For analyzing the count data, traditional probability distributions such as Poisson and negative binomial distributions are considered to be the most suitable models. But in some situations, count data shows large number of zeros that cause heavy tail which leads to over dispersion. In these situations, it is observed that these traditional statistical count models cannot be used efficiently. In order to overcome this problem, many mixed distributions have been introduced in the statistical literature. Among these distributions, Poisson and negative binomial were used as base line distribution for analyzing over dispersed count data. In this paper, we have proposed a mixture of negative binomial mixture.............

Keywords: Mixture Distributions, Negative binomial distribution, Generalized Lindley distribution, Maximum Likelihood Estimation

[1]. Aryuyuen, S., & Bodhisuwan, W. "The negative binomial-generalized exponential (NB-GE) distribution". Applied Mathematical Sciences, vol 7(22), 1093- 1105, 2013.
[2]. Gomez-Deniz, E., Sarabia., J. M., & Calderin-Ojeda., E. "Univariate and multivariate versions of the negative binomial-inverse Gaussian distributions with applications". Insurance Mathematics and Economics, vol 42, 39-49, 2008.
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[4]. Johnson, N. L., Kemp, A. W., & Kotz, S. "Univariate Discrete Distributions, 3rd, Wiley Series in Probability and Statistics", John Wiley and Sons, Inc. Hoboken, New Jersey, U.S.A.2005
[5]. Karlis, D. & Xekalaki, E. "Mixed Poisson distributions",International Statistical Review / Revue Internationalede Statistique,vol 73(1), 35-58, 2005.

Medical diagnosis is an essential problem that is mainly investigated by countless researchers from medical and computer science domains. The aim of medical diagnosis is to recognize whether the patient suffers from a particular disease. Under such hypothesis the medical diagnosis is made with respect to a particular disease, i.e., it is a specific type of classification that assumes only two decision classes, 'Normal' or 'Sick'. It means that for every disease considered by the doctor, a separate diagnosis (classification) with the use of the FCM model should be performed. In this paper, we presented the concepts of Fuzzy Cognitive Map with Decision trees used in the prediction and identification of various disease in health care domain.

Keywords: Data Mining, Classification, Decision Trees, Fuzzy Cognitive Map.

[1]. Song, H. J., Miao, C. Y., Wuyts, R., Shen, Z. Q., D'Hondt, M., &Catthoor, F. (2011). An extension to fuzzy cognitive maps for classification and prediction.Fuzzy Systems, IEEE Transactions on, 19(1), 116-135.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.
[2]. W. B. VasanthaKandasamy and FlorentinSmarandache, "Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps", Book published in 2003.
[3]. Harmanjit Singh Research Scholar, Dr. Gurdev Singh ,Nitin Bhatia,2013,"Fuzzy cognitive maps based election results prediction system" ,International Journal of Computers & Technology Volume 7 No. 1, Council for Innovative Research,pp. 483-492.
[4]. S. M. Reza Nasserzadeh,2008,M. HamedJafarzadeh,TahaMansouri,BabakSohrabi," Customer satisfaction fuzzy cognitive map in banking industry",Volume.2,Communications of the IBIMA.
[5]. Elpiniki I. Papageorgiou, Nikolaos I. Papandrianos, Georgia Karagianni, George C. Kyriazopoulos and Dimitrios Sfyras,2009," A Fuzzy Cognitive Map based tool for prediction of infectious Diseases" FUZZIEEE 2009,Korea.

Mobile Ad hoc Network (MANET) is the significant technology among various wireless communication technologies where all the nodes are mobile and which can be connected to dynamically used wireless link in a random manner. The self-configuring ability of nodes in MANETs made it popular among critical applications like military use or natural emergency recovery. Most of the proposed protocols assume that all nodes in the network are cooperative, and do not address any security issue. To adjust to such trend, it is vital to address its potential security issues. The main objective of this paper is to define the path for security and to further improve delay, energy, throughput, routing overhead, packet delivery ratio and at the same time to create energy enhanced way with excellent security.............

Keywords: Mobile ad-hoc networks, Routing, AODV, M-AODV, NS2.

[1]. Parma Nand, Sharma (2011) "Routing Load Analysis of Broadcast based Reactive Routing Protocols AODV, DSR and DYMO for MANET", IJGDC.
[2]. Ade and Tijare (2010) "Performance Comparison of AODV, DSDV, OLSR and DSR Routing Protocols in Mobile Ad-hoc Networks", IJITKM, Vol. 2, No. 2, pp. 545-548.
[3]. Johansson, et al. (1999) "Scenario based Performance Analysis of Routing Protocols for Mobile Ad-hoc Networks", IEEE.
[4]. Marti, et al. (2000) "Mitigating Routing Misbehavior in Mobile Ad-hoc Networks", ACM, 2000.
[5]. Pushpalatha, et al. (2009) "Trust Based Energy Aware Reliable Reactive Protocol in Mobile Ad-hoc Networks", World Academy of Science.

Preprocessing is one decisive stage in data mining. The most important step in pre-processing is handling missing data. Managing missing data has different strategies such as deletion of incomplete data and imputation (filling) of missing values through depends on statistical and machine learning (ML) procedures. Most of the data mining algorithm cannot work with an incomplete dataset. In such a case, missing values create a problem with real data in the world. Missing value Imputation is a challengeable task in many sectors. Some of them perform their handling imputations in different ways. Missing value imputation determines the quality of datasets. A high accuracy data analysis is essential............

Keywords: Missing value, Imputation methods, Mean, K-nearest neighbor, Naive Bayes, Chain of evidence.

[1]. Jaiwei Han, Micheline Kamber, Jian Pei,"Data mining Concepts and Techniques", Third Edition, Reprinted 2015.
[2]. R. J. Little and D. B. Rubin, "Statistical Analysis with Missing Data.John Wiley and Sons", New York, 1997.
[3]. Schafer, J.L., 1997,"Analysis of incomplete multivariate data.Monographs on Staistics and AppliedProbability", No. 72. Chapman and Hall, London.
[4]. Donders, A.R.T., G.J.M.G. van der Heijden, T. Stijnen, K.G.M. Moons, "Review: A gentleintroduction to imputation of missing values",Journal of Clinical Epidemiology, 59: 1087-1091, 2006.
[5]. M.N. Norazian Ramli, Yahaya, A.S., Ramli, N.A., Yusof, N.F.F.M., Abdullah, M.M.A. "Roles of Imputation Methods for Filling the Missing Values: A Review", AENSI Journals Advances in Environmental Biology, Special Issue for International Conference of Advanced Materials Engineering and Technology (ICAMET 2013), 28-29 November 2013, Bandung Indonesia.

User based filtering of review using collaborative filtering predicts our users interest in our data set using the information provided by the product of r profiles and a enhanced algorithm is used to unify r-base d and item based filtering approach to fulfill the limitations of specific product m. We showed that use r-based and item-based approaches are only two special cases in our probabilistic fusion framework. In existing model, we realized that a major source of error while making predictions is the fact that for some movies the crew members are not there in the training set. Although it is difficult to predict the ratings for the movies with a totally unknown cast, we have employed certain heuristics to minimize the error. Also, there can possibly be
other factors like release date, location, plot, etc..............

Keywords : Sentiment analysis, collaborative filtering, Ratings

[1]. Burke, R., Semantic ratings and heuristic similarity for collaborative filtering. in Proceedings of the 17th National Conference on Artificial Intelligence, (2000).
[2]. Canny, J., Collaborative Filtering with Privacy. in IEEE Conference on Security and Privacy, (2002).
[3]. Chen, M. and Singh, J.P. Computing and using reputations for internet ratings Proceedings of the 3rd ACM conference on Electronic Commerce, ACM Press, Tampa, Florida, USA, 2001.
[4]. Dellarocas, C., Analyzing the Economic Efficiency of eBay-like Online Reputation Reporting Mechanisms. in Proceedings of the 3rd ACM Conference on Electronic Commerce, (2001).
[5]. Dellarocas, C., Efficiency through Feedback-contingent Fees and Rewards in Auction Marketplaces with Adverse Selection and Moral Hazard. in 3rd ACM Conference on Electronic Commerce, (2003).

The ideal is one of the key to incorporate heterogeneity amid the three workings of the network: software, hardware and set-up type. This classical too allows for in cooperation cyber and non-cyber-related bang on the mission. The manuscript presents domino effect of a research of malware distribution in assorted networks via epidemiological modeling framework. The unified methodology full in this analyze aggregates and extends models of malware distribution that what's more solve not financial credit for set-up heterogeneity or set a limit for heterogeneity contained by one component, e.g. software. A system of regular differential equations is solved numerically to make something stand out mortal dependence..........

Keywords: heterogeneous networks, cross-platform, malware propagation, cyber-attack, mission.

[1]. G. Eason, B. Noble, and I. N. Sneddon, "On certain integrals of Lipschitz-Hankel type involving products of Bessel functions," Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. (references)
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Most of the institutions are looking forward for the solution for student's better performance in their Education. Every institution is ready to implement different methods to understand students thinking and their needs. Educational data mining (EDM) is a most important and focused research area. In EDM different type of student data were collected and implemented through various data mining techniques. Classification Algorithms like Decision trees, K- Nearest Neighbor, Neural Networks, Naïve Bayes and so on were used. This paper on the EDM compares the classification algorithms used in EDM.

Keywords - Educational Data Mining, Prediction, Classification Algorithms.

[1]. Amjad Abu Saa. (2016) "Educational Data Mining & Students' Performance Prediction" International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 2016.
[2]. Ahmed Mueen, Bassam Zafar and Umar Manzoor. (2016) "Modeling and Predicting Students' Academic Performance Using Data Mining Techniques" I.J. Modern Education and Computer Science, 2016, 11, 36-42.
[3]. Ashwin Satyanarayana, Mariusz Nuckowski, "Data Mining using Ensemble Classifiers for Improved Prediction of Student Academic Performance" Spring '2016' Mid. Atlantic 'ASEE' Conference, 'April' 8.9,'2016' GWU.

Human brains are capable to produce novel solutions to the dynamically emerging security problems. But they are restricted with process automation, correctness, remembrance, knowledge representations, processing parallel task, increasing execution speed, mobile control and dealing with huge amount of data when compared to intelligent computational tools. In this proposed effort, various design aspects are framed that serves as a boundary to build an intelligent home security model with alarm signal to denote the unauthorized entry of an object. This model uses the advantage of artificial intelligent technique to attain high level home security system incorporated with traditional monitoring system.

Keywords- Artificial Intelligence, Object Recognition, Security, Authorization

[1]. Surinder Kaur, Rashmi Singh, Neha Khairwal & PType equation here.ratyk Jain 2016," Home Automation And Security System", Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.3, July 2016
[2]. Santoso Budijono, Jeffri Andrianto, Muhammad Axis Novradin Noor 2014," Design and implementation of modular home security system with short messaging system", EPJ Web of Conferences 68,00025DOI: 10.1051/epjconf/20146800025
[3]. Jayashri Bangali and Arvind Shaligra 2013," Design and Implementation of Security Systems for Smart Home based on GSM technology" International Journal of Smart Home Vol.7,No.6(2013),pp.201-208
[4]. Sadeque Reza Khan, Ahmed Al Mansur, Alvir Kabir, Shahid Jaman, Nahian Chowdhury 2012," Design and Implementation of Low Cost Home Security System using GSM Network" International Journal of Scientific & Engineering Research Volume 3, Issue 3, March -2012 1 ISSN 2229-5518

This research work aims to design and develop an adaptive kalman filter in physiological detection of anxiety related arousal in children those who are having autism spectrum disorder (ASD). Unlike classical adaptive Kalman filters, which have been designed for state estimation in case of uncertainties about noise covariances, the adaptive Kalman filter proposed in this paper is for the purpose of ASD classification, through joint state-parameter estimation. The stability and minimum variance properties of the adaptive Kalman filter have been implemented. In this part of research work, a stochastic framework, with rigorously established statistical and stability properties. In particular, it is shown that the proposed............

Keywords- kalman filter, autism spectrum disorder, ASD, sensitivity, specificity, fall out, miss rate, accuracy.

[1]. Abraham, A., et al., 2017. Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147, 736–745.
[2]. Akshoomoff, N., Corsello, C., Schmidt, H., 2006. The role of the autism diagnostic observation schedule in the assessment of autism spectrum disorders in school and community settings. Calif. Sch. Psychol. 11 (1), 7–19.
[3]. Altman, D.G., Bland, J.M., 1994. Diagnostic tests 2: predictive values. BMJ 309 (6947), 102. Anderson, J.S., et al., 2011. Functional connectivity magnetic resonance imaging classification of autism. Brain 134 (12), 3739–3751.
[4]. Arbabshirani, M.R., Plis, S., Sui, J., Calhoun, V.D., 2016. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage 145 (Pt B), 137–165.
[5]. Aylward, E.H., et al., 1999. MRI volumes of amygdala and hippocampus in non-mentally retarded autistic adolescents and adults. Neurology 53 (9) (2145–2145).

Autism spectrum disorder (ASD) has become one of the most prevalent mental disorders over the last few years and its prevalence is still growing every year. It is a serious developmental disorder that afflicts children and that is more common than childhood cancer. The disorder is characterized by a wide variety of possible symptoms such as developmental disabilities, extreme withdrawal, lack of social behaviour, severe language and attention deficits, and repetitive behaviours. The symptoms intensity ranges from almost unnoticeable to very severe. Because of this wide variety of symptoms and intensity, therapy needs to be individualized for every person. On the other hand.............

Keywords - AUTISM spectrum disorder, virtual reality, e-learning tool

[1]. J. Bishop, "The Internet for educating individuals with social impairments," Journal of Computer Assisted Learning, vol. 19, pp. 546-556, 2003.
[2]. [5] M. Dauphin, E. M. Kinney, and R. Stromer, "Using Video-Enhanced Activity Schedules and Matrix Training to Teach Sociodramatic Play to a Child with Autism," Journal of Positive Behavior Interventions, vol. 6, pp. 238-250, 2004.
[3]. T. Miller, G. Leroy, J. Huang, S. Chuang, and M. H. Charlop-Christy, "Using a Digital Library of Images for Communication: Comparison of a Card-Based System to PDA Software," presented at First International Conference on Design Science Research in Information Systems and Technology, Claremont, CA, 2006.
[4]. G. Rajendran and P. Mitchell, "Computer mediated interaction in Asperger's syndrome: the Bubble Dialogue program," Computers and Education, vol. 35, pp. 189-207, 2000.
[5]. Happe, F. & Frith, U. (1996). The neuropsychology of autism. Brain - A jnl of Neurology, 119(4):1377-1400 Hardy, C., Ogden, J., Newman, J. & Cooper, S. (2002) Autism and ICT: A Guide for Teachers and Parents. London, UK: David Fulton Publishers Ltd.

Image Processing is a process of performing operations on an image with an intention of getting an improved image. It is also used to mining to get the hidden information in an image. Edge detection is the method of finding the boundaries of object in the image. Recently image processing techniques are being used in the prediction of diseases, where it helps to increase the classification accuracy. In this paper a novel edge detection method based on least square support vector machines is proposed to detect edges with the target of predicting the lung cancer. This paper uses sensitivity, specificity, accuracy and F1-measure as the metric to measure the performance. The results show that the proposed methods outperforms than other algorithm.

Keywords- Edge Detection, classification, SVM, image processing, accuracy.

[1]. A. Olaode, G. Naghdy and C. A. Todd, "Unsupervised Image Classification by Probabilistic Latent Semantic Analysis for the Annotation of Images," 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Wollongong, NSW, 2014, pp. 1-8.
[2]. C. Kuo, H. H. Ho, C. H. Li, C. C. Hung and J. S. Taur, "A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 1, pp. 317-326, Jan. 2014.
[3]. Diamant, E. Klang, M. Amitai, E. Konen, J. Goldberger and H. Greenspan, "Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 6, pp. 1380-1392, June 2017.
[4]. Guang-Xin LI, Ke Wang, Li-Bao Zhang, The fast algorithm of weighted multi-resolution image fusion, China J. Image Graph. 10 (12) (2005)1529–1536.
[5]. J. Guo, W. Zhu, F. Shi, D. Xiang, H. Chen and X. Chen, "A Framework for Classification and Segmentation of Branch Retinal Artery Occlusion in SD-OCT," in IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3518-3527, July 2017.

Testing is very important activity in software development process. Effective testing produces high quality software. Testing is done effectively resulting in quality end product, which meets customer requirements. Agile is an iterative development methodology, where both the development and testing activities are connected. Iteration is process of delivering a small release of software. Agile Testing starts at the beginning of the project with rapid integration between development and testing. This paper deals with agile methodology, principles and agile testing methods. The objective of this paper is to analyze and understand the agile methodology that is to be used in the testing process.

Keywords : Software development, Software Quality, Customer.

[1]. https://agile testing.org/
[2]. https://www.guru99.com/agile-scrum-extreme-testing.html.
[3]. https://www.tutorialspoint.com/software_testing_dictionary/agile_testing.html
[4]. https://en.wikipedia.org/wiki/Agile_testing [5].https://www.agilealliance.org/
[5]. http://www.origsoft.com/whitepapers/software-testingglossary/glossary_of_terms.pdf

Data mining is becoming a invasive knowledge in activities as varied as using historical data to forecast the success of a marketing process looking for patterns in monetary contact to discover banned activities or analyzing genome sequences From this perception it was just a material of time for the control to reach the important area of computer safety This book presents a collection of investigate efforts on the use of data mining in computer safety.

Keywords- Scan Detection; Virus exposure; Anomaly disclosure; Shelter

[1]. Data Mining for Security Applications : Bhavani Thuraisingham, Latifur Khan, Mohammad M. Masud, Kevin W. Hamlen
[2]. Rakesh Agrawal, Tomasz Imieliski, and Arun Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data.
[3]. Daniel Barbara and Sushil Jajodia, editors. Applications of Data Mining in Computer Security. Kluwer Academic Publishers
[4]. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and J Sander. Lof: identifying density-based local outliers. In Proceedings of the 2000 ACM SIG-MOD international conference on Management of data, pages
[5]. Varun Chandola and Vipin Kumar. Summarization {compressing data into an informative representation. In Fifth IEEE International Conference on Data Mining, pages.

Security issue of Cloud Computing (CC) is very significant and it is able to prevent the fast improvement of new methods. This work aims in the direction of calculate the security level with numerous hugescale attributes in CC. A new classifier based Hopfield Neural Network(HNN) is proposed in this paper for predicting the security state of CC, where the Evidential Reasoning(ER) technique is employed in the direction of combine the various system indicators of real CC system and formulate a practical evaluation in the direction of explain the cloud security state. To show the efficiency of the proposed HNNBCSS prediction model is experimented in engineering, the valuation of security condition in the CC platform will be computed by using logs with 100 days. Ten-fold cross validation is performed to verify the security state of the classifier results further successful and satisfactory; where interval determination is used in order verify the accuracy and security of the CC model.

Keywords - Cloud Computing (CC), Hopfield Neural Network (HNN), classifier, security, evidential reasoning (ER), logs.

[1]. Rawat, S.S. and Sharma, N., 2012. A survey of various techniques to secure cloud storage. International Journal of Computer Science and Network Security (IJCSNS), 12(3), p.116.
[2]. Behl, A. and Behl, K., 2012, October. An analysis of cloud computing security issues. World Congress on Information and Communication Technologies (WICT), pp. 109-114.
[3]. D. G. Rosado, D. Mellado, E. Fernandez, and M. Piattini, Security Engineering for Cloud Computing: Approaches and Tools. Hershey, PA, USA: IGI Global, 2012, pp. 1-19.
[4]. S. Vakilinia, B. Heidarpour and M. Cheriet, ``Energy efficient resource allocation in cloud computing environments,'' IEEE Access, vol. 4, pp. 85448557, 2016.
[5]. T. Boukra, ``Identifying new prognostic features for remaining useful life prediction using particle filtering and neuro-fuzzy system predictor,'' in Proc. IEEE 15th Int. Conf. Environ. Elect. Eng. (EEEIC), 2015, pp. 15331538.