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Logical Key Hierarchy is a scalable and efficient method to achieve logarithmic rekeying cost in secure group communication. In applications like pay per view, video conferencing with multiple rekeying operations, the key tree will be unbalanced and will generate worst case rekeying cost. With each join, leave operation we change group key, as well as update all keys along the key path of join/leave user. Key aspect in secure group communication is maintained balanced key tree and achieving logarithmic rekeying cost. In this paper improvement in Non-split balancing high order tree is proposed. I-NSBHO (improved Non Split Balancing High order tree) with proposed Join user algorithm and leave user algorithm maintains balance of tree and always achieve logarithmic rekeying cost. Our experimental result shows the achieved improvement in rekeying cost of I-NSBHO join and leave operations compared to original NSBHO join and leave operations. With Node pruning I-NSBHO improves join cost and maintains logarithmic Rekeying cost for leave operation

Secure Group Communication, NSBHO Tree, Logical Key Hierarchy, Message cost, Rekeying, Key tree, key path, Logarithmic Rekeying Cost)

[1] Takahito Sakamoto, Takashi Tsuji, Yuichi Kaji. "Group key rekeying using the LKH tech-nique and the Huffman algorithm" , 2008 International Symposium on Information Theory and Its Applications, 2008
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[3] H. Lu. "A novel high-order tree for secure multicast key management" , IEEE Transactions on Computers, 2/2005
[4] H. Zhou, M. Zheng and T. Wang, “A Novel Group Key scheme for MANETS”, Advanced in control Engineering and Information Science, Procedia Engineering, IJSIA, 2011, pp. 3388-3395.
[5] S. Zhao, R. Kent and A. Aggarwal, “A Key Management and secure routing integrated framework for Ad-hoc Networks”, Ad-hoc Networks, Elsevier, vol-11, 2013, pp. 1046-1061
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[8] Vinod Kumar, Rajendra Kumar, S.K. Pandey, "A computationally efficient centralized group key distribution protocol for secure multicast communications based upon RSA pub-lic key cryptosystem", Journal of King Saud University - Computer and Information Sciences, 2018.
[9] Aparna S. Pande Y. V. Joshi, Manisha Y. Joshi, “Analysis on Logical Key Hierarchy and Variants for Secure Group Communication”, ICCASP 2018, Book chapter, Springer-Atalntis, AISR, ISSN 1951-6851.
[10] J. Goshi and R.E. Ladner, “Algorithms for Dynamic Multicast Key Distribution Trees,” Proc ACM Symp. Principles of Distributed Computing (PODC 2003), 2003.
[11] C. K. Wong, M. Gauda and S. S. Lam, “Secure Group Communications Using Key Graphs,” IEEE/ACM Transactions on Networking, Vol.8, no.1, pp.16-30, 2000.
[12] P.Vijayakumar, S.Bose, A.Kannan, “Centralized key distribution protocol using the great-est common divisor method”, Computers and Mathematics with Applications , volume 65 1360–1368, 2013

Aparna S. Pande, Yashwant V. Joshi, Manisha Y. Joshi, Lalitkumar Wadhwa, "Analysis of Tree structure for Secure Group Communication Using LKH Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1130-1136, 2019.

Pervasive Computing environment gracefully integrates networked computing devices from tiny sensors to extremely dynamic and powerful devices – with people and their ambient environment. Service discovery is an essential element for pervasive computing to accomplish “anytime, anywhere” computing without users’ active attention to computing devices and network services. Privacy and security issues, however, have not been properly addressed. Therefore, devices and network services are unprotected; personal privacy is sacrificed; and devices and network services are inconvenient to use. In this paper, we will present what is pervasive computing and its applications. In particular, I will focus on how pervasive computing provides various features like history, principle, applications, characteristics, problems and challenges.

ICT, Ubiquitous Computing, Calm Technology

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[3] Z. Song, Y. Labrou, and R. Masuoka. Dynamic service discovery and management in task computing. Mobiquitous, 00:310-318, 2004.
[4] M. Vallee, F. Ramparany, and L. Vercouter. Flexible composition of smart device services. In International Conference on Pervasive Systems and Computing (PSC05), pages 165-171. CSREA Press, 2005.
[5] A.C. Huang and P. Steenkiste. A Flexible Architecture for Wide-Area Service Discovery, The Third IEEE Conference on Open Architectures and Network Programming (OPENARCH 2000), March 26-27, 2000.
[6] C. Adjih, P. Jacquet, and L. Viennot, “Computing Connected Dominated Sets with Multipoint Relays,” Technical Report 4597, INRIA-Rapport de recherche, Oct. 2002
[7] S Mahajan, "Convergence of IT and Data Mining with other technologies ", International Journal of Scientific Research in Computer Science and Engineering, Volume-01, Issue-04, pp (31-37), Aug 2013
[8] Jaskaranjit Kaur and Gurpreet Kaur , "Clustering Algorithms in Data Mining: A Comprehensive Study", International Journal of Computer Sciences and Engineering, Volume-03, Issue-07, Page No (57-61), Jul -2015.

Kiran Mangesh Pradhan, "Pervasive Computing: A New Horizons," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1137-1140, 2019.

Today’s e-book plays an important role in all fields to learn new things through personal computer, laptop or mobile phones. There are various formats available for an e-book. The extensively used format is PDF because it retains the original format of the document. Segmentation is for reusing the content but in existing system the documents are segmented as the text content only. It doesn’t consider the non-text elements such as graphs, tables, and images. In this research layout analysis is performed by extracting both text objects and non-text objects from the PDF document and segmenting the objects separately using Support Vector Machine (SVM) classifiers. Finally we get the output as text objects and non-text objects separately. This method utilizes both bottom up approach for text line extraction and top down approach to divide graph tree created by Kruskal’s algorithm into sub graph which use Euclidean distance between adjacent vertices. Both text and non-text objects are classified using SVM technique. For each segmented text and non-text different dimensional features are extracted for labeling purpose. Several E-book PDF documents are tested and some sample input and output PDF documents are shown in the experimental result.

E-book, PDF, Kruskal’s algorithm, Euclidean distance, SVM

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D. Selvanayagi, "Classification of Text and Images from PDF Using Graph Based Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1141-1146, 2019.

As India is agricultural based country. In a field of agriculture, sustained harvesting must be followed with fixed check of fertility rate of soil because soil nutrient measurement is very essential and plays an important role in proper plant growth and effective fertilization. The more accurate method leads to better future of farmers. In this paper we have reviewed many methods for measuring the soil nutrients. The traditional approach is to perform test in soil testing laboratories where chemical process is performed after drying the soil and other preprocessing but it leads to more efforts and tedious process. As a solution a smarter way in which the level is observed and measured using Photodiodes, Light Emitting Diodes, analog-to-digital converter (ADC), FPGA and NIR Laser. AS a result it will leads to more time saving and detailed measure of nutrients. According to NPK values of soil that are acquired and whether attributes, prediction of future crop is possible. Using various techniques for measuring soil nutrients and according to that nutrients using various classification and machine learning algorithms we can make the prediction of which crop to cultivate. Those Methods are studied analyzed according to various requirements.

Data mining, NPK detection, Optical transducer, Soil fertility, Classification, Crop prediction

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K.B.Kaji, "Review on Soil Analysis for Future Crop Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1147-1150, 2019.

Now a days on road accidents is a major problem of concern. Road accidents happen due to various different reasons such as street development, surge hours, human conduct, drunken driving, red light jumping etc. Increase in urban population have led to increase in road traffic and road accidents. VANET provides various methods to provide road safety. Maximum number of accidents have occurred at intersections with good visibility in urban areas. This paper presents a focused view on the safety of the vehicles at intersecting roads. A scheme is implementing to provide road safety at intersecting roads. In the proposed scheme vehicle to roadside communication is used with Intelligent Traffic Lights (ITLs) and IEEE 802.11p protocol. The advancement in Vehicular Ad hoc Network have led to various organizations, Institutes to focus on the methods of street security and it is now widely used now a days.

VANET, Ad Hoc Network, Accident Prevention, Collision Avoidance, IEEE protocol, ITLs

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Car-Pedestrian Collision using VANET,” Electrical and Computer Engineering Department American University of Beirut, pp. 458-465 IEEE Conference 2017.
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C., “Data-Driven Intelligent Transportation Systems: Survey,” IEEE Transactions on Intelligent Transportation Systems, Vol. 12, Issue 4, pp. 1624-1639, 2011.
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prevention and air pollution control under VANET using cloud environment”, 3rd International Conference on Recent Trends in Engineering & Technology (ICRTET’2014), pp. 900-904, March 2014.
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[19] Centre for Applied Informatics (ZAIK), Institute of Transport Research, German Aerospace Centre, “Sumo,” http://sumo.sourceforge.ne
[20] Yashashwani, Sapna Gambhir, “A Survey On Road Accidents And Vehicle Collision In VANET”, International Journal of Management,
Technology and Engineering, Vol. 8, Issue 5, pp. 182-190, May 2018.
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Mohit Gambhir, Sapna Gambhir, "Road Accident Prevention and Collision Avoidance at Intersection In VANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1151-1155, 2019.

This paper presents passive blind forensic scheme to detect spatial tampering in MPEG-4 (Moving Picture Experts Group-4) digital video. In spatial tampering, small region of frame is copied and pasted at some other location in same frame. A proposed algorithm uses SIFT (Scale Invariant Feature Transform) and RANSAC (Random Sample Consensus) to detect the tampering. In this local features from each frame are extracted using SIFT and those features are matched to identify forged area. At the end RANSAC homography is used to remove the false matching to increase the detection accuracy. The proposed method performance is measured with respect to detection accuracy and computational time and verified on compressed and uncompressed videos. To create test data various geometric alterations used in forgery such as scaling, rotation are considered. The simulation results proves that the proposed method finds the forged area efficiently for all the above mentioned cases with average detection accuracy of 99.5%. The algorithm is tested for various compression rates to check its robustness. The detection accuracy of the algorithm increases as the compression rate increases. The performance of the proposed algorithm is compared with two other methods reported in literature which shows that the proposed scheme has higher detection accuracy compared to other methods. The average computational time observed is 0.56 seconds.

Spatial tampering, Forgery detection, SIFT, RANSAC, Forensic scheme

[1] K. Sitara, B. M. Mehtre, “Digital video tampering detection: An overview of passive techniques”, Digital Investigation,vol 18,pp 8-22,2016.
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[3] S. Upadhyay and S. K. Singh, “Video authentication: Issues and challenges,” International Journal of Computer Science, vol. 9, no. 1-3, pp. 409–418,2012.
[4] H. Yin, W. Hui, H. Li, C. Lin, and W. Zhu, "A Novel Large-Scale Digital Forensics Service Platform for Internet Videos," IEEE Transactions on Multimedia, vol. 14, pp. 178-186, 2012.
[5] T. Stütz, F. Autrusseau, and A. Uhl, "Non-blind structure-preserving substitution watermarking of H. 264/CAVLC inter-frames,” IEEE Transactions on Multimedia, vol. 16, pp. 1337-1349, 2014.
[6] Ardizzone, A. Bruno, G. Mazzola, “Copy-move forgery detection by matching triangles of key points”, IEEE Transactions on Information Forensics and Security vol.10, no.10, pp. 2084-2094, 2015
[7] J. Li, X. Li, B. Yang, X. Sun, “Segmentation-based image copy-move forgery detection scheme”, IEEE Transactions on Information Forensics and Security, vol. 10, no 3,pp.507-518,2015.
[8] V. Christlein, C. Riess, J. Jordan, C. Riess, E. Angelopoulou, “An evaluation of popular copy-move forgery detection approaches”, IEEE Transactions on information forensics and security, vol. 7,no 6,pp.1841-1854,2012.
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[12] W. Li and N. Yu, “Rotation robust detection of copy-move forgery,” in Proc. IEEE International Conference on Image Processing ICIP’10, pp. 2113–2116,2010.
[13] H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” Computer Vision–ECCV, pp. 404–417, 2006.
[14] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE computer Society Conference on Computer Vison and Pattern Recognition, CVPR’05, 2005.
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[16] X. Pan and S. Lyu, “Region duplication detection using image feature matching,” IEEE Transactions on Information Forensics and Security, vol. 5, no. 4, pp. 857–867, Dec. 2010.
[17] Bestagini P, Milani Tagliasacchi M, Tubaro S (2013) Local tampering detection in video sequences, IEEE MMSP, pp. 488–493
[18] W. Wang and H. Farid, "Exposing digital forgeries in video by detecting duplication," in Proceedings of the 9th workshop on Multimedia & security, pp. 35-42, 2007.
[19] V. Subramanyam and S. Emmanuel, "Video forgery detection using HOG features and compression properties," in IEEE International Workshop on Multimedia Signal Processing, pp. 89-94, 2012
[20] M. Pun, X. C. Yuan, and X. L. Bi, "Image forgery detection using adaptive over segmentation and feature point matching,” IEEE Transactions on Information Forensics and Security, vol. 10, pp. 1705-1716, 2015.
[21] Ramesh Chand Pandey, Sanjay Kumar Singh and K.K.Shukla, “Passive Copy- Move Forgery Detection in Videos,” 5th International Conference on Computer and Communication Technology, pp.301-306,ICCCT-2014.
[22] C.-C. Hsu, T.-Y. Hung, C.-W. Lin, and C.-T. Hsu, "Video forgery detection using correlation of noise residue," IEEE 10th Workshop on Multimedia Signal Processing, pp. 170-174, 2008.
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Jayashree D. Gavade, Sangeeta R.Chougule, "A SIFT with RANSAC Based Spatial Tampering Detection in Digital Video," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1156-1163, 2019.

Over the past few years, technology has become very dynamic. It is fuelling itself at an ever increasing rate. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. Over the last decades many interesting techniques of Neural Network (NN) were introduced, and shown to be useful in many applications in different fields. Since neural network brings together techniques from different fields such as vowel recognition, pattern recognition, Character recognition, face recognition, pattern matching, image processing, signature verification, data compression, signal processing among many different sources. This paper presents a study survey of various method of vowel recognition. The methods included and analyzed in this survey are Knowledge Based Cascade Correlation (KBCC), Multilayer Perceptron, Formants, and Linear predictive features.

Vowel,speech,Speech recognition (SR), Knowledge Based Cascade Correlation (KBCC), Multilayer perceptron (MLP), linear predictive (LP)

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[2] Mihaela Grigore “Vowel recognition with non linear perceptron”.
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Susheel Kumar Tiwari, Manmohan Singh, Rahul Sharma, "Vowel Recognition of Speech using Data mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1164-1167, 2019.