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Language is a hallmark of intelligence, and endowing computers with the ability to analyze and generate language as a field of research is known as Natural Language Processing (NLP) - has been the dream of Artificial Intelligence. Software requirements are typically captured in natural languages (NL) such as English and then analyzed by software engineers to generate a formal software design/model. However, English is syntactically ambiguous and semantically inconsistent. Hence, English specifications of software requirements cannot only result in erroneous and absurd software designs and implementations but, the informal nature of English is also a main obstacle in machine processing of English complex specification of the software requirements. To tackle this key dispute, there is need to introduce a controlled NL representation for software requirements, to generate perfect and consistent software models. Proposed framework aims to model complex software requirements expressed in natural language and represent them with a new methodology that captures the natural language understanding(NLU) of events and models them using Stochastic Petri Nets (SPN) instead of only intermediate graph based structure using techniques of Natural Language Processing (NLP), this helps in removing ambiguity and corrects interpretation of requirements. To eliminate ambiguity, work combines all the different meanings (SPN graphs) of each ambiguous sentence into colored SPN graph. SPNs are state machines that help us to visualize better, the combined SPN graph. It can also represent knowledge about the requirement, which can be used to derive test case in early development phase. Hence aim of proposed work is twofold that overcomes the problem of ambiguity and knowledge representation. Stakeholder�s document is input to framework, pre-processed by some pre-filter with certain functionality to improve the parsing. This parsed output gets converted into simple graph which in turn is converted into SPN graph with color representation to improve ambiguity. Pre-filter may be designed with self-learning capabilities to perk up output without human involvement.

NLP, SPN, SRS

[1] K.R. Chowdhary �Natural Language Processing� April 29, 2012
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[3] Agung Fatwanto � Software Requirements Specification Analysis Using Natural language Processing Technique � IEEE Quality in Research 2013
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[10] Haas, Peter J., Stochastic Petri Nets � Modelling, Stability,Simulation. Springer-Verlag New York, Inc 2002, ISBN 0-387-95445-7
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R.S. Ashtankar, W.M. Choudhari, "A Natural Language Processing Based Approach Using Stochastic Petri Nets For Understanding Software Requirement Specifications," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.107-110, 2016.

Thyroid disease is one of the common diseases to be found in human beings. The disease of thyroid gland varies from the low production as well as high production of the thyroid hormone, respectively. However, it is always recommended to diagnose the disease at an earlier stage in order to prevent further harmful effects and to provide the treatment to keep the thyroid hormone at normal level. Data Mining is playing vital role in health care applications. It is used to analyze the large volumes of data. One of the important task in data mining is predicting disease in earlier stage, which assist physician to give better treatment to the patients. Classification is one of the most significant data mining technique. It is supervised learning and used to classify predefined data sets. Data mining technique is mainly used in healthcare organizations for decision making, diagnosing diseases and giving better treatment to the patients. The data set used for this study on hypothyroid is taken from University of California Irvine (UCI) data repository. The entire research work is to be carried out with Waikato Environment in Knowledge Analysis (WEKA) open source software under Windows 7 environment. An experimental study is to be carried out using data mining techniques such as J48 and Decision stump tree. The data records are classified as negative, compensated, primary and secondary hypothyroid. As a result, the performance will be evaluated for both classification techniques and their accuracy will be compared through confusion matrix. It has been concluded that J48 gives better accuracy than the decision stump tree technique.

Hypothyroid, Data Mining, Classification, Decision Tree

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G.R. Banu , "A Role of decision Tree classification data Mining Technique in Diagnosing Thyroid disease," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.111-115, 2016.

The data leak detection plays a major role in organizational industry. The data leak poses serious threat to online social Medias, sensitive datas and so on. We take two papers for this survey. Both of them belong to the area of information forensic and security. In first survey, paper develops a model [PPDLD] the model is based on fuzzy finger print method. The goal of this paper is to generate special type of digest is called fuzzy fingerprint. Rabin finger print algorithm is introduced here just for sampling. A filtering method is used during the digestion process. In second survey, paper deals the fast detection of transformed data leak. This paper suggests a preserving method based on alignment algorithm. The paper aim to detect long and inexact leak patterns from sensitive data and network. Detection is based on comparable sampling algorithm.

Leak Detection; Fuzzy finger print random generation; Rabin karp algorithm; Subsequence preserving; Alignment algorithm

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[3] X. Shu, D. Yao, and E. Bertino, �Privacy-preserving detection of sensitive data exposure,� IEEE Trans. Inf. Forensics Security, vol. 10, no. 5, pp. 1092�1103, May 2015.
[4] F. Liu, X. Shu, D. Yao, and A. R. Butt, �Privacy-preserving scanning of big content for sensitive data exposure with Map Reduce,� in Proc. 5th ACM Conf. Data Appl. Secur. Privacy (CODASPY), 2015, pp. 195�206.
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[9] Chinar Bhandari1, Dr. Srinivas Narasim Kini �A Survey Paper on Data Leak Detection using Semi Honest Provider Framework� International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 Impact Factor (2014): 5.611
[10] J. Li, Q. Wang, C. Wang, N. Cao, K. Ren, and W. Lou, �Fuzzy keyword search over encrypted data in cloud computing,� in Proc. 29th IEEEConf. Comput. Commun., Mar. 2010, pp. 1�5.
[11] Xiaokui Shu, Jing Zhang, Danfeng (Daphne) Yao, and Wu-Chun Feng, Senior Member, IEEE �Fast Detection of Transformed Data Leaks� IEEE transactions on information forensics and security, VOL. 11, NO. 3, MARCH 2016
[12] Ankit Tale, Mayuresh Gunjal,B.A,Ahire �Data Leak Detection Using Information Hiding Techniques� IJCSE, VOL. 2, NO. 3, MARCH 2014

M.A. Thrupthy , J. George , S.M. Nisha, S. Lemya , "Survey on Data Leak Detection Algorithms," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.116-120, 2016.

This paper proposes a novel Universal Steganalysis framework which can be applied for spatial domain and JPEG Domain Steganography algorithms. The objective is to develop a steganalysis algorithm which has to identify any distribution (uniform or non-uniform) of stego-payloads. The framework proposed uses a 3-way tensor model to extract the image features which is important for estimating the embedded change irrespective of domain. To obtain the accurate results and to analyze the error, 360 degree bit change estimation is done. The experimental results evaluated on 3000 images which shows a good detection rate in both domains and a reasonable false acceptance rate and false rejection rate based on the pay load when tested with most of the steganography algorithms.

Spatial Domain, Jpeg Domain, Steganalysis, Tensor, SVM

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[3] J. Kodovsk�, J. Fridrich, and V. Holub, �Ensemble classifiers for steganalysis of digital media�, IEEE Transactions on Information Forensics and Security , 7(2):432�444, April 2012
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[5] Chunfang Yang, Fenlin Liu, Xiangyang Luo, and Ying ZengPixel, �Pixel Group Trace Model-Based Quantitative Steganalysis for Multiple Least-Significant Bits Steganography�, IEEE Transactions On Information Forensics And Security, vol. 8, no. 1, January 2013
[6] Chunfang Yang, Fenlin Liu1, Xiangyang Luo1, Ying Zeng,�Fusion of Two Typical Quantitative Steganalysis Based on SVR�, Journal Of Software, Vol. 8, No. 3, March 2013
[7] Tomas Pevny and Andrew D. Ker, �The Challenges of Rich Features in Universal Steganalysis�, Proc. SPIE 8665, Media Watermarking, Security, and Forensics, 86650M, March 22, 2013.
[8] Ziwen Sun, Hui Li, �Quantitative Steganalysis Based on Wavelet Domain HMT and PLSR�, 10th IEEE International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 2011
[9] Gikhan Gul and Faith Kurugollu,�SVD-Based Universal Spatial Domain Image Steganalysis�, IEEE Transactions on Information Forensics and Security,Vol.5,No.2,June 2010
[10] Changxin Liu, Chunjuan Oujang,�Image Steganalysis Based on Spatial Domain and DWT Domain Features�, IEEE Second International Conference on Network Security, Wireless Communications and Trusted Computing, 2010.
[11] Souvik Bhattacharyya and Gautam Sanyal, �Steganalysis of LSB Image Steganography using Multiple Regression and Auto Regressive (AR) Model�, Int. J. Comp.Tech. Appl., Vol 2 (4), 1069-1077
[12] Zhenhao Zhu, Tao Zhang, Baoji Wan, �A special detector for the edge adaptive image steganography based on LSB matching revisited�, 2013 10th IEEE International Conference on Control and Automation (ICCA)
[13] Weiqi Luo, Yuangen Wang, Jiwu Huang, �Security analysis on spatial +_1Steganography for jpeg decompressed images�, IEEE Signal Processing Letters, (Volume:18 ,Issue: 1)
[14] Tomas Pevny,Tomas Filler, Patrick Bas, �Using high-dimentional image models to perform undetectable steganography� , Lecture Notes in Computer Science Volume 6387,2010,pp 161-177
[15] Mielikainen, � LSB Matching revisited�, Signal Processing Letters, IEEE (Volume:13 , Issue: 5), May 2006
[16] Weiqi Luo, Fangjun Huang, and Jiwu Huang, �Edge Adaptive Image Steganography Based on LSB Matching Revisited�, IEEE Transactions On Information Forensics And Security, vol. 5, no. 2, june 2010
[17] Shunquan Tan, �Targeted Steganalysis of Edge Adaptive Image Steganography Based on LSB Matching Revisited Using B-Spline Fitting�, Signal Processing Letters, IEEE(Volume:19 ,Issue: 6), June 2012
[18] Chao Wang, Xiaolong Li, Bin Yang, Xiaoqing Lu, Chengcheng Liu, � Content- adaptive approach for reducing embedding impact insteganography�, IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 14-19 March 2010
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[29] Guangjie Liu, Zhan Zhang and Yuewei Dai, �Improved LSB-matching Steganography for Preserving Second-order Statistics�, Journal of Multimedia, vol. 5, no. 5, October 2010
[30] J. Fridrich and D. Soukal, �Matrix embedding for large payloads,� in Proceedings SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII, E. J. Delp and P. W. Wong, Eds., vol. 6072, San Jose, CA, January 16�19, 2006, pp. W1�W10.
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C. Arunvinodh, S. Poonkuntran, "Embedding Change Estimation for Universal Steganalysis using 3-way Tensor Model," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.121-128, 2016.

Cloud computing, an internet based technology which provides virtualized computer resources over the internet. Distribution of the dynamic workload among the computer resources in the cloud evenly in such a way that no single node is overloaded or under loaded is called Load Balancing. An efficient load balancer will increase the performance of cloud, maximizes the cloud services and also increases the resource utilization. Today increasing the performance of a cloud depends on many factors, among them Load Balancing is one of the main factors. In this paper we propose a load balancing algorithm which is a variant to the Weight Least Connection (WLC) algorithm. The proposed algorithm shows better results in several aspects like accurate calculation of work load on a resource, distributing the work load on the service nodes efficiently, enhancing the response time and minimizing the overall task execution time.

Cloud computing, Virtualized computer resources, Dynamic workload, Load balancer, Load balancing algorithm, Least Connection (LC) algorithm, Power consumption

[1]. Xiaona Ren, Rongheng Lin,Hua Zou, �A Dynamic Load Balancing Strategy For Cloud Computing Platform Based On Exponential Smoothing Forecast�, IEEE ccis2011, pp-220-225, 2011.
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[4]. E. Kartal Tabak, B. Barla Cambazoglu, Cevdet Aykanat, �Improving the Performance of Independent Task Assignment Heuristics MinMin, MaxMin and Sufferage�, IEEE Transactions On Parallel And Distributed Systems, VOL. 25, NO. 5, MAY 2014.
[5]. Ra�ul Alonso-Calvo, Jose Crespo, �On distributing load in cloud computing: A real application for very-large image datasets� Procedia Computer Science 1, Elsevier, 2669�2677, 2012.
[6]. Kousik Dasgupta, Brototi Mandal, Paramartha Dutta, Jyotsna Kumar Mondal, Santanu Dam, �A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing�, Procedia Technology 10, Elsevier, 340 � 347, 2013.
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V.R.T. Kanakala, K.P. Kumar, S. Kavitha, "A Hybrid Heuristic Algorithm to Enhance Load balancing in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.129-132, 2016.

There are various methods of handling Optimal Binary search trees in order to improve the performance. One of the methods is Dynamic programming which incurs O(n3) time complexity to store involved computations in a table. The data mining technique called Data Preprocessing is used in order to remove noise early in the dataset and enhances consistency of the given data. The post dynamic computing is applied using knowledge of dynamic programming principle which starts with only required data and computes only the necessary attributes required to construct Optimal Binary Search Tree with time complexity O(n) if there are n identifiers / integers / any complex objects. This approach avoids computing all necessary table attributes. Hence, the complexity or cost of post dynamic computing using Dynamic Programming is proven to be less than O(n3) or even less than specified in some cases with experimental results.

Optimal Binary Search Tree (OBST), Data Preprocessing, Post computing, Dynamic Programming, Time Complexity

[1]. Micheline Kamber, Hei,�Data Mining: Concepts and Techniques�, Second Edition, The Morgan Kaufmann Series, ISBN 13: 978-1-55860-901-3,ISBN 10: 1-55860-901-6.
[2]. Data Preprocessing Techniques for Data Mining, iasri.res.in/ebook/win_school_aa/notes/Data_Preprocessing.pdf
[3]. Nguyen Hung Son, Data cleaning and data preprocessing, www.mimuw.edu.pl/~son/datamining/DM/4-preprocess.pdf
[4]. Andy Mirzaian , DYNAMIC PROGRAMMING: OPTIMALSTATIC BINARY SEARCH TREES, http://
www.cse.yorku.ca/~andy/courses/3101/lecture-notes
/OptBST.pdf
[5]. ROLF KARLSSON, ALGORITHM THEORY,HTTP://FILEADMIN.
CS.LTH.SE/CS/PERSONAL/ROLF_KARLSSON/LECT5.PDF
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/pii/ 0020019081901435
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[8]. Ellis Horowitz, Sartaj Sahni, Sanguthevar Rajasekharan, http://vitconference.com/vit_mca/images/resources/
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By-Ellis-Horowitz-1984.pdf
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S.H. Raju, M.N. Rao, "Improvement of Time Complexity and Space on Optimal Binary Search Trees using post dynamic Programming Methodology and Data Preprocessing," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.133-136, 2016.

Diabetic Retinopathy (DR) also known as diabetic eye disease. It is the damage occurs to the retina due to diabetes. It can eventually lead to blindness. So the early detection of disease is needed, Manual detection is time consuming and often make observation error. Hence several computer-aided systems are introduced and which would make fast and consistent diagnosis- aid useful for biomedical and health informatics field. The Diabetic retinopathy detection methods that uses machine learning techniques. In one system classifiers such as the Gaussian Mixture model (GMM), k-nearest neighbor (kNN), support vector machine (SVM) are used and another system that uses GMM, kNN, SVM, and combinational classifiers are used for classifying retinal fundus images.

Diabetic Retinopathy, Image processing, Feature extraction, Bright lesions, classification, diabetic retinopathy (DR), red lesions, segmentation

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[3] S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, �Screening fundus images for diabetic retinopathy,� in Proc. Conf. Record 46th Asilomar Conf. Signals, Syst. Comput., 2012, pp. 1641�1645.
[4] L. Shen and L. Bai, �Abstract adaboost gabor feature selection for classification,� in Proc. Image Vis. Comput., 2004, New Zealand, pp. 77�83.
[5] Anitha L. and Arunvinodh C., "Diverse Frameworks on Retina Verification", International Journal of Computer Sciences and Engineering, Volume-02, Issue-12, Page No (62-67), Dec -2014, E-ISSN: 2347-2693.

K.K. Faisal , C.M. Deepa, S.M. Nisha, G. Gopi, "Study on Diabetic Retinopathy Detection Techniques," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.137-140, 2016.