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Syntactic parsing is an important undertaking which is required for NLP applications including machine interpretation. It is a testing assignment to build up a subjective parser for morphological rich and agglutinative dialects. Syntactic investigation is utilized to comprehend the linguistic structure of a characteristic dialect sentence. It yields all the linguistic data of each word and its constituent. Likewise, issues identified with it assist us with understanding the dialect in a more point by point way. This writing study is preparation to comprehend the distinctive parser advancement for Indian dialects and different methodologies that are utilized to grow such apparatuses and procedures. This paper gives a study of research papers from surely understood diaries and meetings.

Morphological examination, Syntactic Parsing, NLP

[1] A. Abhilash and P. Mannem, “Bidirectional Dependency Parser for Indian Languages.”
[2] B. R. Ambati, S. Husain, J. Nivre, and R. Sangal, “On the Role of Morphosyntactic Features in Hindi Dependency Parsing,” First Work. Stat. Parsing Morphol. Rich Lang. (SPMRL 2010), no. June, pp. 94–102, 2010.
[3] P. J. Antony and K. P. Soman, “Computational Morphology and Natural Language Parsing for Indian Languages : A Literature Survey,” Ijcset, vol. 3, no. 4, pp. 136–146, 2012.
[4] A. Bharati, S. Husain, B. Ambati, S. Jain, D. Sharma, and R. Sangal, “Two semantic features make all the difference in parsing accuracy,” Proc. 6th Int. Conf. Nat. Lang. Process., vol. 8, no. November, 2008.
[5] A. Bharati, M. Gupta, V. Yadav, K. Gali, and D. M. Sharma, “Simple parser for Indian languages in a dependency framework,” Proc. Third Linguist. Annot. Work., no. August, pp. 162–165, 2009.
[6] A. Bharati and R. Sangal, “Parsing free word order languages in the Paninian framework,” Proc. 31st Annu. Meet. Assoc. Comput. Linguist. -, pp. 105–111, 1993.
[7] P. Bhattacharyya, “Natural Language Processing: A Perspective from Computation in Presence of Ambiguity, Resource Constraint and Multilinguality,” CSI J. Comput., vol. 1, no. 2, pp. 1–11, 2012.
[8] S. Chatterji and D. Roy, “Grammar Driven Rules for Hybrid Bengali Dependency Parsing,” 2009.
[9] S. De, “Structure Simplification and Demand Satisfaction Approach to Dependency Parsing in Bangla.” Proceedings of ICON09 NLP Tools Contest: Indian Language Dependency Parsing, Hyderabad, India, 2009.
[10] P. Gadde, K. Jindal, S. Husain, D. M. Sharma, and R. Sangal, “Improving Data Driven Dependency Parsing using Clausal Information,” Hum. Lang. Technol. 2010 Annu. Conf. North Am. Chapter Assoc. Comput. Linguist., no. June, pp. 657–660, 2010.
[11] A. Ghosh, “Dependency Parser for Bengali : the JU System at ICON 2009,” 2009.
[12] A. P. J, N. J. Warrier Assistant Professor, and S. K. P Professor, “Penn Treebank-Based Syntactic Parsers for South Dravidian Languages using a Machine Learning Approach,” Int. J. Comput. Appl., vol. 7, no. 8, pp. 975–8887, 2010.
[13] S. Jain, N. Jain, A. Tammewar, R. Ahmad, B. Dipti, and M. Sharma, “Exploring Semantic Information in Hindi WordNet for Hindi Dependency Parsing,” no. October, pp. 189–197, 2013.
[14] D. Kulkarni, “Specifying Context free Grammar for Marathi Sentences,” vol. 99, no. 14, pp. 38–41, 2014.
[15] B. V. S. Kumari and R. R. Rao, “Hindi Dependency Parsing using a combined model of Malt and MST,” no. December, pp. 171–178, 2012.
[16] A. B. P. R. Ray V. Harish and S. Sarkar, “Part of Speech Tagging and Local Word Grouping Techniques for Natural Language Parsing in Hindi,” Proc. ICON 2003, 2003.
[17] M. Rahman, S. Das, and U. Sharma, “Parsing of part-of-speech tagged Assamese Texts,” J. Comput. Sci., vol. 6, no. 1, 2009.
[18] S. Ramteke, K. Ramteke, and R. Dongare, “Lexicon Parser for syntactic and semantic analysis of Devanagari sentence using Hindi wordnet,” Int. J. Adv. Res., vol. 3, no. 4, 2014.
[19] B. M. Sagar, G. Shobha, and R. Kumar, “Context Free Grammar (CFG) Analysis for simple Kannada sentences,” Proc. Int. Conf. [ACCTA-2010] Spec. Issue IJCCT, vol. 1, no. 2, p. 2, 2010.
[20] B. M. Sagar, G. Shobha, and R. K. P, “Solving the Noun Phrase and Verb Phrase Agreement in Kannada Sentences,” Int. J. Comput. Theory Eng., vol. 1, no. 3, pp. 288–292, 2009.
[21] N. Saharia, “A First Step Towards Parsing of Assamese Text.”
[22] M. Selvam, A. M. Natarajan, R. Thangarajan, and A. S. Approach, “Structural Parsing of Natural Language Text in Tamil Using Phrase Structure Hybrid Language Model,” vol. 2, no. 3, pp. 463–469, 2008.
[23] K. Singla, A. Tammewar, N. Jain, and S. Jain, “Two-stage Approach for Hindi Dependency Parsing Using MaltParser,” no. December, pp. 163–170, 2012.
[24] “ICON09 NLP TOOLS CONTEST : INDIAN LANGUAGE DEPENDENCY PARSING Hyderabad , India,” no. December, 2009.
[25] P. Ghosalkar, S. Malagi, V. Nagda, Y. Mehta, and P. Kulkarni, “English Grammar Checker,” IJCSE no. 3, pp. 98–100, 2016.
[26] T. Suryakanthi, “Parsing Complementizer Phrases In Machine Translation System,” IJCSE,vol. 4, no. 10, pp. 1702–1709, 2012.

Manisha Prajapati, Archit Yajnik, "Parsing for Indian Languages: A Literature Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.1009-1018, 2018.

Theoretical studies on various cell partitioning methods are lucidly presented in the current research pertaining to design and development of VLSI circuits. Owing to the difficulties in designing complex VLSI systems, it is extremely crucial to partition the large circuit into tiny logic blocks to reduce time complexity, space complexity and power consumption. To envisage the same, this communication scrutinizes a heuristic technique by using various data mining algorithms such as K-means algorithms, K-Nearest Neighbor (K-NN), Fuzzy c-means and Support Vector Machine (SVM) for resolution of complexity in VLSI circuits, where K-NN and SVM are employed for classification purpose and Fuzzy c-means and K-means methodologies are deployed for clustering purpose. The upshot of the research revealed that K-NN and Fuzzy c-means methods bestow optimum result pertaining to VLSI cell partitioning.

K-means algorithms, K-nearest neighbor, Fuzzy c-means, The Support Vector Machines, Partitioning, and Data mining

[1] Brian Von Herzen,VLSI Partitioning of a 2-Gs/s Digital Spectrometer, IEEE journal of solid-state circuits, VOL. 26, NO. 5, MAY1991.
[2] Gan, Guojun, Chaoqun Ma, and Jianhong Wu, Data Clustering: Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and AppliedProbability, SIAM, Philadelphia, ASA, Alexandria, VA,2007.
[3] RajineSwetha R, B. ShekarBabu, SumithraDevi K.A, A Survey of Various Algorithms for Vlsi Physical Design, World Academy of Science, Engineering and Technology International Journal of Electronics and Communication Engineering Vol:5, No:3,2011
[4] Sharadindu Roy, Samar SenSarma, improvement of the quality of VLSI circuit partitioning problem using genetic algorithm, Journal of Global Research in Computer Science, , Volume 3, No. 12, pp. 18-22,2012.
[5] Ramalakshmi and S. Saravanan, Multiple Scan Base Partitioning Technique to Increase the Throughput in VLSI Testing, Indian Journal of Science and Technology, Vol9(29), DOI: 10.17485/ijst/2016/v9i29/90862, August2016
[6] Maninder Kaur , Pradip Kumar Sharma, On solving partition driven standard cell placement problem using firefly-based metaheuristic approach, International Journal of Bio-Inspired Computation,9(2), pp.121–127
[7] Shikha Arora, Neerumalhotra,Rajan Vohra, RajdeepSingh, Hybrid Algorithm PSO and SA in Achieving Partitioning Optimization for VLSI Applications , International Journal of P2P Network Trends and Technology (IJPTT) - Volume 2 Issue 1,pp.1-4,2012
[8] MilonMahapatra, M Malathi and B Srinath. An Interconnectivity based Efficient Partitioning Algorithm of Combinational CMOS Circuits. IJCA Proceedings on National Conference on VLSI and Embedded Systems NCVES(1):18-21, March2013
[9] Indu Saini, Dilbag Singh, ArunKhosla. Detection of QRS-complex using K-nearest neighbor algorithm, Int. J. Medical Engineering and Informatics, 5(1),81-101,2013.
[10] Platt J. Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Scholkopf B, Burges C, Smola A, editors. Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Pressn,pp.185–208,1999.
[11] SubhagataChattopadhyay, Dilip Kumar Pratihar, SanjibChandra De Sarkar, A Comparative study of fuzzy c-means algorithm and entropy-based fuzzy clustering algorithms, Computing and Informatics,vol30,pp. 701–720,2011.
[12] Ganesh K. Venayagamoorthyet al., Particle swarm-based optimal partitioning algorithm for combinational CMOS circuits,Engineering Applications of Artificial Intelligence (2006), doi: 10.1016/j.engappai.2006.06.011.
[13] Ratnesh Kumar Shukla,An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images ,International Journal of Scientific Research in Research Paper . Computer Science and Engineering Vol.6, Issue.3, pp.39-43 , June 2018.
[14] T. Dheepak, Low Power Distributed MAC Protocol Against Various Kinds Of Attacks By Using Traffic Analysis Methodology, International Journal of Scientific Research ,Computer Science and Engineering Vol.6, Issue.3, pp. 1-7 , June 2018

Suryakanta Nayak, Mrutyunjaya Panda, "VLSI Cell Partitioning Using Data Mining Approaches," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.1019-1027, 2018.

The best test of vehicular adhoc network is to distinguish the false node in the network. These false node can cause numerous perilous circumstances to the vehicles. The answer for this is the F measure based VANET.F measure bunch the se of hubs into groups and appoint a load to every hub in light of the contention in the network.The most noteworthy clash causing node set will get most elevated weight value and those node set will considered as false in the network. This permits the organization to identify bogus hubs all the more precisely with greatest accuracy and least review. Framework utilizes a half encryption strategy to lessen the time intricacies in the network. This assists with moving alongthe exactness and proficiency of the network.

F measure, half encryption

[1] Manuel Fogue, Francisco J. Martinez, Member, IEEE, Piedad Garrido, Member, IEEE, “Securing Warning Message Dissemination in VANETs Using Cooperative Neighbor Position Verification”, IEEE transactions on vehicular technology, Vol. 64, Issue . 6, 2015
[2] Zhengming Li, Congyi Liu, and Chunxiao Chigan,” On Secure VANET-Based Ad Dissemination With Pragmatic Cost and Effect Control”, IEEE transactions on intelligent transportation systems, Vol. 14, Issue 1, 2013.
[3] Mina Rahbari and Mohammad Ali Jabreil Jamali,” Efficient Detection of Sybil Attack Based on Cryptography in VANET “,International Journal of Network Security & Its Applications (IJNSA), Vol.3, Issue .6, 2011
[4] Osama Abumansoor, Member, IEEE, and Azzedine Boukerche, Senior Member, IEEE, “A Secure Cooperative Approach for Nonline-of- Sight Location Verification in VANET”, IEEE transactions on vehicular technology, Vol. 61, Issue 1, 2012.
[5] Ming Li, Kai Zeng , Wenjing Lou,” Opportunistic broadcast of event-driven warning messages in Vehicular Ad Hoc Networks with lossy links”, Computer networks, Vol 9, Issue 5, 2011
[6] Soufiene Djahel and Yacine Ghamri-Doudane, “A Robust Congestion Control Scheme for Fast and Reliable Dissemination of Safety Messages in VANETs”, IEEE Wireless Communications and Networking Conference,pp 2264-2269, 2012.
[7] Robert K. Schmidtx, Tim Leinm¨ullerx, Elmar Schoch, “Vehicle Behavior Analysis to Enhance Security in VANETs”, article on telematics and computer networks, 2014

Neethu Maria John, Simy Mary Kurian, Vinodh P Vijayan, Neema George, "False Node Identification in VANETs for Inmproved Security," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.1028-1032, 2018.