iosr-JCE   Next Generation Computing Technologies

Abstract: Video analysis is the time consuming process, the paper study on find Bit rate, FPS (Frame per Second), color value and size of the video. It also explained Digital 3DVideo analysis, real-time measurement methods, general functionalities of the videos and its algorithms and available video analyzer tools. The novel method focused on the video analyzer development logics and its related program and real-time analytics of any video.

Keywords - video analysis; video quality analysis; algorithms; methods

[1]. Pablo Flores, Federico Lecumberry, Pablo Arias, Alvaro Pardo, Video Analysis Platform. gmm@fing.edu.uy,
http://iie.fing.edu.uy/vap.
[2]. http://zone.ni.com/devzone/cda/pub/p/id/1078
[3]. http://www.eetimes.com/electronics-news/4196911/Performance-Analysis-for-Objective-Methods-of-Video-Quality-Assessment
[4]. Telchemy, Application Note. Understanding IP Video Quality Metrics. February 2008.
[5]. Guido van Rossum, Python Standard Library: Multimedia Modules. Python Imaging Library (PIL).

Abstract: Big data is a term which refers to a set of data that are so huge or complicated that time-honoreddata processing applications are insufficient. Process include learning, capture, data duration, seek, sharing, storage, transfer, revelation, query, updating and information privacy. The term often signposts the use of predictive analysis or certain new methods to extract value from data, and to a particular size of data set. Accuracy in big data may lead to more confidence in making decision, and better decisions can result in higher operational efficiency, reduce in cost and risk. Analysis of data sets can find new tie-ups to find business trends, prevent diseases, combat crime and so on. Scientists, business executives, advertising and governments alike regularly meet problems with large data in areas including Internet surfing, finance and business informatics. Scientists confront limitations in e-Science work, including meteorology, connectomics, genomics, complex physics simulations and biology.

[1]. https://en.wikipedia.org/wiki/Big_data
[2]. https://www.google.co.in/search?q=%22+The+3Vs+have+been+expanded+to+other+complementary+characteristics%22&gws_rd=cr&ei=rf91V9LmFsaVUe7SjsgM
[3]. https://www.google.co.in/search?q=%22+In+2000,+Seisint+Inc.+%28now+LexisNexis+Group%29+developed%22&gws_rd=cr&ei=EwB2V5SGAsGuU_ulg-gP
[4]. https://www.google.co.in/search?q=%22+Techniques+for+analyzing+data,+such+as+A/B+testing,+machine%22&gws_rd=cr&ei=cQB2V9StKYXiU7SIu5AM

Abstract: Pattern recognition involves study from image processing and from various other fields that includes machine learning. Classifications involved in pattern recognition are supervised and unsupervised. The supervised classification of input data in the pattern recognition method and the unsupervised classification method works by finding hidden structures in unlabeled data..This paper discusses about the various methods of pattern recognition and classifications.

Keywords- Pattern recognition, classification, Feature Extraction

[1]. R.A. Fisher, "The Use of Multiple Measurements in Taxonomic Problems,"Annals of Eugenics, vol. 7, part II, pp. 179-188, 1936. [2]. Dasarathy, B.V.; "Minimal consistent set (MCS) identification for optimal nearest neighbor decision systems design", IEEE Transactions on Systems, Man and Cybernetics, Vol. 24, Issue: 3, pp:511 – 517, March 1994.

[3]. Girolami, M.; Chao He; "Probability density estimation from optimally condensed data samples" Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume: 25, Issue: 10 , pp:1253 – 1264,Oct. 2003.

[4]. Meijer, B.R.; "Rules and algorithms for the design of templates for template matching", Pattern Recognition, 1992. Vol.1. Conference A: Computer Vision and Applications, 11th IAPR International Conference on, pp: 760 – 763, Aug. 1992.

[5]. Hush, D.R.; Horne, B.G.; "Progress in supervised neural networks", Signal Processing Magazine, IEEE, Vol. 10, Issue: 1, pp:8 – 39, Jan. 1993

Abstract: The internet of things is the interconnection of physical objects, sensor, networking synthesizers and embedded software which enables to collect and exchange the data. In recently, many states have focused to implement smart city, smart home, smart indoor personal comfort levels monitoring and smart health care based on the emerging field in IoT. In this paper briefly explains the basic concepts of IoT, some of the technology were reviewed and finally describe the major research and social issues.

Keywords: IoT Architecture, Privacy and Threats

[1]. Memon, Azam Rafique, et al. "An Electronic Information Desk System For Information Dissemination In Educational Institutions."
[2]. Karimi, Kaivan, and Gary Atkinson. "What the Internet of Things (IoT) needs to become a reality." White Paper, FreeScale and ARM (2013).
[3]. Stankovic, John. "Research directions for the internet of things." Internet of Things Journal, IEEE 1.1 (2014): 3-9. [4] Gubbi, Jayavardhana, et al. "Internet of Things (IoT): A vision, architectural elements, and future directions." Future Generation Computer Systems 29.7 (2013): 1645-1660.
[4]. "Understanding the Internet of Things (IoT) ", July 2014.
[5]. Dogo, E. M. et al. "Development of Feedback Mechanism for Microcontroller Based SMS Electronic Strolling Message Display Board." (2014).

Abstract: The project "MAKE EASY FINANCE" is a unique website that helps the staffs to make their loan sanction process easier. It is a full ERP software for finance application. It is a full offline and server based project with complete package of accounts and loan process. The customer who request for the Loan, we will allocate loan process. The details of the customer will be provided to the finance team. If the details are given by the customer gets satisfied ,then Discussion with the customer will be carried on. In that the income of the family and the property details will be verified. If the verification is done with original documents, then the field inspection will be done and the photo copies of the property will be uploaded and it is verified by the higher authority. If it is satisfied ,the Legal verification will be carried out in the government office Whether the property is belongs to them. In the First approval, the customer will request for certain amount for the land ,if that amount is valid for their property then they will proceed or else they will sanction the amount which suitable for their property. If...............

Keywords: [Business rules, cibil score, loan terms, market semantics, information systems].

[1]. Charles L. Forgy, Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem, Artificial Intelligence, 1982. Ross, R.G., Principle of the Business Rule Approach. 2003: Addison-Wesley.
[2]. Chisholm, M., How to Build a Business Rules Engine. 2004: Morgan Kaufmann.
[3]. Huang, W., Business Process Rules Management: Challenges and Solut ions, Stevens Inst itute of Technology, 2007.
[4]. Hall, C. and P. Harmon, The 2006 BP Trends Report on Business Rules Products. 2006, Business Process Trends.
[5]. Huang, W., Business Process Rules Management: Challenges and Solutions, Dissertation Proposal, Howe School of Technology Management, Stevens Institute of Technology, 2007..

Abstract: In data clustering the more traditional algorithms are based on similarity criteria which depend on a metric distance. This fact imposes important constraints on the shape of the clusters found. These shapes generally are hyper spherical in the metric's space due to the fact that each element in a cluster lies within a radial distance relative to a given center. This paper propose a clustering algorithm that does not depend on simple distance metrics and. Therefore, it allows us to find clusters with arbitrary shapes in n-dimensional space. The proposal is based on some concepts stemming from Shannon's information theory and evolutionary computation. Here each cluster consists of a subset...........

Keywords: clustering, data mining, information theory, genetic algorithms.

[1]. Cha. Sil.: Taxonomy of Nominal Type histogram Distance Measures. Massachusetts (2008)
[2]. Mahalanobis. P.C.: On the generalized distance instatistics(1936)
[3]. Bhattucharyya. A.: On a measure of divergence between two statistical populations defined by probability distributions.Calcutta(1943)
[4]. Pollard. D.E.: A user's guide to measure theoretic probability. Cambridge University Press,Cambridge(2002)
[5]. Yang. G.L.. Le Cam. L.M.: Asymptotics in Statistics: Some Basie Concepts. Springer.Berlin(2000)

Abstract: Data sharing here it is not much easier there are many advance of cloud computing techniques available, and a quality investigation on the shared data gives a bunch of remuneration to both the society and folks. Data sharing with a large number of participants must take into account several issues, that results is data loss and integrity of the data may lacks and it including data effectiveness, data veracity and privacy of data manager. Ring signature is a hopeful candidate to construct and unidentified and reliable data sharing system. It allows a data owner to namelessly permit his data which can be put into the cloud for storage or investigation purpose up till now the pricey certificate evidence in the accustomed communal key communications surroundings becomes a blockage..........

Keywords— substantiation, data sharing, cloud computing, forward protection, smart grid

[1]. R. Cramer and V. Shoup, "Signature schemes based on the strong RSA assumption," in Proc. ACM Conf. Comput. Commun. Security, 1999, pp. 46–51.
[2]. G. Ateniese, J. Camenisch, M. Joye, and G. Tsudik, "A practical and provably secure coalition-resistant group signature scheme," in Proc. 20th Annu. Int. Cryptol. Conf. Adv. Cryptol., 2000, vol. 1880, pp. 255–270.
[3]. M. Abe, M. Ohkubo, and K. Suzuki, "1-out-of-n signatures from a variety of keys," in Proc. 8th Int. Conf. Theory Appl. Cryptol. Inform. Security: Adv. Cryptol., 2002, vol. 2501, pp. 415–432
[4]. J. Herranz and G. S_aez, "Forking lemmas for ring signature schemes," in Proc. 4th Int. Conf. Cryptol. India, 2003, vol. 2904, pp. 266–279.
[5]. Y. Dodis, A. Kiayias, A. Nicolosi, and V. Shoup, "Anonymous identification in Ad Hoc groups," in Proc. Int. Conf. Theory Appl.Cryptographic Techn., 2004, vol. 3027, pp. 609–626

Abstract: Insider attack is originating from anauthorized node that had first passed in all the authority steps to access the networking and then involved in compromised. Insider-related research involving the distribution of kernel-based data mining is limited, resulting in substantial vulnerabilities in designing protection against collaborative organizations. Specifically, if more of our assets are going to reside in the cloud, and as increasingly our lives, enterprises and prosperity may depend upon cloud, it is imperative that we understand the scope for insider attacks so that we might best prepare defences.we are going to use String Matching algorithmA substring is a sequence of consecutive contiguous elements of a string, we will denote the substring starting at i and ending at j of string .If the string has same value then only user can send and receive message .It check all types of string for authorized .A prefix of a string S is a substring that starts at position 0, and a suffix a substring that ends at |S|-1. A proper prefix of a S is a prefix that is different to S. Similarly, a proper suffix of S is a suffix that is different to S. The + operator will represent string concatenation.

[1]. A Study Of Privacy Preserving Data Mining Techniques Author:Ms.R.Kavitha, Prof.D.VanathiVolume 3, No.4,
[2]. A Survey on Privacy Preserving Data Mining Techniques Author: Mayil.S and Vanitha.M Vol. 5 (5),
[3]. Achieving Efficient Query Privacy for Location Based Services Author: Femi Olumofin,Piotr K. Tysowski, Ian Goldberg,UrsHengartner 10 (5),
[4]. Application Collusion Attack on the Permission-Based Security Model and its Implications for Modern Smartphone Systems Author: Claudio Marforio, Aur´elienFrancillon, SrdjanCapkun 09
[5]. Collusion-resistant Spatial Phenomena Crowdsourcing via Mixture of Gaussian Processes Regression. Author:Qikun Xiang, IdoNevat, Pengfei Zhang, Jie Zhang

Abstract: Analyze and improve the health care of the baby from the weak point. By continuous monitoring of the baby we can improve the health and find the defect of the baby. The condition of baby can be detected more accuracy and quick manner then only we can able to terminate the problems and give a better solution. By using the genetic algorithm, we can able to find the various complex problem, the solution of the problem can be identifying and optimize the function for the required problem. If the complexity of the problem is in heavy function, the solution of the problem can be identifying and give the accurate solution for the complexity..

Keywords: aaco – advanced ant colony optimization, pca – principal componenet analysis, sta – stemming and ant colony techniques, pca precise.

[1]. A strategy for quality assurance of land-cover/land-use interpretation results with faulty or obsolete reference data, Author: P.Hofmann, P. Lohmann
[2]. Intensive care unit quality improvement: A "how-to" guide for the interdisciplinary teamAuthor: J. Randall Curtis, MD, MPH; Deborah J. Cook
[3]. A Real Time Patient Monitoring System based on Artificial Neural Fuzzy Inference System (ANFIS), Author: Kajal Singh India Divya Sharma ,Shipra Aggarwal
[4]. A Process Mining Driven Framework for Clinical Guideline Improvement in Critical Care, Author:Carolyn McGregor1 , Christina Catley1 and Andrew James2,
[5]. A Survey of Data Mining Techniques on Medical Data for Finding Temporally Frequent Diseases, Author:Mohammed Abdul Khaleel1 , Sateesh Kumar Pradhan2 , G.N.Dash3 , F. A. Mazarbhuiya4

Abstract: Cloud computing allow the users to take benefit from all of these technologies, it enable the user to access the shared resources through internet. Most of the IT industries today are moving on to cloud to meet their computational requirements with reduced cost. This paper gives an idea about various concepts and it covers the growing trends in the field of cloud computing.

Keywords – Cloud Computing, Cloud Service models, Cloud deployment models, Characteristics, Challenges.

[1]. Bhushan Lal Sahu Rajesh Tiwari, " A Comprehensive Study on Cloud Computing", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 9, September 2012 ISSN: 2277 128X.
[2]. Zhifeng Xiao and Yang Xiao, "Security and Privacy in Cloud Computing", IEEE communications surveys & tutorials, vol. 15, no. 2, second quarter 2013.
[3]. Vijay Varadharajan, and Udaya Tupakula, "Security as a Service Model for Cloud Environment", IEEE transactions on network and service management, vol. 11, no. 1, march 2014.
[4]. Mladen A. Vouk, "Cloud Computing Issues, Research and Implementations", Journal of Computing and Information Technology - CIT 16, 2008, 4, 235–246.
[5]. Rafael Moreno-Vozmediano, Rubén S. Montero, and Ignacio M. Llorente, "Key Challenges in Cloud Computing" Enabling the Future Internet of Services. IEEE, Volume: 17, Issue: 4

Abstract: Classification consists of predicting a certain outcome based on a given input. This research work proposes fuzzy ontology based support vector machine classifier. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. The algorithm tries to discover relationships between the attributes that would make it possible to predict the outcome. The proposed research work focuses on classification problem among the medical dataset to predict heart diseases..

[1]. Fernando Jimenez, Gracia Sanchez, Jose M. Juarez, Multi-objective evolutionary algorithms for fuzzy classification in survival prediction, Artificial Intelligence in Medicine, Volume 60, Issue 3, March 2014, Pages 197-219.
[2]. Jennifer N. Cooper, Lai Wei, Soledad A. Fernandez, Peter C. Minneci, Katherine J. Deans, Pre-operative prediction of surgical morbidity in children: Comparison of five statistical models, Computers in Biology and Medicine, Volume 57, 1 February 2015, Pages 54-65
[3]. M. Akhil jabbar, B.L. Deekshatulu, Priti Chandra, Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm, Procedia Technology, Volume 10, 2013, Pages 85-94
[4]. M.H. Vafaie, M. Ataei, H.R. Koofigar, Heart diseases prediction based on ECG signals' classification using a genetic-fuzzy system and dynamical model of ECG signals, Biomedical Signal Processing and Control, Volume 14, November 2014, Pages 291-296.
[5]. Mohamad M. Al Rahhal, Yakoub Bazi, Naif Alajlan, Salim Malek, Haikel Al-Hichri, Farid Melgani, Mansour A. Al Zuair, Classification of AAMI heartbeat classes with an interactive ELM ensemble learning approach, Biomedical Signal Processing and Control, Volume 19, May 2015, Pages 56-67

Abstract: Data Quality analysis is essential task to perform any transaction based operation particularly in huge data environment. Test case Generation is predefined process and these case can applied as specific requirement. The specification can differ based on test coverage criteria. Automated test cases based on source of the program and its design artifacts, in terms of data ware house it's difficult to test due to the multiple type of data format. A Specific framework is needed to overcome this challenge. This research work described a different dimension to Test case generation also in data analytics scenario.

Keywords: test case generation;data quality test;TCG framework;data transformation testing ;Data Accuracy;

[1]. http://dbmanagement.info/Books/MIX/75899021-DW-Master-Test-Plan2_Informatica.pdf
[2]. Pranali Mahadik, Debnath Bhattacharyya, Hye-jin Kiml, "Techniques for Automated Test Cases Generation: A Review," International Journal of Software Engineering and Its Applications Vol. 10, No. 12 (2016), pp. 13-20.
[3]. Akshat Sharma1 , Rishon Patani1 and Ashish Aggarwal1, "SOFTWARE TESTING USING GENETIC ALGORITHMS" International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.2, April 2016 .

[4]. Chuanqi Tao, Jerry Gao "Quality Assurance for Big Data Application– Issues, Challenges, and Needs" DOI reference number: 10.18293/SEKE2016-166.
[5]. https://www.infosys.com/IT-services/validation-solutions/case-studies/Documents/optimal-data-test-coverage.pdf

Abstract: Big Data processing is required to deliver results continuously or near– ongoing, and it is not significant to create results after a prolonged time of processing. For instance, as users search for information using a search motor, the results that are displayed might be interspersed with advertisements.In this paper, optimized feature selection based classification is proposed to perform classification in Big data improve the classification accuracy.

[1]. Lili Dai and Feng Duan, "Comparison of sEMG-based feature extraction and hand motion classification methods," 2015 11th International Conference on Natural Computation (ICNC), Zhangjiajie, 2015, pp. 881-886.
[2]. M. Mohanty, U. Satija and B. Ramkumar, "Sparse decomposition framework for maximum likelihood classification under alpha-stable noise," 2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, 2015, pp. 1-6.
[3]. S. Huda, J. Yearwood, H. F. Jelinek, M. M. Hassan, G. Fortino and M. Buckland, "A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis," in IEEE Access, vol. 4, no. , pp. 9145-9154, 2016.
[4]. N. T. Hai, N. H. Nghia, T. D. Le and V. T. Nguyen, "A Hybrid Feature Selection Method for Vietnamese Text Classification," 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), Ho Chi Minh City, 2015, pp. 91-96.
[5]. Chamidah and I. Wasito, "Fetal state classification from cardiotocography based on feature extraction using hybrid K-Means and support vector machine," 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, 2015, pp. 37-41.