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

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    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.
  • View References

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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

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    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.
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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

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    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.
  • View References

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    [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.

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

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    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.
  • View References

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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

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    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.
  • View References

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    [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.
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    [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.
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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

  • View Full Abstract

    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.
  • View References

    [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.
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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

  • View Full Abstract

    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.
  • View References

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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

  • View Full Abstract

    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.
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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

  • View Full Abstract

    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.
  • View References

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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

  • View Full Abstract

    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.
  • View References

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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

  • View Full Abstract

    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.
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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

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    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.
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