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Over past decades, Indian Sign Language plays an important role for speech and hearing impaired community. This paper focus on novel classification for the detection of sign language efficiently with the use of multi features. The purpose of this paper is to study the existing classification and recognition techniques. And to propose the methodology for better results. From the set of images, features such as edge, texture, histogram and corner features are extracted efficiently using Canny edge detection, Gabor filter, and Harris corner detection. These features are categorized by the hybrid techniques of classification by the contribution of LS-SVM with Naïve Bayes classifier. Initially median filter is utilized for the elimination of noise. The segmentation of image is accomplished by utilizing wavelet transform. Then the recognized sentence will be displayed as a text format in the final outcome. The proposed technique implemented and the practical outcome shows high recognition rate and achieve high accuracy of detection.

Canny Edge Detection, Gabor Filter, Harris Corner Detection, LS-SVM, Median Filter, Naïve Bayes, Wavelet Transform

[1] Nair AV and Bindu V, “A Review on Indian Sign Language Recognition”, International Journal of Computer Applications, Jan 1, vol. 73, No. 22, 2013.
[2] Arici T, Celebi S, Aydin AS and Temiz TT, “Robust gesture recognition using feature pre-processing and weighted dynamic time warping”, Multimedia Tools and Applications, Springer, Oct 1, vol. 72, No. 3, pp. 3045-62, 2014.
[3] Geetha M, Manjusha C, Unnikrishnan P and Harikrishnan R, “A vision based dynamic gesture recognition of Indian sign language on Kinect based depth images”, In Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), In proceedings of International Conference, IEEE, pp. 1-7, 2013.
[4] Raheja JL, Mishra A and Chaudhary A, “Indian sign language recognition using SVM”, Pattern Recognition and Image Analysis, Springer, Apr 1, vol. 26, No. 2, pp. 434-41, 2016.
[5] Sharma M, Pal R and Sahoo AK, “Indian Sign Language Recognition Using Neural Networks and KNN Classifiers”, ARPN Journal of Engineering and Applied Sciences, 2014.
[6] Khurana G, Joshi G and Kaur J, “Static hand gestures recognition system using shape based features”, In Engineering and Computational Sciences (RAECS), Recent Advances in IEEE, Mar 6, pp. 1-4, 2014.
[7] Rajam PS and Balakrishnan G, “Real time Indian sign language recognition system to aid deaf-dumb people”, In Communication Technology (ICCT), In proceedings of 13th International Conference IEEE, Sep 25, pp. 737-742, 2011.
[8] Adithya V, Vinod PR and Gopalakrishnan U, “Artificial neural network based method for Indian sign language recognition”, In Information & Communication Technologies (ICT), In proceedings of IEEE Conference, Apr 11, pp. 1080-1085, 2013.
[9] Jain S, Raja KS and Mukerjee MP, “Indian Sign Language Character Recognition”.
[10] Deora D and Bajaj N, “Indian sign language recognition. In Emerging Technology Trends in Electronics”, Communication and Networking (ET2ECN) in proceedings of 1st International Conference on IEEE, pp. 1-5, 2012.
[11] Jiménez LA, Benalcázar ME and Sotomayor N, “Gesture Recognition and Machine Learning Applied to Sign Language Translation”, In VII Latin American Congress on Biomedical Engineering CLAIB, Bucaramanga, Santander, Colombia, pp. 233-236, Springer, 2016.
[12] Patil SB and Sinha GR, “Distinctive Feature Extraction for Indian Sign Language (ISL) Gesture using Scale Invariant Feature Transform (SIFT)”, Journal of The Institution of Engineers (India): Series B, Springer, pp. 1-8, 2016.
[13] Kumar P, Gauba H, Roy PP and Dogra DP, “Coupled HMM-based multi-sensor data fusion for sign language recognition”, Pattern Recognition Letters, Jan 15, vol. 86, pp. 1-8, Elsevier, 2017.
[14] Rautaray SS and Agrawal A, “Vision based hand gesture recognition for human computer interaction: a survey”, Artificial Intelligence Review, Jan 1, vol. 43, No. 1, pp. 1-54, 2015.
[15] Priyal SP and Bora PK, “A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments”, Pattern Recognition, Elsevier, Aug 31, vol. 46, No. 8, pp. 2202-19, 2013.
[16] Badi HS and Hussein S, “Hand posture and gesture recognition technology”, Neural Computing and Applications, Springer, Sep 1, vol. 25, No. (3-4), pp. 871-8, 2014.
[17] Hasan H and Abdul-Kareem S, “Static hand gesture recognition using neural networks”, Artificial Intelligence Review, Feb 1, pp. 1-35, Springer, 2014.
[18] Singha J and Laskar RH, “Hand gesture recognition using two-level speed normalization, feature selection and classifier fusion”, Multimedia Systems, Mar, pp. 1-6, Springer, 2016.
[19] Agrawal SC, Jalal AS and Bhatnagar C, “Redundancy removal for isolated gesture in Indian sign language and recognition using multi-class support vector machine”, International Journal of Computational Vision and Robotics, Jan 1, vol. 4, No. 1-2, pp. 23-38, 2014.
[20] Garcia-Zurdo R, “Three-dimensional Face Shape By Local Feature Prediction”, International J, vol. 9, No. 1, pp. 1-0, 2015.
[21] Baranwal N, Singh N and Nandi GC, “Indian sign language gesture recognition using discrete wavelet packet transform”, In Signal Propagation and Computer Technology (ICSPCT), In proceedings of International Conference in IEEE, pp. 573-577, 2014.

Sarita D. Deshpande, Yashwant V. Joshi, "Incessant Signs Recognition via Neoteric Classifier Based on Ls-SVM and Naïve Bayes with the Aid of Multi-Features," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.918-928, 2018.

The aim of this research is to study the social media`s influence among the learners. Also, it investigates the influences of social media in the current educational scenario. The opinion of adult learners between the age groups of 19 to 21 is gathered through well-constructed questionnaire to study the influence of social media on the B.Sc visual communication learners. An opinion survey was also conducted among the B.Sc visual communication learners to study the influence of social media such as WhatsApp, Twitter and Facebook. Adult learners` have self-reliance, maturity, independent thinking capability, and decision-making ability. Even though many research works support the social media`s influence among the adult learners, social media usage affects their exam performance. Furthermore, this research reveals the negative side of social media messages. The adult learners believe the social media messages without any verification. Social media use is very common among the tech-savvy new generation learners. It helps to form the social media groups in the educational set-ups. Also, it helps to communicate the information in group project works, continuous assessment works and internships. There is a link that exists between the high-level exam performance and the usage of social media. There is a link that exists between the social media message usages` duration and exam performance. The social media usage between 4 to 6 hours per day reduces the exam performance. The over usage creates a stress during the learning period. The social media usage between one and less than one hour per day develops the exam performance.

Social media, adult learners, self-reliance, exam performance, group, opinion

[1] Redecker, C, “An Analysis of Social Media Usage in Teaching and Learning” Study on the Impact of Web 2.0 Innovations on Education and Training in Europe, JRC Scientific and Technical Report, EUR 23664 EN: publications 2009.
[2] Ute Kraidy, “Digital Media and Education: cognitive impact of information visualization”, Journal of educational media, ELSVIER Vol.27 Iss.3 2002
[3] Nada Dabbagh and Anastasia Kitsantas, “personal learning environment social media and self regulated learning-a natural formula for connection between natural and non natural learning”, Journal of educational media, ELSVIER Vol.15 Iss-1 2012
[4] Crook, C., Fisher, et al. “Web 2.0 technologies for learning” , The current landscape – opportunities, challenges and tensions 2008.
[5] Guess, “Facebook, meet Blackboard. Retrieved on April 21, from ww.insidehighered.com/news /2008/05/14/sync 2010
[6] Arrington, M. (2009). YouTube EDU launch, So Go to Learning Something. Retrieved on April 23, 2010 from tube-edu-launches/ #ixzz0m5GoZUtZ 2009
[7] Prensky, M. “Digital Natives, Digital Immigrants”. On the Horizon, Vol. 9 No. 5, October 2001.
[8] Berinstein “Wikipedia and Britannica”, Retrieved on April 24, 2010 from com/searcher /mar06 / Berinstein.shtml 2009.
[9] Klopfer, E., Osterweil, S., Groff, J., and Haas, J. (2009) “Exploring the Use and the Impacts of Social Media on Teaching and Learning Science in Saudi”, The Education Arcade: Massachusetts Institute of Technology. 2009.
[10]Youmei Liu, “Social media tool as a teaching resource”, Journal of educational technology development and exchange, Vol.3, Iss.1, 2010.

P. Shanthi, Thulasi Bharathy M., "The Influence of Social Media on Visual Communication Learners," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.929-932, 2018.

The Multi-Radio Multi-Channel Wireless Mesh Network (MRMC WMN) is an emerging technology for promoting a variety of applications. In this paper, the issues of channel assignment have been studied in which channels are assigned to different links and enhance the entire network performance. For MRMC WMN, the issue of channel assignment proved to be NP-complete. In this paper, joint Binary swarm optimization technique (BPSO) and linear programming (LP) has been proposed, in which BPSO algorithm is used to solve the issue of channel assignment by using linear programming model. To deal with these issues, a linear objective function has been used in the BPSO algorithm to evaluate the fitness value of each particle. Moreover, a rate-variable model has been proposed to enhance the network performance, wherein the physical interference model has been used to assess the capacity of the network. The simulation results show that the proposed technique efficiently improve the network performance.

Multi-Radio Multi-Channel Wireless Mesh Networks, Channel assignment, BPSO algorithm, Transmission rate

[1] I.F.Akyildiz and X. Wang, “A survey on wireless mesh networks,” IEEE Communications magazine, Vol.43, Issue.9, pp.S23-S30, 2005.
[2] M.Alicherry, R.Bhatia and L.E. Li, “Joint channel assignment and routing for throughput optimization in multi-radio wireless mesh networks,” In Proceedings of the 11th annual international conference on Mobile computing and networking, pp. 58-72, 2005.
[3] H. Cheng, N. Xiong, G. Chen and X. Zhuang, “Channel Assignment with Topology Preservation for Multi-radio Wireless Mesh Networks,” JCM, Vol.5, Issue.1, pp.63-70, 2010.
[4] J. S. Saini, and B. S. Sohi, “A survey on channel assignment techniques of Multi-Radio Multi-Channel Wireless Mesh Network,” Indian Journal of Science and Technology, vol. 9, pp. 1-8, 2016.
[5] P. Kyasanur, C. Chereddi and N.H. Vaidya, “System extensions for Supporting Multiple Channels, Multiple Radios and Other Radio Capabilities” 2006.
[6] X.Y. Li, A. Nusairat, Y. Wu, Y. Qi , j. Zhao, X. Chu and Y. Liu, “Joint throughput optimization for wireless mesh networks,” IEEE Transactions on Mobile Computing, Vol.8, Issue.7, pp.895-909, 2009.
[7] L. Badia , A. Botta and L. Lenzini , “A genetic approach to joint routing and link scheduling for wireless mesh networks,” Ad Hoc Networks, Vol.7, Issue.4, pp.654-664, 2009.
[8] R. Pries, D. Staehle, M. Stoykova, B. Staehle and P. Tran-Gia, “A genetic approach for wireless mesh network planning and optimization,” In Proceedings of the International Conference on Wireless Communications and Mobile Computing, pp. 1422-1427, 2009.
[9] S. Avallone, I.F. Akyildiz and G. Ventre, “A channel and rate assignment algorithm and a layer-2.5 forwarding paradigm for multi-radio wireless mesh networks,” IEEE/ACM Transactions on Networking (TON), Vol.17, Issue.1, pp.267-280, 2009.
[10] J. S. Saini, and B. S. Sohi, “Optimal Power Control algorithm for Multi-Radio Multi-Channel Wireless Mesh Network,” International Journal of Applied Engineering Research, vol. 13, pp. 2072-2077, 2018.
[11] A. Raniwala and T.C. Chiueh, “Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network,” In Proceedings of 24th Annual Joint Conference of the IEEE Computer and Communications Societies, 3, pp. 2223-2234, 2005.
[12] A. Subramanian, H. Gupta and S.R. Das , “Minimum interference channel assignment in multi-radio wireless mesh networks,” In Proceedings IEEE Press. pp. 481–490, 2007.
[13] T. Liu and w. Liao,“Interference-aware QoS routing for multi-rate multi-radio multi-channel IEEE 802.11 wireless mesh networks,” IEEE Transactions on Wireless Communications, Vol.8, Issue.1, pp.166-175, 2009.
[14] A. Mishra, E. Rozner, S. Banerjee and W. Arbaugh, “Exploiting partially overlapping channels in wireless networks: Turning a peril into an advantage,” In Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement, pp. 29-29, 2005.
[15] J.S. Saini and B.S Sohi, “Channel assignment algorithm based on interference reduction for multi-radio multi-channel wireless mesh networks,” International Journal of Advanced Research in Computer Science, 9(1), 2018.
[16] P. Dutta, S. Jaiswal , D. Panigrahi and R. Rastogi R., “A new channel assignment mechanism for rural wireless mesh networks,” In Proceedings of The 27th Conference on Computer Communications, pp. 2261-2269, 2008.
[17] L. Gao and X. Wang , “A game approach for multi-channel allocation in multi-hop wireless networks,” In Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing, pp. 303-312, 2008.
[18] Y. Song, C. Zhang and Y. Fang, “Joint channel and power allocation in wireless mesh networks: A game theoretical perspective,” IEEE Journal on Selected Areas in Communications, Vol.26, Issue.7, pp.1149 – 1159, 2008.
[19] P. Gupta and P.R Kumar, “The capacity of wireless networks”. IEEE Transactions on information theory, Vol.46, Issue.2, pp.388-404, 2000.
[20] N. Ramachandran , E.M, Belding-Royer, K.C. Almeroth and M.M. Buddhikot., “Interference-Aware Channel Assignment in Multi-Radio Wireless Mesh Networks,” In Infocom, 6, pp.1-12, 2006.
[21] J. Padhye , S. Agarwal, V,N. Padmanabhan, L. Qiu, A. Rao and B. Zill, “Estimation of link interference in static multi-hop wireless networks,” In Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement, pp. 28-28, 2005.
[22] S. Xu and T. Saadawi , “Does the IEEE 802.11 MAC protocol work well in multihop wireless ad hoc networks?,” IEEE communications Magazine, Vol.36, Issue.6, pp.130-137, 2001.
[23] C. Yin, R. Yang and W. Zhu , “Improving channel assignment in wireless mesh network with linear programming,” In Proceedings of 6th International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 113-116, 2016.

G.Kaur, J.S. Saini, "Enhancement of Channel Assignment Based on BPSO Algorithm in Multi-Radio Multi-Channel Wireless Mesh Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.933-939, 2018.

In traditional studies about the classification, there are three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), has been said as the most classifiers at producing excessive accuracies. In this study, Tested and Compared the performances of the kNN, Naïve Baye, Decision Tree, Support Vector Machine, Random Forest, Logistic Regression and Combined model over DOS and Normal attacks. These algorithms are among the most influential data mining algorithms in the research community. The detection of fraudulent attacks is considered as a classification problem. In this experiments have performed on different classification methods with the hybrid model on KDDCup99 Dataset. Here compared classifiers using models accuracy with confusion matrix. Cross-Validation means score used for efficiency. For this experiments used python and R programming for implementation. The different types of attacks are routine, DoS, Probe attacks, R2L, and U2R attacks.

Network intrusion, support vector machine, decision tree, Decision Tree, detection

[1] D.Dennin, “An intrusion-detection model”, IEEE Transactions on Software Engineering, 2007.
[2] J. Frank,“Machine learning and intrusion detection: Current and future directions,” in Proceedings of the National 17th Computer Security Conference, Washington, D.C., 2014.
[3] Lee, W., Stolfo, S., &Mok, K. “A Data Mining Framework for Building Intrusion Detection Model.Proc”. IEEE Symp. Security and Privacy, 120-132, 1999.
[4] Amor, N. B., Benferhat, S., &Elouedi, Z., “Naive Bayes vs. Decision Trees in Intrusion Detection Systems.Proc.” ACM Symp.Applied Computing, 420- 424, 2014.
[5] Mukkamala, S., Janoski, G., &Sung, A., “Intrusion detection using neural networks and support vector machines,” Paper presented at the International Joint Conference. On Neural Networks (IJCNN), 2012.
[6] Heba F. Eid, Ashraf Darwish, Aboul Ella Hassanien, and Ajith Abraham, “Principal Components Analysis and Support Vector Machine based Intrusion Detection System,” IEEE, 2017.
[7] T.Shon, Y. Kim, C.Lee and J.Moon, “A Machine Learning Framework for Network Anomaly Detection using SVM and GA”, Proceedings of the 2015 IEEE, 2015.
[8] KyawThetKhaing (2010), Recursive Feature Elimination (RFE) and k-Nearest Neighbor (KNN) in SVM.
[9] H. Liu and H. Motoda(1998), Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic.
[10] N. Nanda, A. Parikh,“Classification and Technical Analysis of Network Intrusion Detection Systems,” International Journal of Advanced Research in Computer Science, Volume 8, No. 4, May-June 2017.
[11] N. Nanda, A. Parikh, “Network Intrusion Detection System: Classification, Techniques and Datasets to Implement,” International Journal on Future Revolution in Computer Science & Communication Engineering ISSN: 2454-4248 Volume: 4 Issue: 3 106 – 109,2018.
[12] P. Tembhare, N. Shukla. “An Integrated and Improved Scheme for Efficient Intrusion Detection in Cloud”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.74-78, June 2017.
[13] P. Dehariya, “An Artificial Immune System and Neural Network to Improve the Detection Rate in Intrusion Detection System”, International Journal of Scientific Research in Network Security and Communication, Volume-4, Issue-1, Feb- 2016.

Nilesh B. Nanda , Ajay Parikh, "Experimental Analysis of k-Nearest Neighbor, Decision Tree, Naive Baye, Support Vector Machine, Logistic Regression and Random Forest Classifiers with Combined Classifier Approach for NIDS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.940-943, 2018.

RSA cryptosystem is the most widely used public key cryptosystem. There have been many variants to the original RSA proposed in the literature and Rebalanced RSA is one of such modified RSA cryptosystems. This paper provides new designs of Rebalanced RSA which are semantically secure against chosen ciphertext attack as well as adaptive chosen ciphertext attack. The proposed schemes are proved to be more efficient than other schemes like DRSA, Rebalanced RSA and standard RSA.

Ciphertext, Decryption, Encryption, Rebalanced RSA

[1] R. Rivest, A. Shamir, and L. Adleman, “A Method for Obtaining Digital Signatures and Public Key Cryptosystems”, Communications of the ACM , Vol.21, No. 2, pp.120-126, 1978.
[2] Ch. Padmaja, “Number theory: Backbone of RSA cryptography”, Global Journal of Pure and Applied Mathematics, Vol.11, No.5, pp.3495-3500, 2015.
[3] Ch. Padmaja, B. Srinivas, and V.S. Bhagavan, “A Systematic Mapping Study of the Published Research on Cryptanalytic Attacks on RSA”, International Journal of Pure and Applied Mathematics, Vol.119, No. 11, pp.283-291, 2018.
[4] S. Goldwasser and S. Micali, “ Probabilistic Encryption”, Journal of Computer and System Sciences, Vol.28, pp.270–299, 1984.
[5] S. Pradhan, “A Study of Public Key Cryptosystems Based on Factorization”, Ph.D. thesis, Pt. Ravishankar Shukla University, Raipur, India, 2013.
[6] Ch. Padmaja, B. Srinivas, and V.S. Bhagavan, “On the Usage of Aryabhatta Remainder Theorem for Improved Performance of Rprime RSA”, Journal of Theoretical and Applied Information Technology, Vol.96, No.9,pp.2505-2518, 2018.
[7] M.J.Wiener, “ Cryptanalysis of short RSA secret exponents”, IEEE Transactions on Information Theory, Vol.36, No.3, pp.553-558, 1990.
[8] Ch. Padmaja, V.S. Bhagavan, and B. Srinivas, “Enhancing the performance of Rebalanced RSA”, Journal of Computer and Mathematical Sciences, (In Press), 2018.
[9] H. Ghodosi, “ An efficient public key cryptosystem secure against chosen ciphertext attack”, Information system security, Lecture Notes in Computer Science, Vol. 4332, pp.303-314, 2007.
[10] D. Pointcheval, “ New Public Key Cryptosystems Based on the Dependent–RSA Problems”, In Proceedings of Eurocrypt`99, Lecture Notes in Computer Science, Vol.1592, pp.239-254, 1999.
[11] M. Bellare and P. Rogaway, “Optimal Asymmetric Encryption-How to Encrypt with RSA”, Advances in Cryptology - Proceedings of Eurocrypt’94, Lecture Notes in Computer Science, Vol. 950, pp.92–111, 1994.
[12] S.Dubey,R.Jhaggar, R.Verma, and D. Gaur, “Encryption and Decryption of Data by Genetic Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, No.3, pp.42-46.
[13] V. Kapoor, “Data Encryption and Decryption Using Modified RSA Cryptography Based on Multiple Public Keys and ‘n’prime Number”, International Journal of Scientific Research in Network Security and Communication, Vol.1, No.2, pp.35-38.

Ch J.L. Padmaja, V.S. Bhagavan, B. Srinivas, "Improving Rebalanced RSA Against Chosen Ciphertext Attacks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.944-947, 2018.

In this paper, an efficient routing algorithm along with an optimization technique named as CS (Cuckoo search) is used. CS is a metaheuristic algorithm that is used to find the best route between the source node and a destination node, on the basis of healthy function. The properties of each node on the basis of energy consumption and distance basis are found out. Therefore, the node that has efficient energy, to forward the data, along with the smaller distance is selected and hence data packets are forward to that next node. In this way, the process is continuing and the data is delivered successfully to the destination node. At last, the performance parameters such as PDR (packet delivery ratio), data overhead and delay is measured.

VANET, Cuckoo search, Routing algorithm

[1] H. Zhou,, Xu, S., Ren, D., Huang, C., & Zhang, H, “Analysis of event-driven warning message propagation in vehicular ad hoc networks”, Ad Hoc Networks, Vol. 55, pp. 87-96, 2017.
[2] K.. Logeshwari, & Lakshmanan, L, “Authenticated anonymous secure on demand routing protocol in VANET (Vehicular adhoc network)”, In Information Communication and Embedded Systems (ICICES), 2017 International Conference on pp. 1-7, 2017.
[3] P. Vijayakumar, Chang, V., Deborah, L. J., Balusamy, B., & Shynu, P. G., “Computationally efficient privacy preserving anonymous mutual and batch authentication schemes for vehicular ad hoc networks”, Future generation computer systems, Vol.78, pp.943-955, 2018.
[4] R. S. Bali, Kumar, N., & Rodrigues, J. J., “An efficient energy‐aware predictive clustering approach for vehicular ad hoc networks”, International Journal of Communication Systems, Vol.30, issue 2, pp.2924-2931, 2017.
[5] Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., & Zedan, H., “A comprehensive survey on vehicular ad hoc network”, Journal of network and computer applications, Vol.37, pp.380-392, 2014.
[6] Bitam, S., Mellouk, A., & Zeadally, S. , “VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks,” IEEE Wireless Communications, Vol. 22, Issue 1,pp. 96-102, 2015.
[7] He, D., Zeadally, S., Xu, B., & Huang, X. , “An efficient identity-based conditional privacy-preserving authentication scheme for vehicular ad hoc networks”, IEEE Transactions on Information Forensics and Security, Vol. 10, Issue 12, pp. 2681-2691, 2015.
[8] Ren, M., Zhang, J., Khoukhi, L., Labiod, H., & Vèque, V., “ A Unified Framework of Clustering Approach in Vehicular Ad Hoc Networks”, IEEE Transactions on Intelligent Transportation Systems, Vol.19, Issue 15, pp. 1401-1414, 2018.
[9] F. Luo, Wang, S., Gong, Y., Jing, X., & Zhang, L., “Geographical Information Enhanced Cooperative Localization in Vehicular Ad-Hoc Networks.”, IEEE Signal Processing Letters, Vol. 25, Issue 4,pp. 556-560, 2018.
[10] P. Vijayakumar, Chang, V., Deborah, L. J., Balusamy, B., & Shynu, P. G., “Computationally efficient privacy preserving anonymous mutual and batch authentication schemes for vehicular ad hoc networks. Future generation computer systems, Vol. 78, pp.943-955, 2018.
[11] Balico, L. N., Loureiro, A. A., Nakamura, E. F., Barreto, R. S., Pazzi, R. W., & Oliveira, H. A., “Localization Prediction in Vehicular Ad Hoc Networks”, IEEE Communications Surveys & Tutorials 2018..
[12] Suleiman, K. E., & Basir, O., “Flow-Level Simulation for Adaptive Routing Protocols in Vehicular Ad-Hoc Networks,” In Ad Hoc Networks pp. 94-105, 2018.
[13] Ding, Z., Ren, P., & Du, Q., “Mobility Based Routing Protocol with MAC Collision Improvement in Vehicular Ad Hoc Networks. arXiv preprint arXiv:1801.06502, 2018..
[14] Baker, T., García-Campos, J. M., Reina, D. G., Toral, S., Tawfik, H., Al-Jumeily, D., & Hussain, A, “GreeAODV: An Energy Efficient Routing Protocol for Vehicular Ad Hoc Networks”,In International Conference on Intelligent Computing pp. 670-681, 2018..
[15] Bagherlou, H., & Ghaffari, A, “A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks. The Journal of Supercomputing, pp.1-25, 2018.
[16] Harshiny, J. S., P. Roshini, and S. Lakshmi Priya, “A Secured Dual Authentication Scheme for Data Transmission in VANET”, "Vol.2, Issue 2, pp. 739-743, 2017.
[17] Gupta, Ritesh, and Parimal Patel. "An Improved Performance of Greedy Perimeter Stateless Routing protocol of Vehicular Adhoc Network in Urban Realistic Scenarios." Vol.1, Issue1,pp.24-29 2016.

N. Kaur, M.S. Devgan, "A Hybrid Routing Protocol Based on Route Optimization Mechanism for VANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.948-951, 2018.

In our proposed research work, the application of live-wire algorithm has been proposed to segment complex Aerial insulator images along with RP algorithm to extract the features of images. Firstly, Gray Level Co-occurrence Matrix (GLCM) is employed to extract the texture features of image by the rapid Gray Level Co-occurrence Integrated Algorithm (GLCIA). We have categorised extracted texture feature into two: one with the stronger discriminative ability and other with weaker ability. The weaker discriminative ability has optimized by using PCA & RP. Successfully segmenting the complex low contrast aerial images is one of the main focus of this paper.After optimization by PCA, segmentation is done by Global Minimization with Texture Descriptor (GMTD).After optimization by RP, segmentation is done by Live-wire. To analyze the comparative effect of algorithms on optimization & segmentation, we have used Brodatz dataset. We have observed that Random projection is computationally faster than PCA due to random selection of ‘best’ basis vector which improves the computational speed of RP. Live wire is use to fast segmentation as well as improves weight parameter. Our results demonstrate a 20% improvement in overall system speed and 10% improvement in segmentation accuracy when compared with traditional algorithms .Another advantage of using this technique is that the process is fully automatic thus can be used for training of machine learning and AI based algorithms.

GLCM,GLCIA,Segmentation,GMTD

[1] Gonzalez Book “Digital Image Processing”,(3rd edition)
[2] Qing gang Wu, Jubai An, and Bin Lin “A Texture Segmentation Based On PCA & Global Minimization Active Contour Model For Aerial Insulator Images” IEEE journal of selected topics in applied earth observations and remote sensing accepted April 11,2012.
[3] Li Liu and Paul W. Fieguth, Member, IEEE “Texture Classification from Random Features”, IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 3, march 2012
[4] D. A. Clausi, “Comparison and fusion of co-occurrence, Gabor and MRF texture features for classification of SAR sea-ice imagery,”Atmos.-Ocean, vol.39, no.3, pp. 183–194, 2000.
[5] automatic recognition of human settlements in arid regions with scattered vegetation,” IEEE J. Sel. Topics Appl. Earth Observ.Remote Sens. (JSTARS), vol. 4, no. 1, pp. 16–26, 2011
[6] P. R. Chowdhury, B. Deshmukh, A. K. Goswami, and S. S. Prasad, “Neural network based dunal landform mapping from multispectral images using texture features,” IEEE J. Sel. Topics Appl. Earth Observ.Remote Sens. (JSTARS), vol. 4, no. 1, pp. 171–184, 2011.
[7] A. A. Ursani, K. Kpalma, C. C. D. Lelong, and J. Ronsin, “Fusion of textural and spectral information for tree crop and other agricultural cover mapping with very-high resolution satellite images, ”IEEEJ.Sel.Topics Appl. Earth Observ.Remote Sens. (JSTARS) ,vol. , no1 , pp.225–235, 2012.
[8] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification, ” IEEE Trans. Syst., Man, Cybern., vol. 3, no. 6, pp. 610–621, 1973.R. F. Walker, P. T. Jack way, and D. Long staff, “Genetic algorithm optimization of adaptive multiscale GLCM features,” Int. J. Pattern Recogn. Artificial Intell., vol. 17, no. 1, pp. 17–39, 2003.
[9] D.A. Clausi and Y. Zhao, “Grey Level Co-occurrence Integrated Algorithm (GLCIA): A superior computational method to rapidly determine co-occurrence probability texture features,” Comput. Geosci., vol. 29,no. 7, pp. 837–850, 2003.
[10] R.F.Walker, P. T. Jackway, and D. Longstaff, “Genetic algorithm optimization of adaptive multiscale GLCM features,” Int. J. Pattern Recogn. Artificial Intell, vol. 17, no. 1, pp. 17–39, 2003.
[11] W.B. Johnson and J. Lindenstrauss, “Extensions of Lipschitz Mappings into a Hilbert Space,” Proc. Conf. Modern Analysis and Probability, pp. 189-206, 1984.
[12] S.Dasgupta and A. Gupta, “An Elementary Proof of a Theorem of Johnson and Lindenstrauss,” Random Structures and Algorithms, vol. 22, no. 1, pp. 60-65, 2003.
[13] E.Bingham and H. Mannila, “Random Projection in Dimensionality Reduction: Applications to Image and Text Data,” Proc. Seventh ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 245-250, 2001.
[14] S.Dasgupta, “Experiments with Random Projections,” Proc. 16th Conf. Uncertainty in Artificial Intelligence, pp. 143-151, 2000.
[15] X.Z. Fern and C.E. Brodley, “Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach,” Proc. 20th Int’l Conf. Machine Learning, 2003.
[16] E.J. Cande`s and T. Tao, “Decoding by Linear Programming” IEEE Trans. Information Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005.
[17] E.J. Cande`s and T. Tao, “Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies?” IEEE Trans. Information Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
[18] D.L.Donoho, “Compressed Sensing,” IEEE Trans. Information Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
[19] G. Biau, L. Devroye, and G. Lugosi, “On the Performance of Clustering in Hilbert Spaces,” IEEE Trans. Information Theory,vol. 54, no. 2, pp. 781-790, Feb. 2008.
[20] R.G. Baraniuk, M. Davenport, R.A. DeVore, and M. Wakin, “A Simple Proof of the Restricted Isometry Property for Random Matrices,” Constructive Approximation, vol. 28, no. 3, pp. 253-263,2008.
[21] D.Achlioptas, “Database-Friendly Random Projections,” Proc. 20th ACM Symp. Principles of Database Systems, pp. 274-281, 2001.
[22] N. Goel, G. Bebis, and A. Nefian, “Face Recognition Experiments with Random Projection,” Proc. SPIE, vol. 5779, pp. 426-437, 2005.
[23] Li, ping, Trevor J Hastie and Kenneth W. Church “Very sparse random projection” Proceedings of 12th ACM SIKDD international conf.on knowledge discovery and data mining ACM 2006.
[24] P.A.Maturkar, Dr.M.A.Gaikwad “RP-live wire algorithms” Proceeding of by IEEE explorer digital library International Conference On “Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017),pp. 74-78 dt. 21st & 22nd Sept. 2017
[25] P.A.Maturkar, Dr.M.A.Gaikwad “Texture Feature Optimization of complex Images using RP&PCA and segmentation by GMTD” Proceeding by IEEE International conference on “Resent innovation in Electrical, Electronics & communicationEngineering-ICRIEECE-2018”, Bhubaneswar, Dt.27th & 28th July 2018
[26] Xiao-Feng Wang, Hai Min, Yi-Gang Zhang, “Multi-scale local region based level set method for image segmentation in the presence of intensity in homogeneity”, Elsevier March 2015 Wanceng Zhang, Xian Sun, Hongqi Wang, Kun Fu, “A generic discriminative part-based model for geospatial object detection in optical remote sensing images”, Elsevier January 2015.
[27] Wanceng Zhang, Xian Sun, Hongqi Wang, Kun Fu, “A generic discriminative part-based model for geospatial object detection in optical remote sensing images”, Elsevier January 2015.
[28] Jian Yang, Peijun Li, Yuhong He, “A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation”, Elsevier August 2014.
[29] Jing Liu, Peijun Li , Xue Wang, “A new segmentation method for very high resolution imagery using spectral and morphological information”, Elsevier March 2015.
[30] Zhijian Huang, Jinfang Zhang, Fanjiang Xu, “A novel multi-scale relative salience feature for remote sensing image analysis”, Elsevier January 2014
[31] Chao Wang, Ai-Ye Shi, Xin Wang and et al., “A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm”, Elsevier October 2014.
[32] Xiang-Yang Wang, Xian-Jin Zhang and et al., “A pixel-based color image segmentation using support vector machine and fuzzy C-means”, Elsevier September 2012.
[33] Jorge E. Patino, Juan C. Duque ,“A review of regional science applications of satellite remote sensing in urban settings”, Elsevier January 2013.
[34] Zhongwu Wang, John R. Jensen, Jungho Im, “An automatic region-based image segmentation algorithm for remote sensing applications”, Elsevier October 2010.
[35] Jianqiang Gao, Lizhong Xu ,“An efficient method to solve the classification problem for remote sensing image”, Elsevier January 2015.
[36] Saman Ghaffarian, Salar Ghaffarian, “Automatic histogram-based fuzzy C-means clustering for remote sensing imagery”, Elsevier November 2014.
[37] Xueliang Zhang, Pengfeng Xiao, Xiaoqun Song, Jiangfeng She, “Boundary-constrained multi-scale segmentation method for remote sensing images”, Elsevier April 2013.
[38] Stelios K. Mylonas, Dimitris G. Stavrakoudis, John B. Theocharis ,“GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images”, Elsevier December 2013.
[39] Xueliang Zhang, Pengfeng Xiao and et al., “Hybrid region merging method for segmentation of high-resolution remote sensing images”, 2014.
[40] Maire, M.; Fowlkes, C.; Malik, J.; “Contour Detection and Hierarchical Image Segmentation”, IEEE 2011.
[41] Jianyu Chen, Delu Pan, Qiankun Zhu and et al., “Edge-Guided Multi scale Segmentation of Satellite Multispectral Imagery”, IEEE 2012.
[42] P. Brodatz, Texture – A Photographic Album for Artists and Designers. New York: Reinhold, 1968.
[43] A.H.M. Jaffar Iqbal Barbhuiya, K. Hemachandran “Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques”Vol.6, Issue.9, pp.315-323, Sep-2018
[44] S. Pathak, V. Sejwar “A Review on Image Segmentation Using Different Optimization Techniques”Vol.5 , Issue.5 , pp.217-221, May-2017

Pradnya A. Maturkar, M.A. Gaikwad, "Comparison of Texture Extraction & Segmentation of Complex Images using PCA_GMTD & RP-Live Wire Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.952-961, 2018.