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The paper is based on the review of several research studies carried out on different mobile operating systems. A mobile operating system (or mobile OS) is an operating system for phones, tablets, smart watches, or other mobile devices which acts as an interface between users and mobiles. The use of mobile devices in our life is ever increasing. Nowadays everyone is using mobile phones from a lay man to businessmen to fulfill their basic requirements of life. We cannot even imagine our life without mobile phones. Therefore, it becomes very difficult for the mobile industries to provide best features and easy to use interface to its customer. Due to rapid advancement of the technology, the mobile industry is also continuously growing. The paper attempts to give a comparative study of operating systems used in mobile phones on the basis of their features, user interface and many more factors.

Mobile Operating system, iOS, Android, Smartphone, Windows

[1] K. Divya,S. Venkata KrishnaKumar “comparative analysis of smart phone operating systems android, apple ios and windows” in International Journal of Scientific Engineering and Applied Science (IJSEAS),Volume-2, Issue-2,pp. 432-438,February 2016.
[2] Ram Sundar G “A Comparative Study of Mobile Operating Systems”, International Journal of Recent Trends in Engineering and Research(IJRTER), Vol 02,Issue 02, pp. 57-61,Feb 2016.
[3] Kiran Bala, Sumit Sharma , Gurpreet Kaur “A Study on Smartphone based Operating System”, International Journal of Computer Applications, Vol 121,pp. 17-22,July 2015.
[4]J.kiran kumar, D.Yugandhar “A Study on Current Mobile Operating Systems”,International Journal of Scientific & Engineering Research, Volume 8, Issue 5, May-2017.
[5]Ahmed Ali “A Review of Different Comparative Studies on Mobile Operating System”, Research Journal of Applied Sciences, Engineering and Technology, Vol 7,Issue 12,pp 2578-2582,2014.

Rina, "A Comparative Analysis of mobile Operating Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.69-73, 2018.

The study of the research is to collect the information like users browsing place, browser used, IP Address, latitude, longitude and operating system automatically when the time of client browsing. With the help of geocast, Geo-Location and the collected information, it will map the client browsed position in the Google Map. This is a web based research used to track all kind of website information in any specific platform. This system collects a large number of users’ browsing information to analyze the website performance, load balancing, web traffic and finding errors for taking the essential improvements. This research is also to find the shortest path from the client browsing place to website hosted server. It will reach the specific routers to find the shortest path. Each and every router will have the router name and IP Address. It collects the router information by using tracert command and shows the geographic location in the Google Map. It shows the various kinds of information’s like, Client captured IP Address, Routers location and IP Address, Total hits of the website right from the beginning, Country based website hits, Region based website hits, City based website hits, Browser based website hits and Platform based website hits. Each and every information, there is a graph for easy identification.

— Geo-location, IP Addresses and Domain Names, Internet Control Message Protocol, Transmission Control Protocol

[1]. M.Chandran, A.V.Ramani, " A Study on Website Quality Evaluation based on Sitemap" International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.55-59, 2014.
[2]. Information on "Geolocation api specification". available at http://dev.w3.org/geo/api/spec-source.html. last accessed at 18 October , 2018
[3]. Information on "https://en.wikipedia.org/wiki/Geolocation" available at en.wikipedia. last accessed at 10 December 218
[4]. Shwaita Kodesia, PremNarayan Arya " Energy Efficient Geographical Routing Protocol with Location Awareness in Mobile", International Journal of Computing, Communications and Networking,vol.2,Issue.1 July-August -2012
[5]. Davis, C., et al. RFC 1876 - A Means for Expressing Location Information in the Domain Name System. Jan. 1996. 30 Jan. 2003. http://www.ckdhr.com/dns-loc/rfc1876.txt.
[6]. Fadia, Ankit. Tracing The Traceroute. 24 Jan. 2002. 30 Jan. 2003 http://www.ankitfadia.com/traceroutew.html.
[7]. Geobytes HomePage. 11 Apr. 2003. GeoBytes, Inc. 18 Apr. 2003 http://www.geobytes.com.
[8]. Moore, David, Jim Donohoe, and Ram Periakaruppan. Where in the World is netgeo.caida.org? Cooperative Association for Internet Data Analysis. 30 Jan. 2003
[9]. Connolly, Gene M., Anatoly Sachenko and George Markowsky. “Distributed Traceroute Approach to Locating IP Devices.” Proceedings of IEEE Second Workshop on Intelligent Data Acquisition and Advanced Computing Systems, September 8-10, L’viv, Ukraine. ACCEPTED.
[10]. Bo Han, Paul Cook and Timothy Baldwin. “Text-based twitter user geolocation prediction”. Journal of Artificial Intelligence Research Volume 49 Issue 1, January 2014, Pages 451-500

M. Chandran, "Geo Routing Based Algorithm Investigation to Track Website Quality Using Geo-Location," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.74-80, 2018.

Databases act as repository of data for various applications. In today’s scenario every organization has the database to store data and database management systems to access that data. SQL (Structured Query Language) is the language of database management system which is a specific language used to write statements against Relational Database Management Systems to retrieve and manipulate data. But the common user asks query in his/her natural language such as Punjabi Language. That natural language query is not understandable to the computer and hence RDBMS cannot process that query. That natural language query can be processed through Natural Language Processing to understand what kind of data the user wants to retrieve. From that natural language query, we need to retrieve data from the database. To automate the process, various data elements need to be extracted from the Punjabi Language query. These data elements include Entity, Attributes, Condition etc. This paper explains the process of extracting Data Elements from the Punjabi Language query.

Natural Language Processing, Punjabi Language Processing, Data Element Extraction, Structured Query Language (SQL)

[1] Joseph, Sethunya & Sedimo, Kutlwano & Kaniwa, Freeson & Hlomani, Hlomani & Letsholo, Keletso, “Natural Language Processing: A Review”,Natural Language Processing: A Review, 6, 207-210, 2016
[2] Reshamwala, Alpa & Mishra, Dhirendra & Pawar, Prajakta, “Review on Natural Language Processing”, IRACST – Engineering Science and Technology: An International Journal (ESTIJ), 3, 113-116, 2013
[3] Srivastava, Siddhant & Shukla, A & Tiwari, Ritu, “Machine Translation : From Statistical to modern Deep-learning practices”, arXiv preprint, arXiv:1812.04238, 2018
[4] Sundar Ram R, Vijay & Lalithadevi, Sobha, “Overview of Verb Phrase Translation in Machine Translation: English to Tamil and Hindi to Tamil”, Conference: the 10th annual meeting of the Forum for Information Retrieval Evaluation, 6-10. DOI: 10.1145/3293339.3293341, 2018
[5] T.K, Bijimol & , Professor & Abraham, Johnt, “A Study of Machine Translation Methods An Analysis of Malayalam Machine Translation Systems”, Conference: NCILC-14, At CUSAT, Cochin, 2014
[6] Sinhal, Ruchika & Gupta, Kapil, “Machine Translation Approaches and Design Aspects” IOSR Journal of Computer Engineering, 16, 22-25, DOI:10.9790/0661-16122225, 2014
[7] Fadiel Alawneh, Mouiad & Sembok, Tengku, “Rule-Based and Example-Based Machine Translation from English to Arabic”, Conference: Bio-Inspired Computing: Theories and Applications (BIC-TA), 2011 Sixth International Conference, 343 – 347, DOI: 10.1109/BIC-TA.2011.76, 2011
[8] M. D. Okpor, “Machine Translation Approaches: Issues and Challenges”, IJCSI International Journal of Computer Science Issues, Vol. 11, Issue 5, No 2, September 2014
[9] Shantanoo Dubey, “Survey of Machine Translation Techniques”, International Journal of Advance Research in Computer Science and Management Studies, Special Issue, Volume 5, Issue 2, February 2017
[10] Krishnamurthy, Parameswari, “Development of Telugu-Tamil Transfer-Based Machine Translation System: An Improvization Using Divergence Index”, Journal of Intelligent Systems, DOI: 10.1515/jisys-2018-0214, 2018
[11] Dash, Niladri & Ramamoorthy, L., “Corpus and Machine Translation”, In book: Utility and Application of Language Corpora, pp, 193-217, DOI: 10.1007/978-981-13-1801-6_12, 2019
[12] Mahata, Sainik Kumar & Mandal, Soumil & Das, Dipankar & Bandyopadhyay, Sivaji, “SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences”, Proc. of ICON-2018, Patiala, India, pages 175–182, December 2018
[13] Carl, Michael & Way, Andy & Daelemans, Walter, “Recent Advances in Example-Based Machine Translation”, Computational Linguistics, 30, 516-520, DOI: 10.1162/0891201042544866, 2004

Harjit Singh, Ashish Oberoi, "Extracting Data Elements from Punjabi Language Query," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.81-85, 2018.

Cloud data security is recognized as making the data confidential along with proper authentication. The Galois/Counter Mode (GCM) is used to provide data confidentiality with associated data as authentication. It aims to provide birthday bound security i.e. it is secure up to 2^(n/2) adversarial queries where n is a block size. But in some cases this much security is not sufficient. In this paper, we have proposed a new approach to authenticated encryption with associated data (AEAD), an improved AEAD scheme which can be secure up to approximately 2^n / p adversarial queries where, p = (n/m) , where n is a block size and m is a bit variance. This bit variance is introduced in the encryption process. In the proposed nonce-respecting AEAD scheme a new pseudorandom function is defined and used for implementation. To generate authentication tag universal hash function is used. In this paper security proofs of proposed scheme are given by presenting its construction and its security model.

Authenticated encryption with associated data, beyond birthday bound security, cloud data confidentiality, data authentication

[1] Chanathip Namprempre Mihir Bellare, "Authenticated Encryption: Relations among notions and analysis of the generic composition paradigm," Lecture Notes in Computer Science,Springer-Verlag, vol. 1976, pp. 531–545, July 2007.
[2] C. Jutla, "Encryption modes with almost free message integrity".
[3] V. Gligor and P. Donescu, "Fast encryption and authentication: XCBC encryption and XECB authentication modes.".
[4] M. Bellare, J. Black, andT. Krovetz P. Rogaway, "OCB: A block-cipher mode of operation for efficient authenticated encryption," 2001.
[5] Rogaway P., "Authenticated-Encryption with Associated-Data," in 9th ACM Conference on Computer and Communications Security, Washington,USA, 2002, pp. 98-107.
[6] A. Bogdanov, A. Luykx, B. Mennink, E. Tischhauser, and K. Yasuda E. Andreeva, "Parallelizable and authenticated online ciphers".
[7] S. Fluhrer, C. Forler F. Abed, "Pipelineable on-line encryption".
[8] C. Forler, and S. Lucks E. Fleischmann, "McOE: a family of almost foolproof on-line authenticated encryption schemes".
[9] M. Bellare and C. Namprempre, "Authenticated encryption: relations among notions and analysis of the generic composition paradigm".
[10] P.Jovanovic,B.Mennink,and S.Neves R.Granger, "Improved masking for tweakable blockciphers with applications to authenticated encryption".
[11] J.Viega D.A.McGrewand, "The security and performance of the Galois/counter mode (GCM) of operation".
[12] Dr. Vijay R. Ghorpade Rajani S. Sajjan, "AES-VR:A New Approach for Cloud Data Confidentiality," International Journal of Computer Technology and Applications, Accepted 2018.
[13] Hong-Gang Hu, Qian Yuan Ping Zhang, "Close to optimally secure variants of GCM," Hindawi, vol. 2018, March 2018.

Rajani S. Sajjan, Vijay R. Ghorpade, "GCM-AES-VR : A Scheme for Cloud Data Confidentiality and Authenticity," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.86-94, 2018.

Open loop V/f control of cascaded H-Bridge inverter fed induction motor drive is simulated in PSIM environment. Cascaded H-Bridge three level inverter is used to obtain good quality of output voltage. This method can be implemented by change in the supply voltage and frequency applied to the three phase induction motor at constant ratio. . The proposed system is an effective replacement for the conventional method which produces high switching losses. Phase shifted SPWM control scheme has been used for Cascaded H-Bridge multilevel inverter. Simulation results are presented in this paper to validate the effectiveness of the proposed scheme.

Cascaded H-Bridge Multilevel Inverter, Multicarrier PWM technique, THD

[1] G.R.Slemon, “Electrical machines for variable frequency drives”, IEEE Proceeding, vol.82, no. 8,pp. 1123-1138, August 1994
[2] J. Rodriguez, J.S.Lai and F.Z.Peng, “Multilevel inverters: A survey of topologies, controls and applications,” IEEE Trans. Ind. Electron., vol. 49, pp. 724-738, Aug. 2002.
[3] Dr.Rama Reddy and G.Pandian, “Implementation of Multilevel inverter fed Induction motor Drive,” Journal of Industrial Technology, vol 24, no. 1, April 2008.
[4] M. Malinowski, K. Gopakumar, J. Rodriguez and M. A. Perez, “A Survey on Cascaded Multilevel Inverters”, IEEE Trans. Ind. Electron., vol. 57, NO. 7, pp. 2197-2206, July-2010.
[5] S. Kouro, M. Malinowski, K. Gopakumar and J. Pou, L. G. Franquelo, Bin Wu, J. Rodriguez “ Recent Advances and Industrial Applications of Multilevel Converters”, IEEE Trans. Ind. Electron., vol. 57, NO. 8, pp. 2553-2580, August-2010.
[6] Ahmad Radan and Zahra Daneshi Far, “Optimized Opportunities in Carrier-Based Multilevel PWM Using Degrees of Freedom of Modulation
[7] Mr. C.S. Kamble, Prof. J.G.Chaudhari, Dr. M.V.Aware, “Digital Signal Processor Based V/f Controlled Induction motor Drive” 3 rd ICET in Engg. and Tech. pp. 345-349
[8] Mineo Tsuji, Shuo chen, Shin-ichi Hamasaki,X. Zaho and E.Yamada, “A Novel V/f control of Induction Motors for wide and precise speed operation”, ISPE,SPEEDAM 2008
[9] B. Wu, “High-Power Converters and AC Drives”, New York: Willey-IEEE Press, Mar. 2006.
[10] R. Krishnan, “Electric Motor Drives Modelling, Analysis and Control” Prentice Hall, 2001.

Chirag T Patel, Jignesh B Bhati, "Analysis of Open Loop V/F Control of Three Level Cascaded H- Bridge Inverter Fed Induction Motor Drive," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.95-98, 2018.

System virtualization is the backbone of Cloud computing has been liberalizing its services to distributed data-intensive platforms such as MapReduce and Hadoop. Cloud computing empowers consumers to access online resources using the internet, from anywhere at any time without considering the underlying hardware, technical management and maintenance problems of the original resources. Cloud services are obtained from data centres which are distributed throughout the world. Big Data Applications with resource aware allocation has become an active research area in last three years. The Hadoop framework has been adopted to work efficiently in cloud computing. System virtualization is the backbone of Cloud computing, has been liberalizing its services to distributed data-intensive platforms such as MapReduce and Hadoop. Cloud computing empowers consumers to access online resources using the internet, from anywhere at any time without considering the underlying hardware, technical management and maintenance problems of the original resources.We present a detail study of various resource allocation and other scheduling challenges as well as frameworks for Hadoop Jobs in Cloud Computing.

Hadoop++, Cloud computing, MapReduce, YARN, Resource-allocation

[1] Z. Abbasi, M. Pore, and S. K. S. Gupta. “Online server and workload management for joint optimization of electricity cost and carbon footprint across data centers”. In Proc. IEEE IPDPS, 2014.
[2] R.Udendhran, “A Hybrid Approach to Enhance Data Security in Cloud Storage ”, ICC `17 Proceedings of the Second International Conference on Internet of things and Cloud Computing at Cambridge University, United Kingdom — March 22 - 23, 2017, ACM ISBN: 978-1-4503-4774-7 doi>10.1145/3018.
[3] R Udendhran, K Muth Uramlingam, “A dynamic data-aware scheduling for map reduce in cloud ”, Advanced Computing and Communication Systems (ICACCS), 2017 4th IEEE International Conference, DOI: 10.1109/ICACCS.2017.8014617.
[4] G. Ananthanarayanan, C. Douglas, R. Ramakrishnan, S. Rao, and I. Stoica. “True elasticity in multi-tenant data-intensive compute clusters”. In Proc. of the ACM Symposium on Cloud Computing (SOCC), 2012.
[5] R. Appuswamy, C. Gkantsidis, D. Narayanan, O. Hodson, and A. Rowstron. “Scale-up vs scale-out for hadoop: Time to rethink” In Proc. ACM Symposium on Cloud Computing (SoCC), 2013.
[6] D. Borthakur, J. Gray, J. S. Sarma, K. Muthukkaruppan, N. Spiegelberg, H. Kuang, K. Ranganathan, D. Molkov, A. Menon, S. Rash, R. Schmidt, and A. Aiyer. “Apache hadoop goes realtime at facebook”. In Proc. of the ACM SIGMOD, 2011.
[7] D. Carrera, M. Steinder, I. Whalley, J. Torres, and E. Ayguad,”e. Enabling resource sharing between transactional and batch workloads using dynamic application placement”. In Proc. ACM/IFIP/USENIX Int’l Conf. on Middleware (Middleware), 2008.
[8] D. Carrera, M. Steinder, I. Whalley, J. Torres, and E. Ayguade. “Autonomic placement of mixed batch and transactional workloads”. IEEE Trans. on Parallel and Distributed Systems (TPDS), 2012.
[9] R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. “SCOPE: Easy and efficient parallel processing of massive data sets”. Proc. VLDB Endowment, 1(2):1265–1276, Aug. 2008.
[10] H. Chen, M. K. Cheng, and Y. Kuo.” Assigning real-time tasks to heterogeneous processors by applying ant colony optimization”. Journal of Parallel and Distributed Computing, 71, 2011.
[11] Y. Chen, S. Alspaugh, D. Borthakur, and R. Katz. “Energy efficiency for large-scale mapreduce workloads with significant interactive analysis”. In Proc. of the EuroSys Conference (EuroSys), 2012.
[12] S. Rao, R. Ramakrishnan, A. Silberstein, M. Ovsiannikov, and D. Reeves. Sailfish: “A framework for large scale data processing”. In Proc. of ACM Symposium on Cloud Computing (SoCC), 2012.
[13] A. Jinda, J. Quian-Ruiz, and J. Dittrich. “Trojan data layouts: Right shoes for a running elephant”. In Proc. of ACM Symposium on Cloud Computing (SoCC), 2011.
[14] Y. Guo, J. Rao, and X. Zhou. “shuffle: Improving hadoop performance with shuffle-on-write”. In Proc. Int’l Conference on Autonomic Computing (ICAC), 2013.
[15] J. Dittrich, J.-A. Quian´e-Ruiz, A. Jindal, Y. Kargin, V. Setty, and J. Schad.” Hadoop++: making a yellow elephant run like a cheetah (without it even noticing)”. In Proc. Int’l Conf. on Very Large Data Bases (VLDB), 2010.
[16] V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth, B. Saha, C. Curino, O. O’Malley, S. Radia, B. Reed, and E. Baldeschwieler. “Apache hadoop yarn: Yet another resource negotiator”. In Proc. ACM Symposium on Cloud Computing (SoCC), 2013.
[17] H. Herodotou and S. Babu. “Profiling, what-if analysis, and cost-based optimization of mapreduce programs”. In Proc. Int’ Conf. on Very Large Data Bases (VLDB), 2011.
[18] K. Kambatla, A. Pathak, and H. Pucha. “Towards optimizing hadoop provisioning in the cloud”. In Proc. USENIX, HotCloud Workshop, 2009.
[19] P. Lama and X. Zhou.” Aroma: Automated resource allocation and configuration of mapreduce environment in the cloud”. In Proc. Int’l Conf. on Autonomic computing (ICAC), 2012.
[20] H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F. B. Cetin, and S. Babu. “Starfish: A self-tuning system for big data analytics”. In Proc. Conference on Innovative Data Systems Research (CIDR), 2011.
[21] A. Verma, L. Cherkasova, and R. H. Campbell. “ARIA: automatic resource inference and allocation for mapreduce environments”. In Proc. of the ACM Int’l Conference on Autonomic Computing (ICAC), 2011.
[22] A. Verma, L. Cherkasova, and R. H. Campbell. “Resource provisioning framework for mapreduce jobs with performance goals”. In Proc. ACM/IFIP/USENIX Int’l Middleware Conference (Middleware), 2011.
[23] V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth, B. Saha, C. Curino, O. O’Malley, S. Radia, B. Reed, and E. Baldeschwieler. “Apache hadoop yarn: Yet another resource negotiator”. In Proc. ACM Symposium on Cloud Computing (SoCC), 2013.
[24] Facebook. Hadoop corona: the next version of mapreduce. https://github.com/facebookarchive/hadoop-20/tree/master/src/contrib/corona.
[25] J. Leverich and C. Kozyrakis. “On the energy (in)efficiency of hadoop clusters”. In Proc. USENIX HotPower, 2009.

R. Rengasamy, M.Chidambaram, "Challenges and Opportunities of Resource-Aware Allocation Frameworks for Big data tools in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.99-105, 2018.

Concurrency control is a big problem in shared database system. The management of the concurrent execution of transactions in a multiuser database system is recognized as concurrency control. The aim of concurrency control is to confirm the serializability of transactions in a multiuser database situation. It is significant because the simultaneous execution of transactions over a shared database can create several data integrity and consistency problems i.e. problem of summarization and problem of maintaining statistics. Multiple granularity means which level lock will apply, if we apply the lock on exact level of database, then definitely we may control the concurrency. In this paper we shall reveal in which level lock should apply for prevent above mentioned problems. With the help of Multiple Granularity, we shall reveal how to prevent the problem arise due to concurrency with the help of suitable examples. It will help the students and research scholars to understand that how to prevent the concurrency problems with the help of Multiple Granularity Locking Protocol method.

Multiple Granularity, Implicit Lock, Explicit Lock, Concurrency, Tuples, Database, Table, Record, Attribute

[1]. Philip A. Bernstein and Eric Newcomer, “Locking”, Chapter 6, p.p. 6-1 to 6.35, 2001
[2]. GeeksforGeetks, a computer science Portal for geeks, https://www.geeksforgeeks.org /dbms-concurrency-control-protocol-multiple-granularity-locking/
[3]. Dr. Anil Kumar Singh, “A study of Concurrent transaction execution and their problems in Distributed Database System”, International Journal of Computer Sciences and Engineering Vol.6(10), p.p.767-769, E-ISSN: 2347-2693, Oct 2018
[4]. JavaTpoint, Multiple Granularity, https://www.javatpoint.com/dbms-multiple-granularity
[5]. Goetz Graefe, “Hierarchical locking in B-tree indexes”, HP Labs 1 Goetz.Graefe@HP.com p.p. 18-42.
[6]. Dr. Anil Kumar Singh, “A study of Preventing Concurrency’s Problems using 2-Phase Locking Protocols (2-PL)”, International Journal of Computer Sciences and Engineering Vol.6(11), p.p. 39-42, E-ISSN: 2347-2693, November 2018

Anil Kumar Singh, "Concurrency control by Multiple Granularity of Locks in Multiusers Database Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.106-108, 2018.

Wireless Sensor Network (WSN) consists of sensor nodes which sense, process and transmit information. The most important parameter that is of great significance in a sensor network is its lifetime. In this research work, an optimized technique for chain head selection in Pegasis protocol has been proposed on the basis of genetic algorithm, gravitational search algorithm and fuzzy logic. Experimental results indicate that the proposed approach results in significant reduced energy consumption thereby enhancing the network lifetime.

wireless sensor network, Pegasis, fuzzy, genetic algorithm, gravitational search algorithm

[1] Manpreet Bath, Jyoti Saxena, Ravneet Kaur, “Energy Efficient Data Gathering in Wireless Sensor Network using Genetic Algorithm”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 3, Issue. 7, pp. 4830 – 4833, 2015.
[2] Ishu Sharma, Rajvir Singh, Meenu Khurana, “Performance Evaluation of PEGASIS Protocol for WSN using NS2”, In the IEEE, International Conference on Advances in Computer Engineering and Applications, pp. 926-929, 2015.
[3] Harsh Darji, Hitesh B. Shah, “Genetic Algorithm for Energy Harvesting Wireless Sensor Networks”, In the IEEE, International Conference on Recent Trends In Electronics Information Communication Technology, pp. 1398-1402, 2016.
[4] Priyank Garg, Reena Rani, Gurpreet Singh, “Achieving Energy Efficiency in WSN using GSA”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 4, pp. 168-172, 2014.
[5] Rohit D. Gawade, S. L. Nalbalwar, “A Centralized Energy Efficient Distance Based Routing Protocol for Wireless Sensor Networks”, Journal of Sensors, pp. 1-8, 2016.
[6] Akila, Uma Maheswari, “A Survey on Recent Techniques for Energy Efficient. Routing in WSN”, International Journal of Sensors and Sensor Networks, pp. 8-15, 2018.
[7] J. Rejina Parvin, C.Vasanthanayaki, “Mobile Sink Nodes for Energy Efficient Wireless Sensor Networks using Gravitational Search Algorithm”, Australian Journal of Basic and Applied Sciences, pp. 171-181, 2015.
[8] Karthikeyan. A, Jagadeep. V, Rakesh. A, “Energy Efficient Multihop Selection with PEGASIS Routing Protocol for Wireless Sensor Networks”, In the IEEE, International Conference on Computational Intelligence and Computing Research, 2014.
[9] Feng Sen, Qi Bing, Tang Liangrui, “An Improved Energy-Efficient PEGASIS-Based Protocol in Wireless Sensor Networks”, In the IEEE, International Conference on Fuzzy Systems and Knowledge Discovery, pp. 2230-2233, , 2011.
[10] Rina Mahakud, Satyanarayan Rath, Minu Samantaray, Baby Sradha Sinha, Priyanka Priya, Ananya Nayak, Aarti Kumari, “Energy Management in wireless sensor network using PEGASIS”, In the International Conference on Intelligent Computing, Communication & Convergence, pp. 207 – 212, 2016.
[11] Kaushik Gotefode, Kishor Kolhe, “Energy Efficiency in Wireless Sensor Network using Fuzzy rule and Tree Based Routing Protocol”, In the IEEE, International Conference on Energy Systems and Applications, pp. 712-717, 2015.

Suraj Srivastava, Dinesh Grover, "An Optimized Technique for Chain Head Selection in Pegasis Protocol for WSN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.109-112, 2018.

The global wind energy capacity has increased rapidly and became the fastest developing renewable energy technology. But unbalances in wind energy are highly impacting the energy conversion and this problem can be overcome by using variable speed wind turbines. Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECS) are gaining tremendous attention nowadays. In this paper the mathematical modeling of wind turbine is simulated in MATLAB and the results are analyzed for both fixed and variable wind speed. Also dynamic modeling of DFIG has been simulated using MATLAB/SIMULINK and the dynamic behavior of DFIG driven by wind turbine is simulated for variable wind speed.

Wind Turbine, Doubly-fed induction generator, Wind Energy Conversion System

[1] Niassati, N., et al. "A new maximum power point tracking technique for wind power conversion systems." Power Electronics and Motion Control Conference (EPE/PEMC), 2012 15th International. IEEE, 2012.
[2] M. Liserre, R. Cardenas, M. Molinas, and J. Rodriguez, “Overview of multi-MW wind turbines and wind parks,” IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1081–1095, Apr. 2011.
[3] S. Muller, M. Deicke, and R. W. De Doncker, “Doubly fed induction generator systems for wind turbines,” IEEE Ind. Appl. Mag., vol. 17, no. 1, pp. 26–33, May–Jun. 2002.
[4] Verdornschot M. Modeling and control of wind turbines using a continuously variable transmission [Master’s thesis]. Eindhoven: Eindhoven University of Technology, Department of Mechanical Engineering; 2009.
[5] A.Tapia, G.Tapia, J.Ostolaza, “Modeling and Control of a Wind Turbine Driven Doubly Fed Induction Generator”, IEEE Trans on Energy Conv.Vol-18,No-2,June-2003.
[6] Ozpineci, Bhurak, and Leon M. Tolbert. "Simulink implementation of induction machine model-a modular approach." Electric Machines and Drives Conference, 2003. IEMDC`03. IEEE International. Vol. 2. Ieee, 2003.
[7] G. Abad, G. Iwanski, J. López, L. Marroyo, and M. A. Rodríguez, Doubly Fed Induction Machine: Modeling and Control for Wind Energy Generation Applications. Hoboken, NJ: Wiley, 2011.
[8] Pati, Swagat, and Swati Samantray. "Decoupled control of active and reactive power in a DFIG based wind energy conversion system with conventional PI controllers." Circuit, Power and Computing Technologies (ICCPCT), 2014 International Conference on. IEEE, 2014.
[9] M.Aktarujjaman, M.E.Haque, K.M.Muttaqi, M.Negnevitsky, and G.Ledwich, sch.of.Eng., Univ.of Tasmania, Hobart, TAS, “Control Dynamics of a Doubly Fed Induction Generator Under Sub and Super-Synchronous Modes of Operation” IEEE Conference Proceedings, 20-24 July 2008, pages 1-9.
[10] C. Yik Tang, Y. Guo, and J. N. Jiang, “Nonlinear dual mode control of variable–speed wind turbines with doubly fed induction generators”,IEEE Trans on Control Systems Technology, in press.
[11] D.Chwa , Kyo-Beum “ Variable Structure Control of the Active and Reactive Powers for a DFIG in Wind Turbines ”, IEEE Trans on IndustrialApplications.Vol-46,No-6,Nov-Dec-2010.
[12] An Improved Control Strategy of Limiting the DC-Link Voltage Fluctuation for a Doubly Fed Induction Wind Generator” by Jun Yao, Hui Li, Yong Liao, and Zhe Chen, Chongqing University, Chongqing, China. IEEE transactions on power electronics, vol. 23, no. 3, may 2008.
[13] W. Qiao, W. Zhou, and R. G. Harley “Wind speed estimation based sensorless output maximization control for a wind turbine driving a DFIG” IEEE Trans on Power Electronics, vol. 23, no. 3, pp May 2008.
[14] Raza Kazmi SM, Goto H, Hai-Jiao G, Ichinokura O. Review and critical analysis of the research papers published till date on maximum power point tracking in wind energy conversion system. In: 2010 IEEE Energy Conversion Congress and Exposition (ECCE). 2010. p. 4075–82.
[15] Hui J, Bakhshai A. A new adaptive control algorithm for maximum power point tracking for wind energy conversion systems. In: IEEE Power Electronics Spe-cialists Conference PESC. 2008. p. 4003–7.
[16] W. Qiao, W. Zhou, J. M. Aller, and R. G. Harley, “Wind Speed Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a DFIG,” IEEE Trans. Power Electron., vol. 23, no. 3, pp. 1156–1169, May 2008.
[17] Abdullah, M. A., et al. "A review of maximum power point tracking algorithms for wind energy systems." Renewable and Sustainable Energy Reviews 16.5 (2012): 3220-3227.
[18] Kazmi SMR, Goto H, Hai-Jiao G, Ichinokura O. A novel algorithm for fast and efficient speed-sensorless maximum power point tracking in wind energy con-version systems. IEEE Transactions on Industrial Electronics 2011;58:29–36
[19] Hansen, Anca D., et al. "Overall control strategy of variable speed doubly-fed induction generator wind turbine." Wind Power Nordic Conference. 2004.
[20] Hansen, Anca D., P. Sørensen, Florin Iov, and Frede Blaabjerg.
"Overall control strategy of variable speed doubly-fed induction
generator wind turbine." In Wind Power Nordic Conference. 2004.

Jignesh B Bhati, Chirag T Patel, "Dynamic behavior of DFIG based wind turbine under fixed and variable wind speed," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.113-117, 2018.

In digital era large volumes of data are generated by enterprises. Mining on this large volume of data provides valuable insights into user behaviors and helps to improve the business. Various Machine learning algorithms are proposed for data mining. Clustering is an important data mining algorithm for grouping the records and analyzing the data. K-means is a most used Clustering algorithm, but the time taken to cluster large volume of records is high. To reduce the clustering time many approaches are proposed in literature. In this work an improved K-means clustering is proposed which is able to reduce the clustering time.

K-means, Clustering, Centroids

[1] Wang Shunye “An Improved K-means Clustering Algorithm Based on Dissimilarity” 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)Dec 20-22, 2013, Shenyang, China IEEE.
[2] Navjot Kaur, Jaspreet Kaur Sahiwal, Navneet Kaur “EFFICIENT KMEANSCLUSTERING ALGORITHM USING RANKING METHOD IN DATA MINING” ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May2012.
[3] Md. Sohrab Mahmud, Md. Mostafizer Rahman, and Md.Nasim Akhtar ―”Improvement of K-means Clustering algorithm with better initial centroids based on weighted average” 2012 7th International Conference on Electrical and Computer Engineering 20-22 December, 2012, Dhaka, Bangladesh, 2012 IEEE.
[4] Juntao Wang & Xiaolong Su “ An improved K-Means clustering algorithm” 2011 IEEE.
[5] Mohamed Abubaker, Wesam Ashour, "Efficient Data Clustering Algorithms: Improvements over K-means", International Journal of Intelligent Systems and Applications, vol. 5, issue 3, pages 37-49, 2013.
[6] Mohammed EI Agha, Wesam M. Ashour, " Efficient and Fast Initializtion Algorithm for K-means Clustering", LJ. Intelligent Systems and Applications, vol. 4, issue 1, pages 21-31, 2012
[7] Stephen J. Redmon, Conor Heneghan, " A method for initializing the K-means clustering algorithm using kd-trees", Journal Pattern Recognition Letters, vol. 28, issue 8, pages 965-973, 2007.
[8] Ling-bo Han, Qiang Wang, Zhengfeng Jiang etc..Improved k-means initial clustering center selection algorithm. Computer Engineering and Applications. 2010, 46(17):150–152.
[9] Wang, H., Qi, J., Zheng, W., & Wang, M. “Balance K-means algorithm. In Computational Intelligence and Software Engineering,” Cise 2009 International Conference on, pp. 1-3, IEEE
[10] Idrizi F., Rustemi, A., & Dalipi F., (2017, June), Anew modified sorting algorithm: A comparison with state of the art. In embedded computing (MECO) .20176th Mediterranean Conference on (pp 1-6)IEEE.
[11] Esteves, R. M., Hacker, T., & Rong, C. “Competitive k-means, a new accurate and distributed k-means algorithm for large datasets” In Cloud Computing Technology and Science (cloudcom), 2013 IEEE 5th International Conference on ,Vol. 1, pp. 17-24.
[12]MerzCand Murphy P, UCI Repository of MachineLearningDatabases,Available:ftp://ftp.ics.uci.edu/pub/machine-learning-databases

P. Jat, K. Jain, "A Revised and efficient K-means Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.118-124, 2018.