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Among the three segments in Oil and Gas industry, where huge data is produced every day, upstream segment and specifically production area is moving towards complete automation with the help of technologies such as IIOT and big data. The Oil and Gas Upstream production involves many sensors to collect the data that are attached to all the wells, and this data is processed through IOT Gateways into traditional servers. Such data can be analyzed using multiple analytics to predict and monitor so many components such as the temperature growth, water rate, gas rate, and oil rate etc., The current paper is to analyze the data produced from 5 different wells. Two months data is collected, summarized to find the correlation between the oil, water and gas produced from each well. The purpose is to find the relationship between each of the components. This helps in predictive maintenance and also gives information on how much oil and gas can be produced. When compared with the historical data, if the correlation coefficient is changing abnormally at any specific point the monitoring team must investigate and do proper maintenance.

IIOT- Industrial Internet of Things, IOT – Internet of Things, O&G - Oil & Gas, Corrleation

[1] August 2006 - Global Industry classification Standard (GISC®) – by Stanford and Poor
[2] Abdulelah Bin Mahfoodh, Mohamad Ibrahim, Maan Hawi, Khalid Hakami (2017). "Introducing a Big Data System for Maintaining Well Data Quality and Integriyt in a World of Heterogeneous Environment". Society of Petroleum Engineers, SPE – 188082 – MS
[3] Mohammed Y Aalsalem, Wazir Zada Khan, Wajeb Gharibi, Muhammad Khurram Khan, Quratulain Arshad (2018), "Wireless Sensor Networks in Oil and Gas Industry: Recent Advances, Taxonomy, Requirements and Open Challenges". Journal of Network and computer applications.
[4] Qian Zhu, Ruicong Wang, Qi Chen, Yan Liu and Weijun Qin (2010), "IOT Gateway: BridgingWireless Sensor Networks into Internet of Things". IEEE/IFIP International Conference on Embedded and Ubiquitous Computing.
[5] Chien-Chi Kao, Yi-Shan Lin, Geng-De Wu and Chun-Ju Huang (2017), "A Comprehensive Study on the Internet of Underwater Things: Applications, Challenges, and Channel Models". MDPI;
[6] Nader Mohamed and Imad Jawhar (2008), " A Fault Tolerant Wired/Wireless Sensor Network Architecture for Monitoring Pipeline Infrastructures ", The Second International Conference on Sensor Technologies and Applications
[7] Pan Yi, Xiao Lizhi, Zhang Yuanzhong (2010), "Remote real-time Monitoring System for Oil and Gas Well Based on Wireless Sensor Networks", IEEE.
[8] Tarek R Sheltami, Abubakar Bala, Elhadi M Shakshuki (2016), "Wireless Sensor Networks for leak detection in pipelines- Survey", Springer – Verlag Berlin Heidelberg
[9] Wazir Zada Khan, Mohammed Y Aalsalem, Muhammad Khurram Khan, Md. Shohrab Hossain and Mohammed Atiquzzaman (2017) , “A reliable Internet of Things based on Architecture for Oil and Gas Industry”, ICACT
[10] Mohammed Y Aalsalem, Wazir Zada Khan, Wajeb Gharibi and Nasrullah Armi (2017), “An intelligent oil and gas well monitoring system based on internet of things” IEEE
[11] Gianmarco De Francisci Morales, Albert Bifet, Latifur Khan, Joao Gama and Wei Fan (2016) “IOT Big Data Stream Mining”, KDD, ACM.
[12] Hossein Hassani and Emmanuel Sirimal Silva (2018), “Big data: a big opportunity for the petroleum and petrochemical industry” OPEC.

A. K. Kavuru, R. J. Ramasree, Md. Faisal, "Correlation analysis on Information retrieved from upstream segment production data in O&G Industry," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.524-529, 2018.

testing the refactoring as for formal semantics is dealt with as a test. Refactoring engines like Eclipse, Netbeans and many other contains various kinds of refactoring techniques like move, inline, copy, extract method etc. Usually, developers used to write the test cases to control their refactoring implementations. Few automated testing techniques are used for testing the refactoring implementations of object-oriented systems. In existing, the pre-conditions are recognized and stated that they are extremely stable. In earlier, the testing is done in JRRT (jastAdd refactoring tool) using the process of Alloy Analyzer and the JDolly technique. Using the similar process and techniques, the proposed work makes, testing on the refactoring implementations in the Netbeans Refactoring Engine. Creating the meta-models using the Alloy Analyzer and generating the programs from the model by using the JDolly program generator for applying the testing concept on it. Test Oracles are involved to retain the nature of the programs after implementing the refactoring concept.

Alloy meta-models, Automated testing, Program generation, Refactoring implementations.

[1] M. Schafer and O.de Moor, “Specifying and implementing refactorings,” in proceedings of the 25th ACM International Conference on Object-Oriented Programming, Systems, Languages, and Applications, ser. OOPSLA’ 10. ACM, 2010, pp. 286-3-1.
[2] W.F.O pdyke, "Refactoring Object-Oriented Frameworks,"Ph.D. dissertation, Univ. of Illinois at Urbana-Champaign, 1992.
[3] Melina Mongiovi Member, IEEE, Rohit Gheyi, Gustavo Soares, Márcio Ribeiro, Paulo Borba and Leopoldo Teixeira” Detecting overly strong preconditions in refactoring engines”, IEEE Transactions on Software Engineering DOI 10.1109, 2007.
[4] G. Soares, R. Gheyi, D. Serey, and T. Massoni, “Making program refactoring safer,” IEEE Software, vol. 27, pp. 52–57, 2010.
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[6] G. Soares, M. Mongiovi, and R. Gheyi, “Identifying overly strong conditions in refactoring implementations,” in Proceedings of the 27th IEEE International Conference on Software Maintenance, ser. ICSM ’11. IEEE Computer Society, 2011, pp. 173–182.
[7] D. Soares, R. Gheyi, and T. Massoni, “Automated behavioral testing of refactoring engines,” IEEE Transactions on Software Engineering, vol. 39, pp. 147–162, 2013.
[8] Liangliang kong “Essential of unit testing tool for special testing.”IEEE xplore, 2008.
[9] D. S. Rosenblum, “A Practical Approach to Programming with Assertions”, IEEE transactions on software engineering, vol21(1),1995,pp.1
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[11] Brett Daniel Danny Dig Kely Garcia Darko Marinov “Automated Testing of Eclipse and NetBeans Refactoring Tools”
[12] Narendar Reddy kancharla, Ananda Rao Akepogu, Gopi chand merugu, Kiran kumar Jogu “ A Quantitative methods to detect design defects after refactoring” in software engineering research and practice 2008.
[13] M. Monogivi, R. Gheyi, G. Soares, L. Teixeira, and P. Borba, “Making refactoring safer through impact analysis”, science of ComputerProgramming, vol. 93, pp. 39-64, 2014.
[14] G. Fraser and A. Arcuri, “EvoSuite: automatic test suite generation for object-oriented software,” in proceedings of the 19th European Conference on Foundations of software Engineering, ser. FSE ’11 ACM, 2011, pp.416-419.

B. Nagaveni, A. Ananda Rao, P. Radhika Raju, "Testing Refactoring Implementations of Object-Oriented Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.530-534, 2018.

All vehicles approximately in the world should have a license number as the identifier. In this growing technology more and more number of techniques are developed for license plate recognition. These methods are employed in many areas such as electronic payment systems, traffic activity monitoring and automatic vehicle ticketing. Many techniques are available for LPR System the last decade and various commercial products are reliable under some ideal environments, but it is still a compunction task to recognize license plates from difficult images. The our fully loaded system should work successfully under a different conditions such as sunny day, night time as well as with different colors and complex backgrounds. In this system one important thing is that when any vehicle break the signal captures it image and send the E-mail to that person “ you have break the signal and you should pay this amount of fine”. And also this system is useful at any authorized location for security purpose.

Image processing, Raspberry pi, security

Chih Hu”License Plate Recognition for Moving Vehicles Using a Moving Camera” Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing 2013.
[2] C.V.Keerthi latha,“License Plate Extraction Of Images Using Raspberry Pi” 2015 IJARCET
[3] Rohollah Mazrae Khoshki1, Subramaniam Ganesan, “ImprovedAutomatic License Plate Recognition(ALPR) system based on single pass Connected Component Labeling (CCL)and reign property function. 2015 IEEE.”
[4] Bin Tian, Brendan Tran Morris, “Hierarchical and Networked Vehicle Surveillance in ITS: A Survey”IEEE transactions on intelligent transportation systems, vol. 16, no. 2, april 2015.
[5] Chao Gou, KunfengWang, Yanjie Yao, and Zhengxi Li”Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines”IEEE transactions on intelligent transportation systems, VOL. 17, NO. 4, APRIL 2016
[6] Mahesh Babu K, M V Raghunadh “Vehicle Number Plate Detection and Recognition using Bounding Box Method” International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) 2016.
[7] Sean Lawlor, Student Member, IEEE, Timothy Sider, Naveen Eluru, Marianne Hatzopoulou, and Michael G. Rabbat, Senior Member, IEEE” Detecting Convoys Using License Plate Recognition Data” IEEE transactions on signal and information processing over networks, vol. 2, no. 3, september 2016
[8] Yonghui Jia, Thomas Gonnot and Jafar Saniie” Design Flow of Vehicle License Plate Reader Based on RGB Color Extractor” 2016 IEEE
[9] Nattachai Watcharapinchai Sitapa Rujikietgumjorn National Electronics and Computer Technology Center Thailand.” Approximate License Plate String Matching for Vehicle Re-Identification” 2017 IEEE
[10] Jun-Wei Hsieh” Suspected Vehicle Detection for Driving without License Plate Using Symmelets and Edge Connectivity” IEEE avss , lecce, Italy august 2017.
[11] Official raspberry pi website,www.raspberry.org

Poonam Rajendra Deshmukh, D.M.Chnadwadkar, "Vehicle Identification Using Digital Camera," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.535-538, 2018.

Safety inspection of concrete surfaces like road and bridge surfaces is a continuous and critical task since it is closely related with structural health and reliability of such surfaces. However, it is difficult to find cracks by visual check especially for large and complex concrete surfaces like roads and bridges. Automation in structural strength monitoring of concrete surfaces has generated a lot of interest in recent years, mainly because of introduction of cheap digital cameras and microcontrollers. However, it is still tough task because of the intensity homogeneity of cracks and complexity of the background. Inspired by recent success on applying deep learning to complex computer problems like vision, object detection etc., deep learning based algorithm is proposed in this paper for detection of cracks on concrete surfaces. The proposed algorithm uses Deep Belief Network (DBN), which is trained using an image data set of 600 crack images of concrete surfaces like bridges, roads etc collected by low cost smart phones. By the analysis of experimental data, the algorithm successfully detects images with cracks of various types. The recognition rate is more than 88% compared with 70% accuracy from a typical image based approach. The results are also compared with SVM (Support Vector Machine) and traditional approaches and the recognition rate in DBN approach has been found much higher than in these approaches. This algorithm if implemented on a robotic device or simple vehicle with image acquisition capability can prove very beneficial for non-expert inspectors, enabling them to perform crack monitoring tasks efficiently.

Deep Learning, Deep Belief Networks, Restricted Boltzmann Machine

[1] H. M. La, R. S. Lim, B. B. Basily, N. Gucunski, J. Yi, A. Maher, F. A. Romero, and H. Parvardeh, “Mechatronic systems design for an autonomous robotic system for high-efficiency bridge deck inspection and evaluation,” IEEE/ASME Trans.
[2] T. Nishikawa, J. Yoshida, T. Sugiyama, and Y. Fujino, “Concrete crack detection by multiple sequential image filtering,” Comput. Aided Civil Infrastructure Eng., vol. 27, no. 1, pp. 29–47, 2012.
[3] Z. Zhu, S. German, and I. Brilakis, “Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation,” Autom. Construction, vol. 20, no. 7, pp. 874–883, 2011. 

[4] T. Yamaguchi and S. Hashimoto, “Fast crack detection method for large-size concrete surface images using percolation based image processing,” Mach. Vision Appl., vol. 21, no. 5, pp. 797–809, 2010. 

[5] T. Yamaguchi, S. Nakamura, and S. Hashimoto, “An efficient crack detection method using percolation-based image processing,” in Proc. 3rd IEEE Conf. Ind. Electron. Appl., Jun. 2008, pp. 1875–1880. 

[6] X. Tong, J. Guo, Y. Ling, and Z. Yin, “A new image based method for concrete bridge bottom crack detection,” in Proc. Int. Conf. Image Anal. Signal Process. (IASP), Oct. 2011, pp. 568–571. 

[7] H.N. Nguyen, T.Y. Kam, and P.Y. Cheng, “A novel automatic concrete surface crack identification using isotropic undecimated wavelet transform,” in Proc. Int. Symp. Intell. Signal Process. Commun. Syst. (ISPACS), Nov. 2012, pp. 766–771. 

[8] R. Adhikari, O. Moselhi, and A. Bagchi, “Image-based retrieval of concrete crack properties for bridge inspection,” Autom. Construction, vol. 39, pp. 180–194, 2014. 

[9] R. S. Lim, H. M. La, Z. Shan, and W. Sheng, “Developing a crack inspection robot for bridge maintenance,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), May 2011, pp. 6288–6293.
[10] Chae M., and Abraham D., (2001). ”Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment.” Journal of 
Computing in Civil Engineering 15, Special issue: Information technology for life-cycle infrastructure management, Pages 4–14. doi: 10.1061/(ASCE)0887-3801(2001)15:1(4).
[11] Aws Khanfar, Mohammed Abu-Khousa, and Nasser Qaddoumi, “Microwave near-field non-destructive detection and characterization of dis-bonds in concrete structures using fuzzy logic techniques,” Composite Structures, Volume 62, Issues 3–4, Pages 335-339, ISSN 0263-8223, 10.1016/j.compstruct. 2003. 09.033, 2003

[12] H. Moon, and J. Kim, “Intelligent Crack Detecting Algorithm On The Concrete Crack Image Using Neural network,” Proceedings of the 28th ISARC, Pages 1461-1467, Seoul, Korea, 2011.

[13] Y. Fujita, Y. Mitani and Y. Hamamoto, “A Method for Crack detection on a Concrete Structure,” 18th International Conference on Pattern Recognition, Volume 3, pp. 901–904, 2006. 

[14] Gajanan K. Choudhary and Sayan Dey. Crack Detection in Concrete Surfaces using Image Processing, Fuzzy Logic, and Neural Networks‖. 2012 IEEE fifth International Conference on Advanced Computational Intelligence (ICACI) October 18-20, 2012.
[15] DING Ailing, JIAO Licheng. Pavement distress recognition based on support vector machine [J]. Journal of Changan University: Natural Science Edition, 2007, 27(2):34-37. 

[16] Yan H S, Xu D. An Approach to Estimating Product Design Time Based on Fuzzy Support Vector Machine [J]. Neural Networks IEEE Transactions on, 2007, 18(3):721-731.
[17] H. Roth, L. Lu, J. Liu, J. Yao, A. Seff, C. Kevin, L. Kim, and R. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Transactions on Medical Imaging, 2015.
[18] D. Ciresan, A. Giusti, L. M Gambardella, and J. Schmidhuber, “Deep neural networks segment neuronal membranes in electron microscopy images,” in Advances in Neural Information Processing Systems, 2012, pp. 2843–2851
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[20] A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural information Processing Systems, 2012, pp. 1097–1105
[21] Y. Zhang, K. Sohn, R. Villegas, G. Pan, and H. Lee, “Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction,” in Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 249–258.
[22] J.J. Kivinen, C. K. Williams, and N. Heess, “Visual boundary prediction: A deep neural prediction network and quality dissection,” in Proceedings of International Conference on Artificial Intelligence and Statistics, 2014, pp. 512–521.
[23] S. Varadharajan, S. Jose, K. Sharma, L. Wander, and C. Mertz, “Vision for road inspection,” in Proceedings of 2014 IEEE Winter Conference on Applications of Computer Vision, 2014, pp. 115122.
[24] Y. Hu and C. Zhao, “A local binary pattern based methods for pavement crack detection,” Journal of Pattern Recognition Research, vol. 5, no. 1, pp. 140–147, 2010.
[25] C. Zhang, N. Ji, and G. Wang, Restricted Boltzmann Machines, Chinese Journal of Engineering Mathematics [J], 2015,(2):159-173. 

[26] G. Hinton, "A practical guide to training restricted boltzmann machines," Machine Learning Group, University of Toronto, Technical report, 2010.
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[28] Hinton, G., Osindero, S., and Teh, Y. “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, 2006, pp. 1527-1554.

Khalid Hussain, "Deep Belief Network and its application for Detection of Concrete Surface Cracks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.539-545, 2018.

Plagiarism or the expropriation of another author’s data and the presentation of it as one’s own, is a serious violation of ethics of professionalism. Attempting to take other person’s works, without proper citation is considered as one way of Plagiarism. With the rapid use of internet access and large amounts of big data, copying of content partially or fully has become a common practice. The proposed technique, map-reduced N-Tuple algorithm for distributed computing platform compares the number of attributes of the comparing tuples, at first. If the number of attributes is different, we are sure that the tuples cannot be equal. If the number of attributes is the same, we further sort the values inside of each tuple of both relations. This sorting is necessarily to make sure, that afterwards we can use the equal functionality, provided by Standard Library to find out, whether all corresponding pairs of two tuples compare equal. Here different capacity data sets are tested for plagiarism, which gives output within short time and more accuracy compared to the Standard Copy Analysis Mechanism. Our proposed algorithm is used to compare documents for processing big data using Hadoop and detect plagiarism for performance enhancement.

Plagiarism, N-Tuple, Big data, Hadoop, MapReduce

[1] Manuel Zini,Marco Fabbri,Massimo Moneglia, Alessandro Panunzi,”PlagiarismDetectionThroughMultilevelTextComparison”, Universit`a di Firenze, Italian Department
[2] Mr. Dnyaneshwar R. Bhalerao and Prof. S.S.Sonawane,”A Survey ofPlagiarism Detection Strategies and Methodologies in Text Document”, Department of Computer Engineering, PICT, Pune-411043.
[3] Asim M. El Tahir Ali, Hussam M. Dahwa Abdulla, and V´aclav Sn´aˇsel,“Overview and Comparison of Plagiarism Detection Tools”, Department of Computer Science, VˇSB-Technical University of Ostrava.
[4] Si, Antonio, Hong Va Leong, and Rynson WH Lau. "Check: a document plagiarism detection system." Proceedings of the 1997 ACM symposium on applied computing. ACM, 1997.
[5] Mohamed Elkhidir, Mohannad M. Ibrahim, Tarig A. Khalid, Shawgi Ibrahim, Mohamed Awadalla, “Plagiarism Detection using Free-Text Fingerprint Analysis” Department of Electrical & Electronic Engineering University of Khartoum Khartoum, Sudan
[6] Basel Halak and Mohammed El-Hajjar, “Plagiarism Detection and Prevention Techniques In Engineering Education” Electronics and Computer Science University of Southampton, Southampton, UK
[7] Natalya Shakhovska, Iryna Shvorob, “The method for detecting plagiarism in a collection of documents” COMPUTER SCIENCE & INFORMATION TECHNOLOGIES, (CSIT’2015) LVIV, UKRAINE
[8] Shikha Jain, Parmeet Kaur, Mukta Goyal, Dhanalekshmi G., “CPLAG: Efficient Plagiarism Detection using Bitwise Operations” Department of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, India.
[9] Jayshree Dwivedi, Prof. Abhigyan Tiwary,,“Plagiarism Detection on Bigdata Using Modified Map-Reduced Based SCAM Algorithm”, Department of Computer Science and Engineering, SIRTS Group of Institute Bhopal, India.
[10] Mrs. Parminder Kaur ., “Methods for Web-Spam Detection on web: Principles and Algorithms”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.119-125, 2018.
[11] Amit Palve, Ajit Patil, Amol Potgantwar, "Big Data Analysis Using Distributed Approach on Weather Forecasting Data", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.39-43, 2017.

Thanu Kurian, Manukuru Hymavathi,Tina Thomas , "Plagiarism Detection on BigData Using Modified Map-Reduced Based N-Tuple Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.546-549, 2018.

recognition of patterns. For a facial image in order to identify the accurate age huge face data is supposed to be attached to the age labels in order to make the algorithms more effective. On the utilization of training data which is labelled weakly or is either unlabelled this imposes a constraint. For example, in the social networks huge number of human photos is there. No age label is offered by these images but the age difference can easily be derived for the pair of an image when a person is same. The age accuracy estimation can be brought about by the suggested scheme based on novel learning to take benefit of data which is labelled weakly with the help of CNN which is an abbreviation of Convolution neural network. In case of repair of an image, the divergence suggested by Kullback-Leibler is applied and this is done to embed the information which is different on the basis of age. The loss of entropy and cross entropy is applied adaptively on all the images in order to get a single and unified peak value. To drive the neural network so as to understand the gradual ages from the information of age differentiation the combination of these losses are designed. With one hundred thousand images of faces which are attached along with their data taken we can also contribute to a data set. With the personal identity and time stamp each image is labelled. It is shown by the two aging faces data bases on the experimentation analysis that for this kind of learning system there are a lot of advantages and one can also achieve state to art performance.

Age estimation, age difference, convolution neural networks, K-L divergence distance

Dyer,Locally adjusted robust registration for human age estimation.In Proceedings of the 2008 IEEE work shop on Applications of Computer vision,pages 1-6,2008
[2]. Jiwen Lu, Venice Erin Liong, and Jie Zhou. Cost-fragile neighborhood parallel incorporate learning for facial age estimation. Picture Processing, IEEE Exchanges on, 24(12):5356– 5368,2015
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[4]. Y.Taigman,M.Yang,M.Ranzato,L.Wolf,Deepface:closingthegaptohumanlevelperformanceinfaceverification,in:ConferenceonComputerVisionandPatternRecognition(CVPR),2014,IEEE,2014,pp.1701–1708.
[5]. M.Yang,S.Zhu,F.Lv,K.Yu,Correspondencedrivenadaptationforhumanprofilerecognition,in:ConferenceonComputerVisionandPatternRecognition(CVPR),2011,IEEE,2011,pp.505–512.
[6]. Xin Geng, Chao Yin, and Zhi-Hua Zhou. Facial age estimation by picking up from stamp scatterings. Case Analysis and Machine Knowledge, IEEE Transactions on, 35(10):2401– 2412, 2013

Mittala Thulasi, Chandra Mohan Reddy Sivappagari, "Automatic Human Age Estimation System for Face Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.550-555, 2018.

Alert based systems for real time suspicious activity detection is an active research area. In this work we have worked on abandoned object detection that is a type of suspicious activity. Real time video surveillance systems that are used in places such as airports, railway stations or different public places, can bring security to an upper level. Security is a major issue in this era. So, it has become crucial to set up proficient risk recognition frameworks that can identify and perceive possibly risky circumstances and alert the authorities to make a suitable move. The video surveillance systems that are used for security reasons require more intellectual and more robust technical directives. In this Proposed research work is to detect abandoned objects in real time. This work depicts a framework that perceives the event of somebody leaving baggage unattended in public areas either intentionally or mistakenly. Tracking of intentionally left object is a major problem as it imposes serious security risks. Also, proper tracking of mistakenly left object is another issue to resolve. Therefore, proposed system invokes an alert mail to the authorized person whenever it encounters a left object. This system can be used in railway stations, air ports where we want to detect real time abandoned objects. The video surveillance systems that are used for security reasons require more intellectual and more robust technical directives.

Video surveillance; Object detection; Background subtraction; Background buffer; Moving object detection

[1] Mathur, Garima, and Mahesh Bundele. "Research on intelligent video surveillance techniques for suspicious activity detection critical review." Recent Advances and Innovations in Engineering (ICRAIE), 2016 International Conference on. IEEE, 2016.

[2] Panchal, Payal, et al. "A review on object detection and tracking methods." International Journal for Research in Emerging Science and Technology 2.1 (2015): 7-12.

[3] Dedeoglu, Yigithan. "Moving object detection, tracking and classification for smart video surveillance." Master`s Thesis, Bilkent University, Ankara, pp2 (2004).

[4] Gupta, Aditya, et al. "Real-Time Abandoned Object Detection Using Video Surveillance." Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing. Springer, New Delhi, 2016.

[5] Parihar, Vikramsingh R., and Anagha P. Dhote. "A Novel Approach to Real Time Face Detection and Recognition." International Journal of Computer Sciences and Engineering (IJCSE) 5.9 (2017): 62-67.

[6] Tavanai, Aryana, et al. "Carried object detection and tracking using geometric shape models and spatio-temporal consistency." International Conference on Computer Vision Systems. Springer, Berlin, Heidelberg, 2013.

[7] Ratnesh Kumar Shukla1, Ajay Agarwal.” An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images” International Journal of Scientific Research in Computer Sciences and Engineering (IJSRCSE), Vol.6, Issue.3, pp.22-26 , June (2018)

[8] Lee, Chan-Su, and Ahmed Elgammal. "Carrying object detection using pose preserving dynamic shape models." International Conference on Articulated Motion and Deformable Objects. Springer, Berlin, Heidelberg, 2006.

[9] Senst, Tobias, Rubén Heras Evangelio, and Thomas Sikora. "Detecting people carrying objects based on an optical flow motion model." Applications of Computer Vision (WACV), 2011 IEEE Workshop on. IEEE,2011.

[10] Senst, Tobias, et al. "Detecting people carrying objects utilizing lagrangian dynamics." Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on. IEEE, 2012.

[11] Saindane, Amrut C., and Pravin S. Patil. "An Efficient Human Recognition Using Background Subtraction and Bounding Box Technique for Surveillance Systems." International Journal of Computer Sciences and Engineering (2016): 72.

[12] Punam Mahesh Ingale.,”The importance of digital image processing and its applications” International Journal of Scientific Research in Computer Sciences and Engineering (IJSRCSE), Vol.06 , Issue.01 , pp.31-32, Jan-2018.

Hemant Vyas, Shraddha Masih, "Alert Based System for Real-time Suspicious Activity Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.556-561, 2018.

A wireless network with the advantages of sensing and processing information is referred to as a Wireless Sensor Network (WSN). It consists of a small sensor node with sensors, a battery, a microprocessor and a storage medium. It is an economical and simple solution for a variety of applications. The openness of wireless sensor networks makes it impossible to cope with various security threats. Several security attacks, black holes, wormhole attacks, DDOS attacks, etc., can jeopardize information and sensor nodes in the network. Distributed Denial of Service (DDoS) attacks are such attacks, the purpose of which is to destroy the network by exhausting resources. Attackers not only send worthless messages to increase network traffic, but also reduce the life of nodes and networks. In WSN, the lifetime of the network is proportional to the battery capacity. Therefore, depleting battery power directly reduces the life of the node. This research work has deal with the mitigation and prevention of DDoS attack by using Artificial Neural Network (ANN) as a classification algorithm. The simulation has been done in CLOUDSIM environment. The main of the work is to lessen the strength of attack with its prevention from reaching it to the victim with the anomaly detection system with the algorithm being proposed. Parameters, such as energy consumption, delay and PDR (packet delivery ratio) has been considered for the evaluation of the proposed

WSN (Wireless sensor network), DDoS (Distributed Denial of service), CLOUDSIM, Energy consumption

[1] I.U. Hassan, A. Kaur, “Prevention and Detection of DDoS Attack on WSN,” International Journal of Research Culture Society, Vol.2, Issue.2, pp. 245-249, 2018.
[2] S. Nagar, S.S.Rajput, A.K. Gupta, M.C. Trivedi, “Secure Routing against DDoS Attack in Wireless Sensor Network,” In Proceedings of the IEEE International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1-6, 2017.
[3] P. Pandey, M. Jain, R. Pachouri,“DDoS Attack On Wireless Sensor Network: A Review,” International Journal of Advanced Research in Computer Science, Vol.8, Issue.9, pp. 227-229, 2017.
[4] S. Dhuria, M. Sachdeva,“Detection and Prevention of DDoS Attacks in Wireless Sensor Networks,” in Networking Communication and Data Knowledge Engineering , Perez G., Mishra K., Tiwari S., Trivedi M. (eds), Springer, Singapore, pp. 3-13, 2018.
[5] K. Kaushal, V. Sahni, “Early Detection of DDoS Attack in WSN,” International Journal of Computer Applications, Vol.134, Issue.13, pp. 14-18, 2016.
[6] R. Upadhyay, U.R. Bhatt, H. Tripathi, “DDOS Attack Aware DSR Routing Protocol in WSN,” International Conference on Information Security & Privacy (ICISP), pp. 68-74, 2016.
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[9] I.U. Hassan, A. Kaur, “Literature Review on Prevention and Detection of DDoS Attack,” International Journal of Computer Engineering and Applications, Vol.12, Issue.4, pp. 260-266, 2018.
[10] S. Patil, S. Chaudhari, “DoS Attack Prevention Technique in Wireless Sensor Networks,” In Proceedings of the ELSEVIER International Conference on Communication, Computing and Virtualization (CCV), pp. 715-721, 2016.
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Sumanjit Kaur, Mohit Marwaha, Guresh Pal Singh, "Detection and Prevention of DDoS Attacks in WSN using Artificial Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.562-566, 2018.

Biometrics defines quantifiable characteristics of measuring features of biological organism. 3D Face recognition is a promising trend in Computer Vision and Image Processing research. Face is considered as one of the most attractive biometrics due to its uniqueness. All the anatomical information from the face can be retrieved without physical intervention. In this paper a comparative analysis of region based approach in 3D Face recognition with its diverse applications in security and autism prediction is highlighted. Experiments are done on Bosphorus dataset and a third party dataset, reports a verification rate of 95.3% at 0.1% false acceptance rate. In Identification scenario the Rank one recognition rate is 99.3%. Running time of Modified Face Recognition Algorithm (MFRA) is determined and analyzed.

Biometrics, 3D Face Recognition, MFRA, Verification Rate, False Acceptance Rate, Rank One Recognition ,ASD

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R. Reji, P. Sojan Lal, "3D Face Recognition as a Biometrics and its Diverse Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.567-572, 2018.

Biometric is the process which is used to measure people’s distinctive physical and behavioural characteristics with the help of mathematical analysis. The technology is principally used for detection and right to use management, or for distinguishing people WHO area unit beneath police work. Now a days used biometric systems are of face, fingerprints, iris, retina, signature, palm print, identification and so on to see a person’s identity. In this paper, we have a tendency to contemplate face and fingerprint features for authentication and confirmation. Victimisation this knowledge we have a tendency to project a model for authentication in multimodal biometry that is typically referred to as Context-Sensitive Exponent Associative Memory Model (CSEAM). CSEAM applied on biometry patterns and afford security for the data. In the first step of this paper, Discrete Wavelet Transformation (DWT) face and finger can be applied at first and then Fusion can be applied . In the second stage Principle Component Analysis (PCA) can be applied at first and then Singular Value Decomposition (SVD) can be applied to extract features. In the third stage these features can be stored for authentication and verification in smart cards). In CSEAM model, exponential Kronecker product applied for verification and authentication on input samples. Verification and authentication can be done using different key sizes. This paper shows better results for the key size of 8x8 by using DWT while comparing to the Pavan Kumar K[1] et all.

Biometric, Discrete Wavelet Transformation, exponential kronecker product

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Alapati Kavitha, M.V Rama Krishna,N. Venkata Ramana Gupta, PESN Krishna Prasad, "Feature Selection using DWT+SVD for Fusion Based Multi model Authentication," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.573-577, 2018.