documents.” Concordia Institute for Information Systems Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, CIISE (EV7.640), Montreal, QC H3G 1M8, Canada 8: 147-160.
[16] Sparrow, M.K. The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks 13 (1991), 251–274.
[17] Krebs, V. E. Mapping networks of terrorist cells. Connections 24, 3 (2001), 43–52.
[18] Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems. ACM Trans. Program. Lang. Syst. 15, 5 (Nov. 1993), 795-825. DOI= http://doi.acm.org/10.1145/161468.16147.
[19] Ding, W. and Marchionini, G. 1997. A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
[20] Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (The Hague, The Netherlands, April 01 – 06, 2000). CHI ’00. ACM, New York, NY, 526-531. DOI= http://doi.acm.org/10.1145/332040.332491.
[21] Tavel, P. 2007. Modeling and Simulation Design. AK Peters Ltd., Natick, MA.
[22] Sannella, M. J. 1994. Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
[23] Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar.2003), 1289-1305.
[24] Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE. In Proceedings of the 16th Annual ACM Symposium on User Interface Software and Technology (Vancouver, Canada, November 02 – 05, 2003). UIST ’03. ACM, New York, NY, 1-10.
[25] Yu, Y. T. and Lau, M. F. 2006. A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions. J. Syst. Softw. 79, 5 (May. 2006), 577-590.
[26] Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender, Ed. ACM Press Frontier Series. ACM, New York, NY, 19-33.
[27] Hosseinkhani, J, Chuprat. S, and Taherdoost. H. (2012). Criminal Network Mining by Web Structure and Content Mining, Advances in Remote Sensing, Finite Differences and Information Security, Prague, Czech Republic, September 24-26, 210-215.
[/accordionitem][/cq_vc_accordion]

Hybrid Intelligent Techniques for Text Categorization
Authors :- Ahmed T. Sadiq and Sura Mahmood Abdullah
Keywords :- Rough Set Theory; Text Categorization; Text Mining.
Published Online :- 19 April 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]Text categorization is the task in which text documents are classified into one or more of predefined categories based on their contents. This paper shows that the proposed system consists of three main steps: text document representation, classifier construction and performance evaluation. In the first step, a set of pre-classified text documents is provided. Each text document is initially preprocessed in order to be split into features, these features are weighted based on the frequency of each feature in that text document and eliminate the non-informative features. The remaining features are next standardized by reducing a feature to its root using the stemming process. Due to the large number of features even after the non-informative features removal and the stemming process, the proposed system applies specific thresholds to extract distinct features which represent that text document. In the second step, the text categorization model (classifier) is built by learning the distinct features which represent all the pre-classified text documents for each sub-category of main categories; this process can be achieved by using one of the supervised categorization techniques that is called the rough set theory. Thereafter, the model uses a pair of precise concepts from the above theory that are called the lower and upper approximations to classify any test text document into one or more of main categories and sub-categories. In the final step, the performance of the proposed system is evaluated. It has achieved good results up to 96%, when applied to a number of test text documents for each sub-category of main categories.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/04/Hybrid-Intelligent-Techniques-for-Text-Categorization.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Hotho, A., Nürnberger, A. & Paaß, G. (May 2005). A Brief Survey of Text Mining. LDV Forum – GLDV Journal for Computational Linguistics and Language Technology, Vol. 20, No. 1, pp. 19-62.
[2] Korde, V.& Mahender, C. (March 2012). Text Classification and Classifiers: A Survey. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 3, No. 2, pp. 85-99.
[3] Ruiz, M. (December 2001). Combining Machine Learning and Hierarchical Structures for Text Categorization. PhD Thesis, Computer Science Dept., University of Iowa, Iowa City, Iowa, USA.
[4] Karamcheti, A. (May 2010). A Comparative Study on Text Categorization. M.Sc Thesis, University of Nevada, Las Vegas.
[5] Takamura, H. (March 2003). Clustering Approaches to Text Categorization. PhD Thesis, Information Processing Dept., Graduate School of Information Science, Nara Institute of Science and Technology, Japan.
[6] Nigam, K. (May 2001). Using Unlabeled Data to Improve Text Classification. PhD Thesis, School of Computer Science, Carnegie Mellon University, USA.
[7] Lee, K. (September 2003). Text Categorization with a Small Number of Labeled Training Examples. PhD Thesis, School of Information Technologies, University of Sydney, Australia.
[8] Ifrim, G. (February 2005). A Bayesian Learning Approach to Concept-Based Document Classification. M.Sc Thesis, Computer Science Dept., Saarland University, Saarbrücken, Germany.
[9] Radhi, A. (June 2006). Machine Learning for Text Categorization. PhD Thesis, Computer Science Dept., University of Technology, Baghdad, Iraq.
[10] Wanas, N., Said, D., Hegazy, N. & Darwish, N. (December 2006). A Study of Local and Global Thresholding Techniques in Text Categorization. proceedings of the 5th Australasian Data Mining Conference (AusDM), Sydney, Australia, Vol. 61, pp. 91-101.
[11] Granitzer, M. (October 2003). Hierarchical Text Classification Using Methods from Machine Learning. M.Sc Thesis, Institute of Theoretical Computer Science (IGI), Graz University of Technology, Austria.
[12] Addis, A. (March 2010). Study and Development of Novel Techniques for Hierarchical Text Categorization. PhD Thesis, Electrical and Electronic Engineering Dept., University of Cagliari, Italy.
[13] Sebastiani, F. (1999). A Tutorial on Automated Text Categorization. proceedings of the 1st Argentinian Symposium on Artificial Intelligence (ASAI), Buenos Aires, AR, pp. 7-35.
[14] Sebastiani, F. (March 2002). Machine Learning in Automated Text Categorization. ACM Computing Surveys, Italy, Vol. 34, No. 1, pp. 1-47.
[15] Feldman, R.& Sanger, J. (October 2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, USA, New York.
[16] Pan, F. (September 2006). Multi-Dimensional Fragment Classification in Biomedical Text. M.Sc Thesis, Queen’s University, Kingston, Ontario, Canada.
[17] Komorowski, J., Pawlak, Z., Polkowski, L. & Skowron, A. (1999). Rough Sets: A Tutorial. In : Pal, S.K., Skowron, A. (Eds) Rough-Fuzzy Hybridization :A New Trend in Decision Making, pp. 3-98, Springer-Verlag, Singapore.
[18] Pawlak, Z. (2004). Some Issues on Rough Sets. Transactions on Rough Sets I, Lectures Notes in Computer Science (LNCS) 3100, Springer-Verlag Berlin Heidelberg, Vol. 1, pp. 1-58.
[19] Suraj, Z. (December 2004). An Introduction to Rough Set Theory and Its Applications: A Tutorial. proceeding of the 1st International Computer Engineering Conference (ICENCO’) New Technologies for the Information Society, Cairo, Egypt, pp. 1-39.
[20] Pawlak, Z. (March 2002). Rough Set Theory and Its Applications. Journal of Telecommunications and Information Technology, pp.7-10.
[21] Kiritchenko, S. (2005). Hierarchical Text Categorization and Its Application to Bioinformatics. PhD Thesis, School of Information Technology and Engineering, Faculty of Engineering, University of Ottawa, Ottawa, Canada.
[/accordionitem][/cq_vc_accordion]

Hierarchical Structural Driven Model for Integrative Information Management Architecture
Authors :- Mohd Izzuddin Mohd Tamrin, Tengku Mohd Tengku Sembok and Mira Kartiwi
Keywords :- Intermediary processes, semi-automated case base reasoning, XML/XPath driven support models.
Published Online :- 04 April 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]Heavy reliance on the other nodes in a complex supply chain network and failure to deliver the specialized assignment by merely one node can caused disruption to the entire processes. The authors introduce the Integrative Information Management Architecture (IIMA) to generate semi-automated assistance against common supply chain deviations. Support models are used and they are made from tree-like structure in XML documents. The recommendations are stored in the leaf nodes whereas the intermediary nodes are filled with characteristics of the deviation. The IIMA employed the XPath together with a series of questions to navigate through the relevant path. We had developed the prototype of the IIMA but run it in simulated environments. The results showed that the group with IIMA support improved the number of completed processes compared to without the IIMA support.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/04/Hierarchical-Structural-Driven-Model-for-Integrative-Information-Management-Architecture.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] Bowersox, D.J., Closs, D.J., & Drayer, R.W. (2005). The digital transformation: Technology and beyond. Supply Chain Management Review, 9(1), 22-29.
[2] Devaraj, S., Krajewski, L., & Wei, J.C. (2007). Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain. Journal of Operations Management, 25(6), 1199-1216.
[3] Gadde, L., & Hulthen, K. (2008). Improving logistics outsourcing through increasing buyer-provider interaction. Industrial Marketing Management, 38(6), 633-640.
[4] Cassivi, L. (2006). Collaboration planning in a supply chain. Supply Chain Management: An International Journal, 11(3).
[5] Lusch, R.F., Vargo, S.L., & O’Brien, M. (2007). Competing through service: Insight from servicedominant logic. Journal of Retailing, 1(83), 5-18.
[6] Delen, D., Hardgrave, B.C., & Sharda, R. (2007). RFID for better supply chain management through enhanced information visibility. Production and Operation Management, 16(5), 613-624.
[7] Cheung, C.F., Cheung, C.M., & Kwok, S.K. (2012). A knowledge-based customization system for supply chain integration. Expert System with Application, 39, 3906-3924.
[8] Huang, Y., Williams, B.C., & Zheng, L. (2011). Reactive, model-based monitoring in RFID-enabled manufacturing, Computer in Industry, 62, 811-819.
[9] Kelepouris, T., Pramatari, K., & Doukidis, G. (2007). RFID-enabled traceability in the food supply chain. Industrial Management and Data Systems, 107(2), 183-200.
[10] Chow, H.K., Choy, K., & Lee, W. (2007). A dynamic logistics process knowledge-based system: An RFID multi-agent approach. Knowledge-Based Systems, 20(4), 357-372.
[11] Soroor, J., Tarokh, M.J., & Shemshadi, A. (2009). Initiating a state of the art system for real-time supply chain coordination. European Journal of Operational Research, 196(2), 635-650.
[12] Ribeiro, L., Barata, J., & Colombo, A. Supporting agile supply chains using a service-oriented shop floor. Engineering Applications of Artificial Intelligence, 22, 6 (2009), 950-960.
[13] Cheng, J.C., Law, K.H., Bjornsson, H., Jones, & A., Sriram, R. (2010). A service oriented framework for construction supply chain integration. Automation in Construction, 19(2), 245-260.
[14] Tarantilis, C., Kiranoudis, C., & Theodorakopoulos, N. (2008). A Web-Based ERP System for Business Services and Supply Chain Management: Application to Real-World Process Scheduling. European Journal of Operational Research, 187(3), 1310-1326.
[15] Wu, C.C., Jian, M.S., & Chou, T.Y. (2011). Environment Affection Based RFID Potantial Bio-Disease Tracking/Tracing System, WSEAS Transactions on Systems, 10(2), 38-48.
[/accordionitem][/cq_vc_accordion]

Evaluation of Usability Problems of Labor Portal in Saudi Arabia
Authors :- Haifa Fahad AL-Zuabi and Imad Fakhri AL-Shaikhli
Keywords :- Evaluation, Usability, Labor Portal Quality, Experts’ Evaluation
Published Online :- 04 April 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]The purpose of this research was to evaluate the quality of Labor portal. Ministry of Labor focused on over the past years to establish a database of the labor market in the Kingdom of Saudi Arabia. The data includes workers in Saudi private sector, whether Saudis or expatriates where mechanization included all labor of 39-office work in various regions. The governorates of the Kingdom operates IT management to develop this rule to get the information and data related to the labor market quickly and accurately. 7 experts evaluated the quality portal of Saudi Arabia Ministry of Labor using self-developed instrument containing eight factors from the literature review on 5-likert scale. The result of the study shows that, the Labor portal quality is fairly good by 78.2% and it needs further improvement and development by 21.8%. Suggestions were made to the Saudi government, particularly, the Saudi Ministry of labor that, there is a need to further test the portal empirically based on the experts’ evaluation.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/04/Evaluation-of-Usability-Problems-of-Labor-Portal-in-Saudi-Arabia.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] MCIT. (2009). ICT indicators in the Kingdom of Saudi Arabia. Accessed 1 September, 2012 from http://www.mcit.gov.sa/english.
[2] Maheshwari, B. V., Kumar, U. K., & V. Sharan, V. (2010). Framework for e-government portal development, in e-government development and diffusion: Inhibitors and facilitators of digital democracy. Hershey, PA, Information Science.
[3] Stowers, G. (2001). Commerce comes to government on the desktop: E-commerce applications in the public sector. The Price water house Coopers Endowment for the Business of Government, Arlington, VA., U.S.A.
[4] Shelly, G. B., Napier, H. A., & Rivers, O. N. (2010). Discovering the Internet: Complete concepts and techniques, 3 ed., Boston, USA: Mass Course Technology/Cengage Learning.
[5] Van Brakel, P. (2003). Information portals: A strategy for importing external content. Electronic Library, vol. 21, no. 16, pp. 591-600.
[6] Alanezi, M. A., Kamil, A. & Basri, S. (2010). A proposed instrument dimensions for measuring egovernment service quality. International Journal of u- and e- Service, Science and Technology, vol. 3, no. 4, pp. 1-18.
[7] Kim, J. H., Kim, M., & Kandampully, J. (2009). Buying environment characteristics in the context of eservice. European Journal of Marketing, vol. 43, no. 9/10, pp. 1188-1204.
[8] Bringula, R. P., & Basa, R. S. “Factors Affecting Faculty Web Portal Usability,” Educational Technology & Society, vol. 14 , no. 4, p. 253–265, 2011.
[9] Shachaf, P., Oltmann, S. M., & Horowitz, S. (2008). Service equality in virtual reference. Journal of the American Society for Information Science and Technology, vol. 4, no. 535-50, p. 59.
[10] Obi, M. C. (2009). Development and Validation of a scale for measuring e-government user satisfaction. UMI Dissertation Publishing, ProQuest LLC, Nova Southeastern University.
[11] Yang, Z., Cai, S., Zhou, Z., & Zhou, N. (2005). Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Information & Management, vol. 42, pp. 575-589.
[12] Madu, C. N., & A. A. Madu, A. A. (2002). Dimensions of e-quality. International Journal of Quality & Reliability Management, vol. 19, no. 3, pp. 246-259.
[13] Greenberg, G., Fitzpatrick, G., Gutwin, C., & Kaplan, S. (2000). Adapting the locales framework for heuristic evaluation of groupware. Australian Journal of Information Systems, vol. 7, p. 102–108.
[14] Nielsen, J. (1994). Heuristic evaluation: Usability inspection methods. New York, USA: John Wiley.
[15]Nielsen,J.(1997). Nielsen normal group. Accessed 1 September, 2012 from
http://www.useit.com/papers/web_discount_usability.html.
[16] Chen, S. Y., & Macredie, R. D. (2005). The Assessment of Usability of Electronic Shopping: A heuristic evaluation. International Journal of Information Management, vol. 25, no. 6, pp. 516-532.
[17] Baker, K., Greenberg, S., & Gutwin, C. (2001). Heuristic evaluation of groupware based on the mechanics of collaboration. The eighth IFIP working conference on engineering for human– computer interaction, Toronto.
[18] Fu, L., Salvendy, G., & Turley, L. (2002). Effectiveness of user testing and heuristic evaluation as a function of performance classification. Behaviour & Information Technology, vol. 21, pp. 137-143.
[19] Zaphiris, P., Dellaporta, A., & Mohamedally, D. (2006). User needs analysis and evaluation of portals: People, processes and technology. A. Cox, Ed., London, UK: Facet, p. 249.
[20] Alfarraj, O., Drew, S., & Alghamdi, R. (2011). E-government stage model: Evaluating the rate of web development progress of government websites in Saudi Arabia. International Journal of Advanced Computer Science and Applications, vol. 2, no. 9, pp. 82-90.
[21] Al-Aama, A. (2011). Evaluating Saudi municipal portal sophistication. Asian Transactions on Science & Technology, vol. 1, no. 5, pp. 1-13.
[22] Miroslav, V., & Franc, K. (2009). Coefficient of structural concordance and an example of its application: Labor productivity and wages in slovenia. Panoeconomicus, vol. 2, pp. 227-240.
[23] Liao, S. C., Hunt, E. A., & Chen, W. (2010). Comparison between Inter-rater Reliability and inter-rater agreement in performance assessment. Annals Academy of Medicine, vol. 39, no. 8, pp. 613-618.
[24] AL-Zuabi, H., & AL-Shaikhli, I. (2012). Quality evaluation of safeer portal for Saudi students studying abroad.International Conference on Advanced Computer Science Applications and Technologies– ACSAT.
[25] Jaeger, P. T., (2003). Endless wire: E-government as global phenomenon. Government Information Quarterly, 20, 323-331
[26] Al-Zuabi, H., & Mahmud M. (2011). Implementation of e-government in Arab countries: A literature review. International Conference ICRIIS, Malaysia.
[27] Marcel C. O. (2009). Development and validation of a scale for measuring e-government user Satisfaction. PhD Thesis, Nova Southeastern University, USA.
[/accordionitem][/cq_vc_accordion]

Retinal Identification Based on Density Clustering and Fuzzy Logic and Connecting This information to Electronic Health Record
Authors :- Farnaz Farshchian, Ebrahim Parcham and Shahram Tofighi
Keywords :- Fuzzy Logic, Density Classification, Electronic Medical File, Adaptive Filter
Published Online :- 04 April 2013

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View Full Abstract” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”]

[accordionitem]This paper aims at identification of individuals using the retinal image that the extraction of blood vessels based on density classification and identification is carried out according to the Fuzzy logic and then according to the performed operations of individual’s electronic health record. For this purpose, a new algorithm was presented for extraction of specific characteristics of retina based on image analysis and statistic calculations. Extraction of eye blood vessels is carried out based on adaptive filters. Our proposed method in comparison to previous methods which merely have carried out the identification through individually comparison of retina has a higher accuracy and speed. This distinction in the identification accuracy and speed was used in the type of classification and Fuzzy rules. In this paper, we will cluster the image noise at the first stage and at the end of clustering, two classes of noise and the original image will remain. This clustering will be done using the density method of Density-Based Clustering. Then we will extract the eye vessels using adaptive filters. After identifying classes and eye vessels extraction, we will identify the image of the retina under experiment based on the five-stage Fuzzy logic and rules and extract the individual’s file.[/accordionitem] [/cq_vc_accordion]

[download url=”http://elvedit.com/journals/IJACSIT/wp-content/uploads/2013/04/Retinal-Identification-Based-on-Density-Clustering-and-Fuzzy-Logic-and-Connecting-This-information-to-Electronic-Health-Record.pdf”]

[cq_vc_accordion contentcolor=”#ffffff” accordiontitle=”View References” accordiontitlesize1=”1em” accordioncontentsize1=”1em” titlepadding1=”8px 0″ titlecolor=”#ffffff”][accordionitem][1] S. Suresh, N. Sundararajan, P. Saratchandran, A sequential multi-category classifier using radial basis function networks, Neurocomputing 71 (2008) 1345–1358.
[2] A. Glazer and M. Sipper, “Evolving an automatic defect classification tool.Lecture notes in computer science,” in Proc. Applic. Evolutionary Computing- EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, 2008, pp. 194–203.
[3] A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise – Martin Ester, Hans-Peter Kriegel, Jörg Sander, XiaoweiXu, KDD ’96
[4]M.E. Martinez-Perez, A.D. Hughes, S.A. Thom, A. A. Bharath, and K.H. Parker,“Segmentation of blood vessels from red-free and fluorescein retinal images”, Medical ImageAnalysis, vol. 11, no. 1, pp. 47–61, 2007.[5] J.J. Staal, M.D.
[5] Abramoff, M. Niemeijer, M.A. Viergever, and B. van Ginneken, “Ridge basedvessel segmentation in color images of the retina,” IEEE Transactions on Medical Imaging,pp. 501–509, 2004.
[6] T. Chanwimaluang and G. Fan, “Hybrid Retinal Image Registration”, IEEE Trans. on Information Technology in Biomedicine, Vol. 10, No. 1, pp129-142, Jan. 2006.
[7] T. Chanwimaluang and G. Fan, “An Efficient Algorithm for Extraction of Anatomical Structures in Retinal Images” , in Proc. IEEE International Conference on Image Processing, Barcelona, Spain, September 2008.
[8] T. Chanwimaluang and G. Fan, “An Efficient Blood Vessel Detection Algorithm for Retinal Images using Local Entropy Thresholding”, in Proc. of the 2003 IEEE International Symposium on Circuits and Systems, Bangkok, Thailand, May 25-28, 2003.
[9] N. R. Pal and S. K. Pal, “Entropic thresholding”, Signal Processing, Vol. 16, pp. 97-108, 1989.
[10] C.-I. Chang, K. Chen, J. Wang, M. Althouse, “A relative entropy-based approach to image thresholding”, Pattern Recognition, Vol. 10, No. 1, pp. 129-142, 2006.
[11] A. Hoover, V. Kouznetsova, and M. Goldbaum, Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Medical Imaging, vol. 19, no. 3, pp. 203-210, March 2000.
[12] X. Jiang and D. Mojon, “Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp. 131-137, January 2005.
[13] J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina”, IEEE Trans. Medical Imaging, vol. 23, no. 4, pp. 501-509, April 2004.
[14] XueYingZhang ,Peng Wang, “Improved T-S Fuzzy NeuralNetwork in Application of Speech Recognition System”, ComputerEngineering and Applications, vol. 45, pp 246-248, April, 2009.
[15] Yang Wu, Wu Yuan, Wu Zhongru, “Forecast Model Study Based onFuzzy Neural Network” ,Water Resources and Power, vol. 22, pp 63-65, Mar, 2010.
[16] Sander J., Ester M., Kriegel H.-P., and Xu X. (1998) “Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications” Data Mining and Knowledge Discovery, 2(2): 169-194
[17] Gonzalez R.C. and Woods R.E. (2009) “Digital Image Processing” Prentice Hall, Upper Saddle River: New ersey
[18] S.Chaudhuri, S. Chatterjee, N.Katz, M.Nelson and M.Goldbaum, “Detection of Blood Vessels in Retinal images using two-dimensional Matched Filters”, IEEE Trans. On Medical Imaging, vol.8, no.3 sep 1989, pp 263-269.
[/accordionitem][/cq_vc_accordion]